{"meta":{"query_hash":"d1408aa5b2d4","filters":{"topic":"Advanced Multi-Objective Optimization Algorithms"},"cohort_total":645,"direct_labels_cover":0,"predictions_cover":645,"exported":645,"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/d1408aa5b2d4","api":"https://metacan.xera.ac/api/v1/cohort?topic=Advanced+Multi-Objective+Optimization+Algorithms"},"results":[{"id":"W122117794","doi":"10.1007/978-1-4020-6264-3_75","title":"Bond Graph Causality Assignment and Evolutionary Multi-Objective Optimization","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Causality (physics); Bond graph; Mathematical optimization; Assignment problem; Computer science; Optimization problem; Task (project management); Nonlinear system; Theoretical computer science; Algorithm; Mathematics; Combinatorics; Engineering","score_opus":0.031192938822297216,"score_gpt":0.27636203719282504,"score_spread":0.24516909837052783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W122117794","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":[1.6751875e-7,0.00054108317,0.8152655,0.00008850005,0.00046298484,0.00070917205,0.00003353205,0.00036661822,0.18253247],"genre_scores_gemma":[0.00007380028,0.00041542575,0.847988,0.00033311226,0.00008945358,0.000026786489,0.000070934635,0.00006666308,0.1509358],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969227,0.00004309315,0.00059769955,0.0013396624,0.00066688523,0.00043000883],"domain_scores_gemma":[0.99769247,0.0001884172,0.00045102809,0.00078815495,0.00060533476,0.00027458215],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034044834,0.0006301655,0.00049632724,0.00055563013,0.00030817295,0.00012437547,0.00048592774,0.0004925859,0.00016556986],"category_scores_gemma":[0.00006031189,0.00063798844,0.00014103757,0.0001905974,0.00027744917,0.0008375537,0.00056403945,0.0004886768,0.00004720356],"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":[0.000047839138,0.00029363917,0.00007315911,0.00006563002,0.0003744208,0.0001843222,0.00058506004,0.39768982,0.00001611126,0.56768185,0.0016391601,0.031348977],"study_design_scores_gemma":[0.0020316676,0.00023709606,0.0006456079,0.00014461204,0.000079771446,0.00017868997,0.000053943597,0.9562143,0.000128846,0.02809054,0.010276539,0.0019184055],"about_ca_topic_score_codex":0.000029208573,"about_ca_topic_score_gemma":0.000024209585,"teacher_disagreement_score":0.5585244,"about_ca_system_score_codex":0.0005688995,"about_ca_system_score_gemma":0.00016743162,"threshold_uncertainty_score":0.99960715},"labels":[],"label_agreement":null},{"id":"W122534238","doi":"10.1007/978-1-4020-3610-1_65","title":"Preservation of Multiple Point Structure when Conditioning by Kriging","year":2005,"lang":"en","type":"book-chapter","venue":"Quantitative geology and geostatistics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Kriging; Categorical variable; Conditioning; Variogram; Range (aeronautics); Mathematics; Statistics; Mathematical optimization; Computer science; Algorithm; Applied mathematics; Engineering","score_opus":0.016053441515165322,"score_gpt":0.2635232030837113,"score_spread":0.24746976156854597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W122534238","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":[0.00004470791,0.00092460663,0.98996407,0.00027865477,0.00020735837,0.00031260363,0.00096872443,0.000057211506,0.0072420416],"genre_scores_gemma":[0.0030472837,0.00027915556,0.96340317,0.0002632739,0.00003873584,0.000008041206,0.0008729893,0.000033212356,0.03205417],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99848115,0.0000675348,0.00047397558,0.00053812,0.00020602201,0.00023322241],"domain_scores_gemma":[0.99742407,0.001070849,0.0006816287,0.0002917888,0.00046018593,0.00007150245],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012451646,0.00031764832,0.0004474415,0.00018760191,0.0001810348,0.000033114524,0.000276779,0.0002775005,0.00018867443],"category_scores_gemma":[0.00043026893,0.00033729558,0.000039930856,0.000049879218,0.0004589684,0.000490775,0.0001894564,0.00037335182,0.000011561581],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020005336,0.000014699072,0.00013167172,0.000042423326,0.00009977994,0.000011207933,0.0012058985,0.0030440476,0.000052746767,0.98634803,0.0017507015,0.0072787786],"study_design_scores_gemma":[0.0011001241,0.0003507815,0.0003689132,0.00011343,0.000055016597,0.00003285718,0.00009848824,0.38237652,0.0001742678,0.591783,0.022952799,0.00059377786],"about_ca_topic_score_codex":0.000031375705,"about_ca_topic_score_gemma":0.00007635646,"teacher_disagreement_score":0.39456502,"about_ca_system_score_codex":0.000038251932,"about_ca_system_score_gemma":0.000074935604,"threshold_uncertainty_score":0.9999079},"labels":[],"label_agreement":null},{"id":"W13012010","doi":"10.1007/978-3-540-85646-7_26","title":"Multi-scenario Multi-objective Optimization with Applications in Engineering Design","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in economics and mathematical systems","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multi-objective optimization; Mathematical optimization; Pareto principle; Decision maker; Engineering optimization; Context (archaeology); Preference; Computer science; Class (philosophy); Optimization problem; Operations research; Management science; Engineering; Mathematics; Artificial intelligence","score_opus":0.015735269306477978,"score_gpt":0.2208013627078256,"score_spread":0.20506609340134763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W13012010","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":[0.000002387388,0.0003917391,0.9966436,0.00004374486,0.00006889427,0.0018182113,0.000008692853,0.00008825867,0.0009344611],"genre_scores_gemma":[0.0024211535,0.00035098512,0.9961178,0.000037577433,0.00004805089,0.00019857106,0.000015672607,0.00006966415,0.0007405302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823004,0.000027378475,0.0006054981,0.000743229,0.00009306837,0.00030079667],"domain_scores_gemma":[0.9985786,0.00046881055,0.00029968776,0.00045556147,0.00008684996,0.000110502566],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022860049,0.00046499117,0.00069405563,0.0003593011,0.000060338174,0.00018095224,0.00033205055,0.0003742055,0.0000032827859],"category_scores_gemma":[0.00007595307,0.00041825627,0.00005008228,0.00011235273,0.00005287956,0.00025780164,0.00009110275,0.0004050582,0.0000061517067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004679981,0.00005849705,0.000005194034,0.00007032257,0.000022509674,0.000005693342,0.000339122,0.95890963,0.0000025449149,0.036908958,1.3832684e-7,0.0036727376],"study_design_scores_gemma":[0.0007447327,0.00005569675,0.000012303382,0.00031486762,0.0000119447695,0.000038505183,0.000006120414,0.99297005,0.000019134653,0.005189705,0.00016774221,0.00046918722],"about_ca_topic_score_codex":0.000011034355,"about_ca_topic_score_gemma":0.000040487805,"teacher_disagreement_score":0.034060456,"about_ca_system_score_codex":0.00040083646,"about_ca_system_score_gemma":0.00008175323,"threshold_uncertainty_score":0.9998269},"labels":[],"label_agreement":null},{"id":"W140476297","doi":"10.1023/a:1020836221527","title":"Radial Solutions and Orthogonal Trajectories in Multiobjective Global Optimization","year":2002,"lang":"en","type":"article","venue":"Journal of Optimization Theory and Applications","topic":"Advanced Multi-Objective Optimization Algorithms","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":"Université du Québec à Montréal","funders":"","keywords":"Mathematics; Karush–Kuhn–Tucker conditions; Vector optimization; Theory of computation; Nonlinear programming; Mathematical optimization; Nonlinear system; Lagrange multiplier; Applied mathematics; Pareto principle; Set (abstract data type); Optimization problem; Multi-objective optimization; Computer science; Algorithm","score_opus":0.012555732355580673,"score_gpt":0.25020690630586456,"score_spread":0.2376511739502839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W140476297","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":[0.00026893825,0.0008749529,0.9975004,0.00034088464,0.00009679766,0.00032189634,0.000012771149,0.000040027346,0.0005432857],"genre_scores_gemma":[0.1731652,0.0014956326,0.82488066,0.00014902573,0.00014592832,0.000066524815,0.000007794902,0.0000148472645,0.000074413125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986466,0.00022372084,0.00048279518,0.00026826398,0.0001873077,0.0001913248],"domain_scores_gemma":[0.99867666,0.00026537164,0.00038056332,0.0001724323,0.0003729024,0.00013208199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052517356,0.00015766206,0.00021520165,0.00022956046,0.00029751885,0.000120892415,0.00022179184,0.00008678419,0.000028906712],"category_scores_gemma":[0.00020575627,0.00015581131,0.000046521593,0.00087528856,0.00016919976,0.0012690545,0.00007664314,0.00016845742,0.000001129253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019484292,0.00012926431,0.00027773413,0.000004154974,0.000016425603,0.0000021397284,0.00042506398,0.85388625,0.000007566793,0.1331311,0.000011235655,0.01208956],"study_design_scores_gemma":[0.0011834654,0.00007370248,0.0010208753,0.000018636465,0.000020912543,0.00019565171,0.00024233069,0.9866563,0.000019806977,0.010216858,0.00016913585,0.00018231683],"about_ca_topic_score_codex":0.0000015453708,"about_ca_topic_score_gemma":0.0000030481544,"teacher_disagreement_score":0.17289625,"about_ca_system_score_codex":0.000113503906,"about_ca_system_score_gemma":0.0000505883,"threshold_uncertainty_score":0.6353801},"labels":[],"label_agreement":null},{"id":"W1460549585","doi":"10.1007/978-3-642-29946-9_13","title":"Regularized Least Squares Temporal Difference Learning with Nested ℓ2 and ℓ1 Penalization","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Overfitting; Computer science; Regularization (linguistics); Reinforcement learning; Solver; Mathematical optimization; Algorithm; Artificial intelligence; Applied mathematics; Mathematics; Artificial neural network","score_opus":0.012328453084216541,"score_gpt":0.2322221378745569,"score_spread":0.21989368479034035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1460549585","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":[0.00019059668,0.0005136618,0.9974788,0.00021487838,0.00042400087,0.00051881804,0.0000022210745,0.0002674751,0.00038957116],"genre_scores_gemma":[0.15425666,0.00008283046,0.84429073,0.00029013449,0.00022353521,0.000016261012,0.000019774747,0.00005862165,0.00076147745],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963462,0.00009152969,0.00044174428,0.0015099591,0.0009508486,0.0006597434],"domain_scores_gemma":[0.9976014,0.0003349687,0.00049116655,0.0008353918,0.00048776582,0.0002492943],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00050031097,0.000621426,0.00056856143,0.00071419514,0.00045701046,0.00060596113,0.0013538393,0.00027257903,0.000016475007],"category_scores_gemma":[0.00013568104,0.000523166,0.000055921642,0.0007919132,0.00089624326,0.0011902081,0.0009144356,0.0008218595,0.000010076044],"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":[0.00003655717,0.00005804892,0.0033636983,0.00007093439,0.000023173689,0.000069515576,0.0020821893,0.2196853,0.00039609513,0.012285302,0.0000014373594,0.7619277],"study_design_scores_gemma":[0.0007643224,0.00025918675,0.0026637998,0.0004287779,0.00001457355,0.0001969676,0.0000010656463,0.9848639,0.0004757241,0.00915842,0.00028938134,0.0008838756],"about_ca_topic_score_codex":0.000024909277,"about_ca_topic_score_gemma":0.000051189658,"teacher_disagreement_score":0.7651786,"about_ca_system_score_codex":0.00023484572,"about_ca_system_score_gemma":0.0003438608,"threshold_uncertainty_score":0.999722},"labels":[],"label_agreement":null},{"id":"W1480330138","doi":"10.1613/jair.4806","title":"Bayesian Optimization in a Billion Dimensions via Random Embeddings","year":2016,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":374,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Deutsche Forschungsgemeinschaft","keywords":"Bayesian optimization; Embedding; Computer science; Categorical variable; Curse of dimensionality; Solver; Bayesian probability; Artificial intelligence; Optimization problem; Mathematical optimization; Integer programming; Machine learning; Theoretical computer science; Algorithm; Mathematics","score_opus":0.07307959598059346,"score_gpt":0.39083408000608344,"score_spread":0.31775448402549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1480330138","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":[0.0014864787,0.000084485626,0.99537027,0.0023038054,0.00035058512,0.0002565175,8.1002696e-7,0.000023443827,0.00012362475],"genre_scores_gemma":[0.60375285,0.00047047512,0.39548966,0.00003349079,0.00014611905,0.0000068766744,2.5729446e-7,0.000017130587,0.000083121966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99673647,0.00051243044,0.0009018873,0.00033075328,0.0010106164,0.0005078327],"domain_scores_gemma":[0.9962897,0.0011281166,0.00030293717,0.000339451,0.001717783,0.00022207075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003428349,0.00013802238,0.00027703095,0.0014079458,0.00019191402,0.00014721643,0.0008460852,0.00009853821,0.00010230252],"category_scores_gemma":[0.002111295,0.00009285769,0.00010232518,0.0021935524,0.00020553659,0.0015156212,0.00021755288,0.0004424105,0.00005568787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025568804,0.0003254128,0.000103192215,0.0000057330944,0.000016917838,0.00016150568,0.0010491416,0.5424291,0.018570555,0.0066608866,0.000047075486,0.4303748],"study_design_scores_gemma":[0.0002698063,0.00028516244,0.000023442999,0.00016577516,0.0000021959202,0.00008555794,0.00022155473,0.9199374,0.049060814,0.029703872,0.00010139456,0.00014305486],"about_ca_topic_score_codex":0.00001983055,"about_ca_topic_score_gemma":0.000019145782,"teacher_disagreement_score":0.6022664,"about_ca_system_score_codex":0.0003634111,"about_ca_system_score_gemma":0.00025522025,"threshold_uncertainty_score":0.37866268},"labels":[],"label_agreement":null},{"id":"W1528483814","doi":"10.1029/2011wr011527","title":"Review of surrogate modeling in water resources","year":2012,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":961,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Surrogate model; Computer science; Metamodeling; Variety (cybernetics); Fidelity; Field (mathematics); Management science; Data science; Systems engineering; Operations research; Risk analysis (engineering); Machine learning; Artificial intelligence; Engineering; Software engineering","score_opus":0.09503813210406548,"score_gpt":0.3663211718458375,"score_spread":0.27128303974177204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1528483814","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":[0.69188154,0.044716213,0.24682075,0.0038278857,0.0004048427,0.0022322172,0.000008038881,0.00033541452,0.00977312],"genre_scores_gemma":[0.9498157,0.0050983187,0.042549934,0.00041449582,0.00021242,0.00015466796,0.000017986395,0.00006826893,0.001668245],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99567735,0.0008730956,0.00059087604,0.00050979026,0.0010283797,0.0013204877],"domain_scores_gemma":[0.9982265,0.00013180831,0.00004933559,0.0008730584,0.00049874774,0.00022052869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004666636,0.00020250173,0.00037879864,0.0006527317,0.00017796765,0.00009023738,0.0013362587,0.00009231343,0.00011681508],"category_scores_gemma":[0.00018871507,0.00012597327,0.000088887515,0.0007581406,0.00016749589,0.00088744715,0.0013137977,0.0005266656,0.00024262359],"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":[0.00050796085,0.0035827262,0.017486777,0.02117663,0.000333088,0.00025872665,0.46035337,0.25604153,0.07907656,0.001953007,0.0015845143,0.1576451],"study_design_scores_gemma":[0.0016937325,0.00021251495,0.00028350877,0.005230625,0.000010714004,0.000049686827,0.00069597113,0.69252956,0.11830175,0.0013708661,0.17870359,0.0009174612],"about_ca_topic_score_codex":0.00019006064,"about_ca_topic_score_gemma":0.000008893598,"teacher_disagreement_score":0.4596574,"about_ca_system_score_codex":0.00012469523,"about_ca_system_score_gemma":0.000014227884,"threshold_uncertainty_score":0.5137041},"labels":[],"label_agreement":null},{"id":"W1529269817","doi":"","title":"Performance of Simulated Annealing, Tabu Search, and Evolutionary Algorithms forMulti-objective Network Partitioning","year":2006,"lang":"en","type":"article","venue":"Algorithmic operations research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tabu search; Simulated annealing; Mathematical optimization; Heuristics; Computer science; Metaheuristic; Evolutionary algorithm; Set (abstract data type); Pareto principle; Multi-objective optimization; Algorithm; Mathematics","score_opus":0.028422498015934283,"score_gpt":0.32132988627485826,"score_spread":0.292907388258924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1529269817","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":[0.16227221,0.00075596623,0.8340251,0.00029651815,0.0002317357,0.001201456,0.000045054625,0.00022285357,0.00094910525],"genre_scores_gemma":[0.59275687,0.00021281022,0.40591973,0.000021943559,0.00025178798,0.00008376444,0.00007307616,0.00002932503,0.00065068936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961111,0.00045054665,0.00065799896,0.0008248613,0.0010196711,0.0009358023],"domain_scores_gemma":[0.9963801,0.0004053619,0.00006429874,0.0006630092,0.0023064488,0.00018076645],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0013718582,0.0002743756,0.000341374,0.0005619371,0.0015253734,0.00024375714,0.0006529652,0.00016041138,0.00003417211],"category_scores_gemma":[0.00018316612,0.00028013124,0.00006951578,0.0024899817,0.0005343833,0.0019189023,0.00057725725,0.0006232082,0.000036641188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015377576,0.00014062456,0.0016321136,0.000019842797,0.000031353506,0.0000114914865,0.0004393729,0.9798793,0.0004616872,0.0045068907,0.000118095166,0.012743852],"study_design_scores_gemma":[0.0009150171,0.00027625856,0.013332145,0.0000678795,0.0000060854804,0.000050611874,0.000143751,0.9823111,0.0018148005,0.00062083924,0.00017444548,0.00028700786],"about_ca_topic_score_codex":0.0012989383,"about_ca_topic_score_gemma":0.000102382975,"teacher_disagreement_score":0.43048468,"about_ca_system_score_codex":0.00028692058,"about_ca_system_score_gemma":0.00040800715,"threshold_uncertainty_score":0.9999651},"labels":[],"label_agreement":null},{"id":"W1531908466","doi":"10.1007/978-3-540-74581-5_2","title":"A Multi-Objective Genetic Algorithm Based on Density","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Western University","funders":"","keywords":"Pruning; Selection (genetic algorithm); Genetic algorithm; Fitness function; Mathematical optimization; Computer science; Set (abstract data type); Population; Construct (python library); Computation; Evolutionary algorithm; Algorithm; Mathematics; Artificial intelligence; Biology","score_opus":0.0200675455014978,"score_gpt":0.2705543683378924,"score_spread":0.2504868228363946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1531908466","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":[0.000004137276,0.00012934687,0.99487907,0.00014624081,0.0020566287,0.00089214277,0.000012911164,0.00037180344,0.0015077051],"genre_scores_gemma":[0.0026547413,0.000018204351,0.9936124,0.002934145,0.00035958132,0.000016378826,0.000007079833,0.00007640284,0.00032104107],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936598,0.00007620558,0.0006904426,0.0028629259,0.0016394962,0.0010711582],"domain_scores_gemma":[0.99535155,0.0008744366,0.00047898167,0.0020799027,0.0008516955,0.00036340707],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008844045,0.0009481284,0.00075212657,0.0019317211,0.00044410856,0.00040961336,0.0031159197,0.00052838144,0.00002481303],"category_scores_gemma":[0.0002470453,0.0009315595,0.0002245358,0.0013502006,0.00096439564,0.000546521,0.0010526795,0.001377047,0.00011470494],"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":[0.0000070138203,0.000074450785,0.000017676888,0.000008173474,0.000007628962,0.00025202148,0.00020400216,0.41746572,0.000019230389,0.00035846137,0.0000021497108,0.58158344],"study_design_scores_gemma":[0.00084371254,0.0002905533,0.0007507253,0.0002260876,0.0000085656175,0.00007273276,1.8653515e-7,0.986491,0.001513435,0.008608435,0.00020732787,0.0009872094],"about_ca_topic_score_codex":0.00002950387,"about_ca_topic_score_gemma":0.00006949688,"teacher_disagreement_score":0.58059627,"about_ca_system_score_codex":0.0011858611,"about_ca_system_score_gemma":0.00087578676,"threshold_uncertainty_score":0.9993135},"labels":[],"label_agreement":null},{"id":"W1542369266","doi":"10.1007/11740698_13","title":"The Importance of Scalability When Comparing Dynamic Weighted Aggregation and Pareto Front Techniques","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer 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":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scalability; Computer science; Computation; Multi-objective optimization; Genetic algorithm; Population; Pareto principle; Mathematical optimization; Algorithm; Machine learning; Mathematics; Database","score_opus":0.008861077321542761,"score_gpt":0.24371619178205867,"score_spread":0.2348551144605159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1542369266","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":[0.0002331018,0.0009033398,0.99648005,0.00033152185,0.00037719164,0.00063130533,0.0000045361653,0.00016862953,0.00087029976],"genre_scores_gemma":[0.07813392,0.00009580242,0.9214009,0.00013676904,0.000065099135,0.00001830607,0.0000069380058,0.00002475433,0.00011752006],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99690217,0.000052371703,0.0006977255,0.001213065,0.0007137004,0.00042095006],"domain_scores_gemma":[0.99699616,0.0005583621,0.0006386303,0.0012285025,0.0004910519,0.00008730552],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00077683775,0.00041476433,0.00050061266,0.00033395924,0.0003613526,0.00028224287,0.0017614828,0.00020599845,0.000002670772],"category_scores_gemma":[0.00011224568,0.00032551645,0.00007515957,0.00032677702,0.0013729221,0.0006292242,0.0009981927,0.0005048053,0.0000016563018],"study_design_candidate":"design_other","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":[0.000016127857,0.00003699183,0.0023173157,0.000047819587,0.000013577775,0.00001257238,0.00056426966,0.018945722,0.0000746318,0.0061767353,0.00000988671,0.97178435],"study_design_scores_gemma":[0.00016842577,0.000071582,0.0011896141,0.00021156692,0.0000049119108,0.000017330296,2.2847412e-7,0.8351943,0.0018451933,0.16078742,0.00017743399,0.00033203253],"about_ca_topic_score_codex":0.000035401292,"about_ca_topic_score_gemma":0.0005377485,"teacher_disagreement_score":0.9714523,"about_ca_system_score_codex":0.00044177132,"about_ca_system_score_gemma":0.00023171397,"threshold_uncertainty_score":0.9999197},"labels":[],"label_agreement":null},{"id":"W1543201103","doi":"10.1007/978-3-642-13800-3_30","title":"Time-Bounded Sequential Parameter Optimization","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":74,"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":"Computer science; Mathematical optimization; Bounded function; Overhead (engineering); Focus (optics); Optimization problem; Discrete optimization; Algorithm; Mathematics","score_opus":0.013939214822209132,"score_gpt":0.2546079592798334,"score_spread":0.24066874445762423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543201103","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":[0.000008190136,0.00007729818,0.99362105,0.00033434422,0.0026595546,0.0006215557,0.000008235437,0.00038052362,0.0022892328],"genre_scores_gemma":[0.00101869,0.000028020106,0.9962631,0.0009992459,0.0004557205,0.000015937356,0.000023283963,0.00006780728,0.001128192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99536204,0.00005250631,0.00063549884,0.0020532354,0.0011332674,0.00076345995],"domain_scores_gemma":[0.99643666,0.0004889663,0.00047249236,0.0017408959,0.0006142127,0.00024678893],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006264314,0.0006994267,0.0005966485,0.0010367878,0.0004081708,0.00082062837,0.0031681347,0.00061379996,0.0001932972],"category_scores_gemma":[0.00026938983,0.0006903687,0.00016792583,0.00082235143,0.0012420279,0.001415981,0.0014038238,0.0013305624,0.00016934144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005039136,0.000029192943,0.0000018451688,0.000009294699,0.00001104907,0.000048671656,0.0002956554,0.8379529,0.00026862216,0.0022356058,0.0000039749025,0.15913817],"study_design_scores_gemma":[0.00038344774,0.000092177725,0.000004121856,0.00009237169,0.000008557586,0.00008890342,5.1783402e-8,0.94748294,0.0024673676,0.047983892,0.00065109733,0.00074504624],"about_ca_topic_score_codex":0.0000068956083,"about_ca_topic_score_gemma":0.000023179931,"teacher_disagreement_score":0.15839311,"about_ca_system_score_codex":0.00044330617,"about_ca_system_score_gemma":0.00071245106,"threshold_uncertainty_score":0.99955475},"labels":[],"label_agreement":null},{"id":"W1550542145","doi":"10.1023/a:1011860702585","title":"Mixed Variable Optimization of the Number and Composition of Heat Intercepts in a Thermal Insulation System","year":2001,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Categorical variable; Mathematical optimization; Mathematics; Discrete optimization; Variable (mathematics); Continuous optimization; Parametric statistics; Continuous variable; Reduction (mathematics); Function (biology); Optimization problem; Computer science; Statistics; Mathematical analysis","score_opus":0.006937388061216975,"score_gpt":0.19937026656132575,"score_spread":0.1924328785001088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1550542145","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":[0.039142862,0.00002463004,0.96022475,0.000029020212,0.00012128725,0.00017030313,0.0000015350586,0.000044956414,0.00024063536],"genre_scores_gemma":[0.59775877,0.00002329103,0.4021836,0.0000056861054,0.000005857291,0.0000056500385,0.0000040488694,0.0000068988656,0.0000062105155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933946,0.00004083098,0.00025843424,0.00015983147,0.000106758314,0.00009468726],"domain_scores_gemma":[0.99960226,0.00003838488,0.00008424169,0.00014494869,0.0001019998,0.000028187296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011592597,0.00009556597,0.00013972698,0.00010514844,0.000039872637,0.00002600514,0.00009962347,0.000053888532,0.0000061886863],"category_scores_gemma":[0.000024244828,0.0000862823,0.000015477266,0.0005497303,0.000022810964,0.0004466279,0.00007999147,0.000057372574,1.4464881e-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":[0.0000054478724,0.000018964029,0.0015643958,0.000039932256,0.0000048754277,3.1612808e-7,0.00028800822,0.99319017,0.002573739,0.0020215525,2.2121786e-7,0.00029234894],"study_design_scores_gemma":[0.0004777192,0.000009846744,0.008440634,0.0001305982,0.000004354543,0.000018769582,0.000042549407,0.98999846,0.00079224556,0.0000036142546,0.0000023021346,0.00007890709],"about_ca_topic_score_codex":0.000017549537,"about_ca_topic_score_gemma":4.0346208e-7,"teacher_disagreement_score":0.5586159,"about_ca_system_score_codex":0.000048613183,"about_ca_system_score_gemma":0.000011654663,"threshold_uncertainty_score":0.35184902},"labels":[],"label_agreement":null},{"id":"W1552846432","doi":"10.1007/3-540-36970-8_24","title":"A New MOEA for Multi-objective TSP and Its Convergence Property Analysis","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"York University","funders":"","keywords":"Crossover; Mathematical optimization; Selection (genetic algorithm); Convergence (economics); Evolutionary algorithm; Computer science; Property (philosophy); Operator (biology); Markov chain; Multi-objective optimization; Population; Tournament selection; Mutation; Mathematics; Artificial intelligence; Machine learning","score_opus":0.028218236087315086,"score_gpt":0.28008080356214216,"score_spread":0.25186256747482705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1552846432","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":[0.000004960046,0.0007665914,0.99590945,0.00035416093,0.0010428505,0.0013752492,0.000019744586,0.00016076013,0.00036623064],"genre_scores_gemma":[0.0027766214,0.00012282758,0.9925868,0.0011238023,0.00014290787,0.00004532576,0.0000074862005,0.00004780083,0.003146447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995243,0.000054704156,0.0006013035,0.002581387,0.00074382214,0.0007757565],"domain_scores_gemma":[0.99681276,0.000419117,0.0004445322,0.0010654799,0.00088271516,0.00037541654],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006131686,0.0007357686,0.00088432187,0.0013868194,0.0003862701,0.00041831724,0.0019904473,0.00034655523,0.00002689109],"category_scores_gemma":[0.00036075033,0.00058779214,0.00023708274,0.0019761857,0.0003880422,0.001058555,0.0008348888,0.00057276076,0.0000137607185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026089623,0.000082973245,0.00012704892,0.000072017385,0.00028149495,0.000051200357,0.002400223,0.5019221,0.00017392196,0.013192414,0.00003790773,0.48163262],"study_design_scores_gemma":[0.000746294,0.00016294018,0.000102163285,0.00008391362,0.00007490982,0.00003305606,4.645044e-7,0.98427397,0.0014606111,0.011412547,0.00082249416,0.00082662835],"about_ca_topic_score_codex":0.000030927567,"about_ca_topic_score_gemma":0.00012238577,"teacher_disagreement_score":0.4823519,"about_ca_system_score_codex":0.0004183873,"about_ca_system_score_gemma":0.0007308513,"threshold_uncertainty_score":0.99965733},"labels":[],"label_agreement":null},{"id":"W1566833990","doi":"10.1007/978-94-017-0161-7_42","title":"A Model-Based Framework for Robust Design","year":2003,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","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":"McGill University","funders":"","keywords":"Design matrix; Inverse; Logarithm; Robustness (evolution); Mathematics; Matrix (chemical analysis); Bandwidth (computing); Set (abstract data type); Computer science; Mathematical optimization; Algorithm; Applied mathematics; Statistics; Mathematical analysis; Regression analysis; Telecommunications; Geometry","score_opus":0.07856166619759361,"score_gpt":0.2824109632491055,"score_spread":0.20384929705151186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1566833990","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":[6.5559574e-10,0.0000903245,0.8991708,0.0002861638,0.00033282064,0.0010585745,0.00001435734,0.00035417717,0.09869277],"genre_scores_gemma":[0.0000011215973,0.00001606487,0.76251394,0.0020932204,0.000048983115,0.00009746858,0.000009460958,0.00007890372,0.23514082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807125,0.000021338976,0.00033840036,0.0009047823,0.0003106075,0.00035361372],"domain_scores_gemma":[0.9976973,0.00047708032,0.00025296895,0.0009706154,0.00045086694,0.00015115357],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019379325,0.00046075173,0.00039973593,0.00021964584,0.00015450547,0.00012523486,0.00082273944,0.0004997038,0.000108310756],"category_scores_gemma":[0.0001326439,0.000453186,0.00020806861,0.00008218302,0.0000682227,0.00025472912,0.00008860052,0.00034294816,0.000058655212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044886833,0.0000110323645,1.9384641e-8,0.00000696121,0.000012118988,0.000002081168,0.0000112359985,0.5349264,4.317286e-7,0.46010098,0.0009057364,0.004018524],"study_design_scores_gemma":[0.00027371533,0.0000543326,2.038144e-8,0.000047594873,0.00001125254,0.0000020652074,5.3727246e-7,0.6876477,0.00006876959,0.30529544,0.006212061,0.0003865174],"about_ca_topic_score_codex":3.6987976e-7,"about_ca_topic_score_gemma":6.7133107e-7,"teacher_disagreement_score":0.15480554,"about_ca_system_score_codex":0.00018867922,"about_ca_system_score_gemma":0.00028906093,"threshold_uncertainty_score":0.999792},"labels":[],"label_agreement":null},{"id":"W1574085630","doi":"10.1007/978-3-642-15461-4_43","title":"Inverse Modeling in Geoenvironmental Engineering Using a Novel Particle Swarm Optimization Algorithm","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 Saskatchewan","funders":"","keywords":"Particle swarm optimization; Computer science; Algorithm; Mathematical optimization; Inverse; Convergence (economics); Multi-swarm optimization; Inverse problem; Premature convergence; Population; Mathematics","score_opus":0.021452649873980066,"score_gpt":0.23416118009005418,"score_spread":0.2127085302160741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1574085630","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":[0.0003182195,0.000058969687,0.9979586,0.00006276483,0.001005563,0.0004180717,0.000005499087,0.00013084275,0.00004144433],"genre_scores_gemma":[0.010805337,0.000021826438,0.98868513,0.00023511573,0.00016593699,0.000009230088,0.0000056271883,0.000051202147,0.000020584475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99672973,0.000014751486,0.0005449598,0.0013640444,0.00069035724,0.0006561743],"domain_scores_gemma":[0.9985816,0.00012234999,0.00020937595,0.00079546403,0.000117392716,0.00017381445],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046364908,0.00049270474,0.0004018642,0.0007155733,0.00017783885,0.0002770487,0.0013443122,0.00031727672,0.000014295027],"category_scores_gemma":[0.00008005714,0.0005467464,0.000077687255,0.0007122105,0.0002567959,0.0013106251,0.0010357231,0.00096775376,0.000007678738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014964489,0.000037243542,0.000005873403,0.0000062051577,0.0000034990526,0.000024352727,0.00038307568,0.9520991,0.0008568261,0.00025696767,2.2586287e-8,0.04632534],"study_design_scores_gemma":[0.0005179043,0.000032614673,0.00000468733,0.00013919115,0.000004460619,0.000061292165,4.405119e-7,0.99526733,0.0019189547,0.0014707342,0.000012093887,0.0005702658],"about_ca_topic_score_codex":0.000038676782,"about_ca_topic_score_gemma":0.00003229975,"teacher_disagreement_score":0.045755073,"about_ca_system_score_codex":0.0007459387,"about_ca_system_score_gemma":0.00023151317,"threshold_uncertainty_score":0.9996984},"labels":[],"label_agreement":null},{"id":"W1578253793","doi":"10.1007/978-3-540-68123-6_28","title":"Hybrid Unsupervised/Supervised Virtual Reality Spaces for Visualizing Gastric and Liver Cancer Databases: An Evolutionary Computation Approach","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","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":"National Research Council Canada","funders":"","keywords":"Computer science; Classifier (UML); Artificial intelligence; Data mining; Machine learning; Cluster analysis; Pattern recognition (psychology)","score_opus":0.047448569717176245,"score_gpt":0.30546356919975776,"score_spread":0.2580149994825815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1578253793","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":[0.00015650693,0.0014382327,0.99578506,0.00014297102,0.00071309105,0.0012176733,0.00015917634,0.00025717574,0.0001301197],"genre_scores_gemma":[0.019242015,0.00077152,0.97851825,0.0005866781,0.00046004253,0.00007866433,0.00021544003,0.000064468935,0.000062919935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950303,0.00011640288,0.0006248639,0.0025103437,0.000988546,0.0007295742],"domain_scores_gemma":[0.9969506,0.0006414755,0.0003937105,0.0009573701,0.00073914166,0.0003176937],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00062647014,0.0007156242,0.00067277823,0.00096326094,0.0007476982,0.0003881913,0.001550761,0.00018527472,0.0000065262807],"category_scores_gemma":[0.00013053432,0.0007220074,0.000112701004,0.00067630113,0.0009818742,0.0024844082,0.0010549371,0.0005462717,0.0000029158507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024406989,0.00010169613,0.00004621166,0.00007116547,0.000018769431,0.00003212617,0.0010890717,0.6102181,0.00004878913,0.0024798212,0.000044201486,0.3858256],"study_design_scores_gemma":[0.0007883789,0.0002642701,0.00026006612,0.00017370691,0.000016833676,0.00021828442,0.000002283144,0.9937702,0.00014066798,0.003201485,0.00036895517,0.0007948971],"about_ca_topic_score_codex":0.00014265138,"about_ca_topic_score_gemma":0.00003150982,"teacher_disagreement_score":0.38503072,"about_ca_system_score_codex":0.00057184533,"about_ca_system_score_gemma":0.0007841831,"threshold_uncertainty_score":0.9995231},"labels":[],"label_agreement":null},{"id":"W1603639300","doi":"10.1109/ccece.2001.933604","title":"A robust model parameter extraction technique based on meta-evolutionary programming for high speed/high frequency package interconnects","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Computer science; Coplanar waveguide; Parametric statistics; Electronic engineering; Flip chip; Stripline; Evolutionary algorithm; Terahertz radiation; Convergence (economics); Algorithm; Engineering; Optoelectronics; Mathematics; Materials science; Telecommunications; Artificial intelligence","score_opus":0.07434086611109436,"score_gpt":0.27811175565235396,"score_spread":0.2037708895412596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1603639300","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":[0.00009635219,0.000034533772,0.9954522,0.0008927569,0.0002272907,0.0018013244,0.000019958887,0.0007437592,0.0007318237],"genre_scores_gemma":[0.24641618,0.0000041720546,0.7514954,0.00048300068,0.000041406805,0.0008226373,0.000019181281,0.000034881574,0.0006831209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774915,0.00009685732,0.0004128886,0.00090521254,0.00036064998,0.00047522824],"domain_scores_gemma":[0.9980558,0.0004613445,0.00022139076,0.0007761342,0.00034917318,0.0001361481],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026957484,0.00036671007,0.00036954985,0.00035387697,0.00022930244,0.0001471227,0.0005460278,0.00016866648,0.00017294445],"category_scores_gemma":[0.00030798806,0.00031600965,0.00023749175,0.00055813126,0.0000662327,0.0013949568,0.00008621877,0.000262398,0.000034949844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022047061,0.00072209386,0.0000105660965,0.00002951859,0.00013279011,0.00001815043,0.00009144822,0.94030493,0.002179386,0.03399341,0.0004524194,0.02204321],"study_design_scores_gemma":[0.0006247481,0.000273654,0.00002076808,0.000019627822,0.00006651421,0.000016928148,0.000009817865,0.9777923,0.0110746,0.009642426,0.00006866465,0.00038992183],"about_ca_topic_score_codex":0.000037677244,"about_ca_topic_score_gemma":0.000008554694,"teacher_disagreement_score":0.24631983,"about_ca_system_score_codex":0.00028497764,"about_ca_system_score_gemma":0.000042172916,"threshold_uncertainty_score":0.9999292},"labels":[],"label_agreement":null},{"id":"W1603688956","doi":"10.1109/ijcnn.2005.1556070","title":"Feature subset selection via multi-objective genetic algorithm","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Backpropagation; Genetic algorithm; Feature selection; Feature (linguistics); Selection (genetic algorithm); Population-based incremental learning; Algorithm; Artificial neural network; Artificial intelligence; Set (abstract data type); Pareto principle; Data mining; Pattern recognition (psychology); Machine learning; Mathematical optimization; Mathematics","score_opus":0.022377410316511352,"score_gpt":0.26340761261689805,"score_spread":0.2410302023003867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1603688956","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":[0.0008957065,0.00011361287,0.99051094,0.0024685233,0.0026159564,0.0007208691,0.00002819688,0.0006485508,0.0019976648],"genre_scores_gemma":[0.31097528,0.00029678165,0.6771178,0.0014281989,0.002955565,0.0002661999,0.00009571881,0.00012213073,0.006742265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958885,0.000057113677,0.00063347263,0.0014192238,0.0009626552,0.0010390733],"domain_scores_gemma":[0.9973184,0.00006166723,0.0006246555,0.0002684264,0.0014475697,0.00027932355],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026086185,0.0006878162,0.0004690517,0.0005155475,0.00034995825,0.00068930123,0.0011918804,0.00030995446,0.00012331919],"category_scores_gemma":[0.000058332927,0.00068617647,0.00021048298,0.0006654257,0.00014595939,0.0015887524,0.00019882759,0.0009322346,0.00010493032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015495355,0.0011756053,0.0016847948,0.000023083076,0.00023006127,0.00009883147,0.00022846904,0.7942552,0.004570664,0.0103623485,0.038073957,0.14914203],"study_design_scores_gemma":[0.0011982158,0.00014392396,0.0057501975,0.0000658538,0.000016643498,0.00025631353,0.0000223845,0.9861861,0.0025922547,0.0008898838,0.0021600567,0.0007181667],"about_ca_topic_score_codex":0.00006315288,"about_ca_topic_score_gemma":0.000081932354,"teacher_disagreement_score":0.3133931,"about_ca_system_score_codex":0.000666929,"about_ca_system_score_gemma":0.00010465095,"threshold_uncertainty_score":0.9995589},"labels":[],"label_agreement":null},{"id":"W1692713333","doi":"10.1109/icpr.2002.1044794","title":"Feature selection using multi-objective genetic algorithms for handwritten digit recognition","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":114,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Feature selection; NIST; Artificial intelligence; Genetic algorithm; Novelty; Pattern recognition (psychology); Selection (genetic algorithm); Feature (linguistics); Generalization; Handwriting recognition; Data mining; Machine learning; Artificial neural network; Feature extraction; Speech recognition; Mathematics","score_opus":0.04481181056939632,"score_gpt":0.29718223166823804,"score_spread":0.2523704210988417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1692713333","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":[0.00081146153,0.000066935245,0.99666923,0.000092092305,0.0004960912,0.00096280966,0.000014176073,0.0003598966,0.00052728003],"genre_scores_gemma":[0.0068235192,0.000016695696,0.9910755,0.00023935613,0.00009483338,0.00012440752,0.000014931319,0.00004018466,0.0015705813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982124,0.00010906605,0.00022677875,0.00077102793,0.00022877846,0.0004519511],"domain_scores_gemma":[0.9985496,0.00012722291,0.00016474901,0.00028189764,0.00074393617,0.00013259728],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015629611,0.00027794312,0.00023138797,0.00023183384,0.0003843745,0.00022125739,0.00025989368,0.00016200914,0.000019148878],"category_scores_gemma":[0.00028220867,0.00027516636,0.000116068506,0.0008540729,0.00004476958,0.0010650798,0.00005357144,0.00017335081,0.000022906972],"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":[0.00009544554,0.0013210794,0.0021346838,0.00010150064,0.000393856,0.00002962669,0.0020889316,0.15743157,0.017655576,0.004178569,0.0010267264,0.8135424],"study_design_scores_gemma":[0.0018395762,0.00014211854,0.0006185324,0.000019757677,0.000021805496,0.00013111137,0.0000952897,0.9484023,0.044489115,0.0021609878,0.0016013144,0.00047813164],"about_ca_topic_score_codex":0.000016118105,"about_ca_topic_score_gemma":0.000019429333,"teacher_disagreement_score":0.8130643,"about_ca_system_score_codex":0.00028055615,"about_ca_system_score_gemma":0.00013423392,"threshold_uncertainty_score":0.9999701},"labels":[],"label_agreement":null},{"id":"W1766801768","doi":"10.1016/j.ifacol.2015.08.165","title":"Robust design of experiments using constrained stochastic optimization","year":2015,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Particle swarm optimization; Mathematical optimization; Monte Carlo method; Optimal design; Set (abstract data type); Robust optimization; Fisher information; Design of experiments; Maximization; Stochastic optimization; Computer science; Mathematics; Statistics","score_opus":0.13765580702195318,"score_gpt":0.3134614791210271,"score_spread":0.17580567209907394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1766801768","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":[0.00035958836,0.00011906284,0.99842924,0.00008602435,0.0003327271,0.00039174993,0.000010824253,0.00014595009,0.00012484177],"genre_scores_gemma":[0.012115336,0.0000041485932,0.9875952,0.00009250328,0.000059868828,0.000011294811,0.000015516454,0.000024095572,0.00008203089],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984673,0.00010892376,0.00035002673,0.0004167293,0.0003829577,0.00027404595],"domain_scores_gemma":[0.99862075,0.00009557291,0.00025347163,0.00037558275,0.0004699147,0.00018471465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023691167,0.00021239006,0.0002676032,0.0001617126,0.00008043187,0.00004655241,0.00042351277,0.00008099234,0.000027606584],"category_scores_gemma":[0.0002884398,0.00021326603,0.000046473397,0.00053928565,0.00012307485,0.00064726855,0.00013459602,0.0000963415,0.0000073204715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020194137,0.000112650785,0.0000065420177,0.0000034582358,0.00001921184,0.000006018083,0.0007846472,0.99478865,0.002793611,0.0002226284,9.792246e-7,0.0012413855],"study_design_scores_gemma":[0.0013998292,0.00011999886,0.0000043343584,0.00002954589,0.000011211117,0.000027833632,0.00032071234,0.99587166,0.0019435295,0.00005440992,0.000001598583,0.00021530698],"about_ca_topic_score_codex":0.000015005477,"about_ca_topic_score_gemma":5.594024e-7,"teacher_disagreement_score":0.011755748,"about_ca_system_score_codex":0.00016255342,"about_ca_system_score_gemma":0.00026054576,"threshold_uncertainty_score":0.8696736},"labels":[],"label_agreement":null},{"id":"W177935800","doi":"10.1007/978-3-642-10701-6_4","title":"Knowledge-Based Variable-Fidelity Optimization of Expensive Objective Functions through Space Mapping","year":2010,"lang":"en","type":"book-chapter","venue":"Adaptation, learning, and optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Space mapping; Fidelity; Computer science; Optimization problem; Space (punctuation); Surrogate model; Mathematical optimization; Variable (mathematics); Vector optimization; Algorithm; Mathematics; Multi-swarm optimization","score_opus":0.020379935224439136,"score_gpt":0.25123480710065743,"score_spread":0.23085487187621828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W177935800","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":[0.000001132721,0.0006267851,0.9220762,0.00010258816,0.00068084314,0.0008348861,0.000025226107,0.0003619582,0.07529037],"genre_scores_gemma":[0.000510543,0.00075093034,0.93912107,0.00008382916,0.00016620291,0.000061403094,0.0006453875,0.00012062864,0.05854002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99677676,0.00019693821,0.0009274875,0.001229228,0.0005080258,0.00036153637],"domain_scores_gemma":[0.99342924,0.0005359263,0.0016581375,0.00061113434,0.0036105793,0.0001549815],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040577195,0.00065788795,0.0006902873,0.0005943393,0.0007473146,0.00018925466,0.00037073292,0.0007085324,0.00037950138],"category_scores_gemma":[0.0007484413,0.0007572875,0.00015278335,0.00056819816,0.00031473066,0.0014195777,0.00018956978,0.0008998658,0.000017530829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024170475,0.000059082442,0.00001417248,0.00006188134,0.0000807565,0.0000018168039,0.0029758944,0.95216554,0.000028617205,0.042776126,0.000063221516,0.0017487027],"study_design_scores_gemma":[0.0010757726,0.00017074267,0.000015150123,0.00021363058,0.00009318066,0.000011153344,0.0004413677,0.97851145,0.00009936116,0.0028478932,0.015819754,0.00070053467],"about_ca_topic_score_codex":0.00005542741,"about_ca_topic_score_gemma":0.000029594263,"teacher_disagreement_score":0.039928235,"about_ca_system_score_codex":0.00021600092,"about_ca_system_score_gemma":0.0006912508,"threshold_uncertainty_score":0.9994878},"labels":[],"label_agreement":null},{"id":"W1788829857","doi":"10.1016/j.apm.2015.09.008","title":"Multi-fidelity shape optimization of hydraulic turbine runner blades using a multi-objective mesh adaptive direct search algorithm","year":2015,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TransCanada (Canada); Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Solver; Fidelity; Mathematical optimization; Computer science; Inviscid flow; Algorithm; High fidelity; Turbine; Optimization problem; Engineering; Mathematics; Mechanical engineering; Aerospace engineering","score_opus":0.0888120996797342,"score_gpt":0.30487300785960314,"score_spread":0.21606090817986895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1788829857","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":[0.00076717284,0.00009652223,0.99655426,0.00002809138,0.00009511159,0.0011900465,0.000016395032,0.0003399471,0.0009124293],"genre_scores_gemma":[0.17607139,0.00001625421,0.8236081,0.000054603093,0.00005158309,0.00007686309,0.0000100495945,0.00006780893,0.00004336508],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99623406,0.00016908058,0.000953591,0.0010660049,0.0009008845,0.0006763751],"domain_scores_gemma":[0.99696535,0.00042770657,0.00039429427,0.0007942703,0.0010185024,0.00039989725],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010130794,0.0005108493,0.0008359515,0.00032690165,0.00020636662,0.00011697102,0.0007836143,0.00023479493,0.000027001297],"category_scores_gemma":[0.00018192905,0.00048081044,0.00015685138,0.001098309,0.00025992398,0.0008096535,0.0005389156,0.00042333992,0.000034764125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031730488,0.0005611844,0.0000016925798,0.000041058407,0.00007431328,0.0000062980785,0.0032576355,0.98497367,0.0004209382,0.005978246,0.0000015308027,0.0046517146],"study_design_scores_gemma":[0.0017320153,0.00007482593,0.000002529632,0.00008853754,0.000045527147,0.000014308733,0.00064026535,0.9859809,0.0050222403,0.0058967597,0.000001665069,0.0005003936],"about_ca_topic_score_codex":0.000054769265,"about_ca_topic_score_gemma":0.0000011893605,"teacher_disagreement_score":0.17530422,"about_ca_system_score_codex":0.00034804075,"about_ca_system_score_gemma":0.0002335909,"threshold_uncertainty_score":0.9997644},"labels":[],"label_agreement":null},{"id":"W1833684988","doi":"10.1007/0-387-23152-8_57","title":"A New Method to Construct the Non-Dominated Set in Multi-Objective Genetic Algorithms","year":2006,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Construct (python library); Benchmark (surveying); Convergence (economics); Set (abstract data type); Mathematical optimization; Computer science; Algorithm; Genetic algorithm; Cluster analysis; Multi-objective optimization; Mathematics; Artificial intelligence","score_opus":0.02084364257414391,"score_gpt":0.302866864529187,"score_spread":0.2820232219550431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1833684988","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":[5.867567e-7,0.0001288464,0.9057192,0.00026021388,0.0004973688,0.0019258405,0.000045632703,0.00021021129,0.09121211],"genre_scores_gemma":[0.00003298021,0.000018387285,0.7563311,0.00056559755,0.00010753935,0.00006313961,0.00001743253,0.00008755578,0.24277626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99605215,0.00015249236,0.0008578885,0.0016469631,0.0006189654,0.00067154015],"domain_scores_gemma":[0.9969475,0.00050157675,0.00041731185,0.0013481553,0.00048393194,0.00030152625],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004235033,0.00086592574,0.0008595957,0.00075411604,0.00017519506,0.00021927511,0.0018403947,0.00044666443,0.00018328789],"category_scores_gemma":[0.00006218042,0.0006927836,0.00023145619,0.0006035914,0.00013683588,0.00032216817,0.0008836233,0.0008195471,0.00029278896],"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":[0.000052408996,0.00010628707,0.000042849624,0.000029955405,0.0002842417,0.00048971135,0.0028440726,0.19992156,0.000173275,0.039657973,0.007693795,0.7487039],"study_design_scores_gemma":[0.003147548,0.00019683993,0.0010308399,0.0001632576,0.000053289816,0.0002491112,0.00007282155,0.9597177,0.0008597093,0.01227166,0.020484295,0.0017529089],"about_ca_topic_score_codex":0.0010996367,"about_ca_topic_score_gemma":0.0005178005,"teacher_disagreement_score":0.75979614,"about_ca_system_score_codex":0.00055129174,"about_ca_system_score_gemma":0.00057953224,"threshold_uncertainty_score":0.9995523},"labels":[],"label_agreement":null},{"id":"W187020170","doi":"10.5555/1999416.1999433","title":"Multiobjective evolutionary optimization of a transportation fleet with a modified monetary cost function","year":2010,"lang":"en","type":"article","venue":"Summer Computer Simulation Conference","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Defence Research and Development Canada","funders":"","keywords":"Computer science; Multi-objective optimization; Sorting; Genetic algorithm; Mathematical optimization; Pareto principle; Monte Carlo method; Set (abstract data type); Evolutionary algorithm; Function (biology); Fleet management; Operations research; Engineering; Mathematics; Machine learning","score_opus":0.022515919018922703,"score_gpt":0.259158231377448,"score_spread":0.2366423123585253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W187020170","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":[0.005635867,0.000013069327,0.99222785,0.00008589376,0.00062547676,0.0008684521,0.00003771517,0.00028374046,0.00022191313],"genre_scores_gemma":[0.5711649,0.0000030946806,0.42848554,0.00006670502,0.000057842,0.000040827625,0.00014558187,0.000015476986,0.000019988425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787503,0.00010543641,0.0005216078,0.000715852,0.0005004778,0.00028158302],"domain_scores_gemma":[0.99706787,0.00023060333,0.0004392511,0.00057633,0.0015564286,0.00012952591],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001441929,0.00030943402,0.00030732958,0.00032917009,0.00018734316,0.00008840429,0.00042647062,0.00015711959,0.000052776446],"category_scores_gemma":[0.000026019361,0.0003025423,0.00007731734,0.00081809325,0.00014482575,0.0018241815,0.000049157585,0.00031364535,0.000008635634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012134526,0.00014944683,0.001221768,0.000013750988,0.00004236273,0.000002870504,0.00064859726,0.9698361,0.0002166506,0.006942888,0.000010628533,0.02079359],"study_design_scores_gemma":[0.0015732435,0.00019627358,0.03042729,0.000031421867,0.000026159096,0.0000040740047,0.000024887086,0.96647257,0.00030878742,0.00050123874,0.000097839526,0.00033623443],"about_ca_topic_score_codex":0.000056759764,"about_ca_topic_score_gemma":0.000080419646,"teacher_disagreement_score":0.56552905,"about_ca_system_score_codex":0.000056682275,"about_ca_system_score_gemma":0.00019343744,"threshold_uncertainty_score":0.99994266},"labels":[],"label_agreement":null},{"id":"W1871676304","doi":"","title":"Bayesian optimization in high dimensions via random embeddings","year":2013,"lang":"en","type":"article","venue":"UvA-DARE (University of Amsterdam)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":242,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bayesian optimization; Embedding; Computer science; Categorical variable; Solver; Bayesian probability; Artificial intelligence; Optimization problem; Integer programming; Mathematical optimization; Machine learning; Algorithm; Mathematics","score_opus":0.005049378076417137,"score_gpt":0.1907773810526467,"score_spread":0.18572800297622954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1871676304","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":[0.008047475,0.000017013197,0.9894771,0.0007723423,0.00017727679,0.00043267908,0.0000052055434,0.00013414297,0.00093677535],"genre_scores_gemma":[0.42590007,0.000020870455,0.5734137,0.00009123537,0.000010260322,8.9406575e-7,0.000013686721,0.000010857015,0.00053836545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987416,0.0000994376,0.00018734056,0.0004463155,0.00025039143,0.00027490064],"domain_scores_gemma":[0.9988166,0.000102308804,0.00020038727,0.0004441165,0.00030807962,0.00012848403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000113564674,0.00016858733,0.00028852114,0.00037059406,0.00018980759,0.000045002955,0.0006382253,0.00009800473,0.0003577023],"category_scores_gemma":[0.000036703797,0.00020625142,0.00007846171,0.00079331757,0.00010972443,0.001846663,0.00037842692,0.00014375754,0.00006825211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056507477,0.00024561875,0.0011890943,0.000027203267,0.000043929642,0.000055780765,0.004770526,0.96310025,0.00088384136,0.001774185,0.00057160086,0.027281456],"study_design_scores_gemma":[0.0027993065,0.00006112404,0.0037811697,0.000044015105,0.0000086062055,0.0000076493325,0.00056308886,0.99142706,0.00016391887,0.0007769536,0.00012429823,0.00024279409],"about_ca_topic_score_codex":0.00063574617,"about_ca_topic_score_gemma":0.00006976183,"teacher_disagreement_score":0.4178526,"about_ca_system_score_codex":0.00013419605,"about_ca_system_score_gemma":0.000048012014,"threshold_uncertainty_score":0.84106886},"labels":[],"label_agreement":null},{"id":"W1951115035","doi":"10.24908/pceea.v0i0.4810","title":"Enhance Derivative Design Considering Global Sensitivity of Design Parameters","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Sensitivity (control systems); Multidisciplinary design optimization; Engineering design process; Computer science; Material derivative; Mathematical optimization; Curse of dimensionality; Aerodynamics; Variable (mathematics); Design process; Product design; Product (mathematics); Multidisciplinary approach; Mathematics; Engineering; Process engineering; Aerospace engineering; Artificial intelligence; Mechanical engineering; Process integration","score_opus":0.011479184713670971,"score_gpt":0.2221849050328735,"score_spread":0.21070572031920254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1951115035","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":[0.01656154,0.000030284642,0.97975534,0.0017437929,0.0006714185,0.00080037565,0.0000073857327,0.000092366754,0.00033750382],"genre_scores_gemma":[0.5807461,0.0000031429706,0.4189414,0.00012876348,0.000017480063,0.000053894346,6.3943384e-7,0.000010900734,0.000097670956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987421,0.000042916585,0.00031436465,0.00027090227,0.00031753938,0.00031217633],"domain_scores_gemma":[0.99732894,0.00030338267,0.0005798123,0.00015558576,0.0014548111,0.0001774827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064561726,0.00016818162,0.00020944397,0.00016811625,0.00012616324,0.0001165845,0.00036972365,0.00010698749,0.000006392728],"category_scores_gemma":[0.0032460615,0.00017466374,0.000064792635,0.00089597504,0.000035401597,0.0008320343,0.00005757909,0.00014007294,0.000007270187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007874208,0.00025903998,0.030460645,0.00026698157,0.00042195022,6.15358e-7,0.0059820646,0.8409769,0.054984123,0.032227382,0.012108248,0.022304183],"study_design_scores_gemma":[0.0002830006,0.000043051718,0.06935493,0.00020371797,0.000029707144,0.000012033577,0.00031242368,0.75922865,0.16646498,0.0033393446,0.00020675884,0.0005213682],"about_ca_topic_score_codex":0.0048464173,"about_ca_topic_score_gemma":0.0003874112,"teacher_disagreement_score":0.56418455,"about_ca_system_score_codex":0.0021700868,"about_ca_system_score_gemma":0.00082126603,"threshold_uncertainty_score":0.7326365},"labels":[],"label_agreement":null},{"id":"W1959931857","doi":"10.1007/s00158-015-1289-x","title":"Pareto front spacing with differential geometry in multidisciplinary systems","year":2015,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multi-objective optimization; Multidisciplinary design optimization; Mathematical optimization; Pareto principle; Engineering design process; Multidisciplinary approach; Differential (mechanical device); Mathematics; Computer science; Applied mathematics; Engineering; Mechanical engineering","score_opus":0.015826606312884108,"score_gpt":0.2571660949186689,"score_spread":0.24133948860578477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1959931857","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":[0.20252241,0.0002721196,0.7953717,0.0001461754,0.0006934987,0.0006049819,0.000010992026,0.00019358021,0.00018454451],"genre_scores_gemma":[0.67190886,0.000031381955,0.32767263,0.0000065744644,0.00008955835,0.000045775887,0.0000675281,0.000027051949,0.00015065614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763715,0.00013787659,0.00045416065,0.00084974954,0.0004395029,0.0004815305],"domain_scores_gemma":[0.99866825,0.0000682296,0.00023602037,0.00043591516,0.0002795581,0.00031203698],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017312069,0.0004013376,0.0004066826,0.00038698682,0.00033559202,0.00022397604,0.00037029275,0.00013918083,0.0000070573296],"category_scores_gemma":[0.00005196831,0.00031303038,0.000039258863,0.00063052005,0.00013737188,0.0017452283,0.0005842647,0.00025074452,0.000004502219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010861318,0.000039439572,0.011006291,0.000037060276,0.000016403736,0.00004498641,0.0030532833,0.98357886,0.000060720875,0.00071931473,0.0000059326685,0.0013291106],"study_design_scores_gemma":[0.0025168962,0.0002338541,0.022700347,0.000081743274,0.000016911266,0.0001280774,0.0014954418,0.97199,0.00007627203,0.00028419483,0.0000038996072,0.00047234527],"about_ca_topic_score_codex":0.00009221378,"about_ca_topic_score_gemma":0.00005266636,"teacher_disagreement_score":0.46938643,"about_ca_system_score_codex":0.00023239256,"about_ca_system_score_gemma":0.00009988406,"threshold_uncertainty_score":0.99993217},"labels":[],"label_agreement":null},{"id":"W1966864823","doi":"10.1115/1.1904639","title":"An Efficient Pareto Set Identification Approach for Multiobjective Optimization on Black-Box Functions","year":2004,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":123,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Pareto principle; Robustness (evolution); Multi-objective optimization; Black box; Computer science; Computation; Set (abstract data type); Identification (biology); Engineering design process; Algorithm; Mathematics; Engineering; Artificial intelligence","score_opus":0.0375184653111403,"score_gpt":0.29753607695384204,"score_spread":0.26001761164270176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966864823","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":[0.00013619888,0.000012531363,0.9982172,0.00021002174,0.00051858096,0.00078596955,0.0000104458095,0.00008065772,0.000028358958],"genre_scores_gemma":[0.21963118,0.000008848314,0.7800241,0.0001121542,0.00012754064,0.00003981615,0.00001157558,0.000021717782,0.000023046427],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997861,0.0002502573,0.00065615826,0.00044395612,0.0005329117,0.00025567965],"domain_scores_gemma":[0.99734944,0.00024791478,0.0006712927,0.00041901198,0.0011005644,0.00021175401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011892392,0.0002005562,0.0002722065,0.00030515608,0.00023870174,0.00016611429,0.0006013787,0.0001296758,0.0000048249435],"category_scores_gemma":[0.00056892727,0.0001776848,0.0001562416,0.0005695037,0.000042119846,0.0007524366,0.000035853067,0.0002521225,0.00000928203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001431577,0.00066754466,2.825713e-7,0.000005228733,0.00003098282,0.0000033052904,0.000341569,0.98742837,0.0018760543,0.007237511,0.000040783198,0.0022251988],"study_design_scores_gemma":[0.00195554,0.0011621283,0.000022803932,0.000023200455,0.000031286487,0.000042110612,0.00021831495,0.9788583,0.014797582,0.0026866032,0.000011472849,0.00019064576],"about_ca_topic_score_codex":0.0000014032121,"about_ca_topic_score_gemma":2.7298924e-7,"teacher_disagreement_score":0.21949498,"about_ca_system_score_codex":0.00045157218,"about_ca_system_score_gemma":0.00019019053,"threshold_uncertainty_score":0.72457755},"labels":[],"label_agreement":null},{"id":"W1966918386","doi":"10.1007/s00158-010-0529-3","title":"A comparative study of metamodeling methods considering sample quality merits","year":2010,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":96,"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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid","keywords":"Metamodeling; Kriging; Computer science; Robustness (evolution); Artificial neural network; Sample (material); Sample size determination; Data mining; Machine learning; Artificial intelligence; Mathematics; Statistics","score_opus":0.05472640559811635,"score_gpt":0.40639614121822254,"score_spread":0.35166973562010617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966918386","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":[0.31521693,0.000029602637,0.6838413,0.000035548484,0.00032391175,0.00041937034,0.000008883988,0.000084170744,0.000040308307],"genre_scores_gemma":[0.46346575,0.000004898043,0.536472,0.0000045162556,0.000017035853,0.000014362801,0.000009125617,0.0000066570697,0.0000056315166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817073,0.0002866984,0.0005375084,0.00056742504,0.0002229512,0.00021465545],"domain_scores_gemma":[0.9983086,0.0004368063,0.00034135673,0.00040592597,0.00039678506,0.000110510664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000405239,0.00023953551,0.00045065006,0.00015215504,0.00042726102,0.00007194859,0.00029125108,0.000072182884,0.000016182326],"category_scores_gemma":[0.00021765206,0.00020672276,0.0000477947,0.00043651642,0.000130603,0.0009890718,0.0004518136,0.00023906579,3.0693747e-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":[0.000028806442,0.00008532001,0.0010506596,0.00002043452,0.000046491707,0.0000011660195,0.011474113,0.9686086,0.00853663,0.003339215,3.4628982e-7,0.0068082185],"study_design_scores_gemma":[0.0010227418,0.00014056005,0.0059179263,0.0000063374173,0.000027824008,0.000013627895,0.0020705708,0.98529273,0.0029648326,0.002287235,0.0000019078277,0.0002537331],"about_ca_topic_score_codex":0.00009220703,"about_ca_topic_score_gemma":0.00007230197,"teacher_disagreement_score":0.14824882,"about_ca_system_score_codex":0.000018476869,"about_ca_system_score_gemma":0.000038722057,"threshold_uncertainty_score":0.84299093},"labels":[],"label_agreement":null},{"id":"W1967532012","doi":"10.1115/power2013-98149","title":"Multi-Objective Optimization of Runner Blades Using a Multi-Fidelity Algorithm","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Andritz (Canada); Polytechnique Montréal","funders":"","keywords":"Solver; Mathematical optimization; Computer science; Fidelity; Process (computing); Turbine; Variable (mathematics); Multi-objective optimization; Range (aeronautics); Constraint (computer-aided design); Algorithm; Mathematics; Engineering; Mechanical engineering","score_opus":0.028195408378132374,"score_gpt":0.2873632761045762,"score_spread":0.2591678677264438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967532012","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":[0.00057388854,0.00006311837,0.9975977,0.000064356784,0.00029064834,0.0008436873,0.000009326738,0.00032469147,0.00023258585],"genre_scores_gemma":[0.019603249,0.000020236443,0.9796191,0.0001697398,0.000037741283,0.0000556001,0.000007983928,0.000034479555,0.0004518614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764514,0.00015707582,0.00062408624,0.00075135165,0.00038281604,0.00043953356],"domain_scores_gemma":[0.99720865,0.00012612368,0.00038175745,0.00071154186,0.00139566,0.00017626224],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002385336,0.00032433326,0.0003843909,0.000301792,0.00019119514,0.00013757485,0.00068405794,0.00014751215,0.00018354699],"category_scores_gemma":[0.00022342603,0.00029740355,0.00012662166,0.0009984492,0.00016622487,0.002388082,0.00040004248,0.00018981265,0.000047984857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002518862,0.00040697222,0.00029783644,0.000011220096,0.000047962032,0.0000027487429,0.00074004027,0.96975106,0.003809733,0.00028867353,0.000016982392,0.024624266],"study_design_scores_gemma":[0.0012396598,0.000047243164,0.002377099,0.000020696056,0.000010259035,0.000013497969,0.00024911293,0.9881096,0.0074420804,0.00012994575,0.000010423774,0.00035039944],"about_ca_topic_score_codex":0.00075142726,"about_ca_topic_score_gemma":0.000013414182,"teacher_disagreement_score":0.024273867,"about_ca_system_score_codex":0.00019860081,"about_ca_system_score_gemma":0.00012877882,"threshold_uncertainty_score":0.9999478},"labels":[],"label_agreement":null},{"id":"W1968093411","doi":"10.4028/www.scientific.net/amm.48-49.314","title":"Cognitive Radio Decision Engine Based on Multi-Objective Genetic Algorithm","year":2011,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Cognitive radio; Weighting; Genetic algorithm; Computer science; Mathematical optimization; Population; Adaptation (eye); Multi-objective optimization; Optimization problem; Algorithm; Machine learning; Mathematics; Wireless; Telecommunications","score_opus":0.018372302589333426,"score_gpt":0.24030071739757222,"score_spread":0.2219284148082388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968093411","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":[0.0007253884,0.000027884347,0.9974472,0.000007906285,0.00058710855,0.0007319864,0.000052235988,0.00017790312,0.00024240016],"genre_scores_gemma":[0.12490525,0.00005801564,0.87442243,0.00035236086,0.00004718544,0.00014990762,0.0000120666,0.000035364752,0.000017405735],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983587,0.00005495398,0.00030946778,0.0007070304,0.00023887366,0.00033096367],"domain_scores_gemma":[0.99899995,0.0001551402,0.00016783657,0.00037115242,0.00015610596,0.00014978282],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028104923,0.00029639635,0.00032902445,0.00018658853,0.0001554075,0.00009493742,0.0003070422,0.00012387687,0.00005950145],"category_scores_gemma":[0.00006055614,0.00027496513,0.00003503713,0.0002433887,0.000023799694,0.00015535539,0.00019191841,0.00009208134,0.00005294888],"study_design_candidate":"design_other","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":[0.0003906056,0.00087138155,0.0000018537739,0.000037598864,0.000107114196,0.00010326067,0.002186547,0.0013086684,0.017586999,0.17558335,0.00003159232,0.801791],"study_design_scores_gemma":[0.005010727,0.00048941415,0.00049506885,0.00008585961,0.000043210857,0.000025723075,0.00014475617,0.6213558,0.32746506,0.043980043,0.00008996949,0.0008143597],"about_ca_topic_score_codex":0.000015224883,"about_ca_topic_score_gemma":0.000001028418,"teacher_disagreement_score":0.8009767,"about_ca_system_score_codex":0.00005030892,"about_ca_system_score_gemma":0.00004676804,"threshold_uncertainty_score":0.99997026},"labels":[],"label_agreement":null},{"id":"W1969411792","doi":"10.1115/detc2008-50061","title":"A Study of Covariance Functions for Multi-Response Metamodeling for Simulation-Based Design and Optimization","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Pennsylvania Department of Community and Economic Development","keywords":"Metamodeling; Covariance; Computer science; Covariance function; Engineering design process; Set (abstract data type); Design of experiments; Mathematical optimization; Machine learning; Algorithm; Mathematics; Covariance matrix; Engineering; Statistics","score_opus":0.12711970093626482,"score_gpt":0.34310777848872687,"score_spread":0.21598807755246205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969411792","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":[0.00070072076,0.000018101158,0.99638635,0.00004959949,0.00009098966,0.0026177785,0.000008796662,0.00012627471,0.0000013764194],"genre_scores_gemma":[0.24064673,0.0000012261163,0.7588146,0.000053686777,0.000007932916,0.00032378544,0.000004026061,0.000013867203,0.00013417612],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988539,0.00013328246,0.0003171514,0.00040388657,0.00013825737,0.00015348375],"domain_scores_gemma":[0.9964108,0.002302989,0.00016436726,0.00028084646,0.00078612054,0.00005490995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004569004,0.00013596982,0.00019806252,0.0001832337,0.0003433194,0.000028554508,0.00016848769,0.00004322649,0.0000027787796],"category_scores_gemma":[0.00090316834,0.00013250687,0.000045298308,0.00037387112,0.000036376252,0.00047932655,0.00003257887,0.000033969533,3.797342e-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":[0.0003446285,0.00036484437,0.000029740388,0.000005331099,0.000022482169,4.3464647e-7,0.00058698264,0.9978407,0.00008776708,0.000102575024,0.00000411505,0.0006103872],"study_design_scores_gemma":[0.0046280585,0.0005283474,0.00007931265,0.0000043836453,0.000019614155,0.000001267589,0.00012913605,0.9939847,0.00043941953,0.000028082128,0.000012164585,0.00014552762],"about_ca_topic_score_codex":0.0000053544727,"about_ca_topic_score_gemma":0.000002084844,"teacher_disagreement_score":0.23994601,"about_ca_system_score_codex":0.00003979849,"about_ca_system_score_gemma":0.00012815683,"threshold_uncertainty_score":0.5403474},"labels":[],"label_agreement":null},{"id":"W1969784127","doi":"10.1007/s11081-009-9082-6","title":"Benchmarking multidisciplinary design optimization algorithms","year":2009,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":134,"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":"Benchmarking; Multidisciplinary design optimization; Computer science; Modular design; Python (programming language); Multidisciplinary approach; Algorithm; Programming language","score_opus":0.011916215872024604,"score_gpt":0.2317326145440032,"score_spread":0.2198163986719786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969784127","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":[0.00001784264,0.00012308064,0.99826986,0.0002552694,0.0002825751,0.00027033754,0.0000010890302,0.00053122506,0.00024870332],"genre_scores_gemma":[0.007978588,0.0002422978,0.99146783,0.000098809505,0.000087614455,0.00001646563,0.000019227167,0.000022788869,0.000066364766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987094,0.000035939018,0.00027584095,0.0004708925,0.00020160402,0.0003063594],"domain_scores_gemma":[0.999265,0.00007337108,0.00009341603,0.00028286772,0.00013413938,0.00015118648],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020227439,0.00024043593,0.00018533239,0.00026211486,0.00021084187,0.00019594686,0.00025266985,0.00009634271,0.000021032041],"category_scores_gemma":[0.00006936035,0.00026097268,0.000036666966,0.00065553817,0.000017724125,0.0012214821,0.000094781746,0.00013295458,0.0000027891163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027467165,0.000029255167,0.000009650037,0.0000044089606,0.0000065484664,0.000006370304,0.00022475795,0.9857853,0.00008896067,0.00085307885,0.000012508865,0.012976429],"study_design_scores_gemma":[0.00046790097,0.00008441225,0.0002820342,0.000029148472,0.000005741778,0.000030397889,0.000013273997,0.99849814,0.00020016037,0.000039169143,0.000039665945,0.00030994826],"about_ca_topic_score_codex":0.0000010647044,"about_ca_topic_score_gemma":5.2262454e-8,"teacher_disagreement_score":0.012712862,"about_ca_system_score_codex":0.000075153584,"about_ca_system_score_gemma":0.000026909462,"threshold_uncertainty_score":0.99998426},"labels":[],"label_agreement":null},{"id":"W1969837585","doi":"10.1109/nabic.2009.5393704","title":"Solving multiple-objective optimization problems using GISMOO algorithm","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Benchmark (surveying); Pareto principle; Computer science; Genetic algorithm; Algorithm; Artificial immune system; Multi-objective optimization; Mathematical optimization; Optimization algorithm; Optimization problem; Pareto optimal; Artificial intelligence; Mathematics; Machine learning","score_opus":0.01842879423602076,"score_gpt":0.260995659927023,"score_spread":0.24256686569100225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969837585","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":[0.00005234094,0.00007138331,0.9951911,0.00021957318,0.00035525375,0.00059174426,0.0000028809602,0.0007587251,0.0027570121],"genre_scores_gemma":[0.02017849,0.000024837977,0.9787737,0.0005427174,0.000099994795,0.000014542123,0.000008951054,0.000025287638,0.0003314641],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977686,0.00009286125,0.00041088238,0.00079575944,0.00040749818,0.0005244054],"domain_scores_gemma":[0.99842006,0.00011554376,0.00022789388,0.00057173264,0.0005000683,0.00016472863],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023272316,0.00031293635,0.00027073594,0.000297858,0.0003670939,0.0002655729,0.00062985736,0.000120341894,0.000037506834],"category_scores_gemma":[0.00016866249,0.00030815252,0.000093102404,0.0011126846,0.000052748695,0.0022972305,0.00017476628,0.00020926502,0.00002053735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020581565,0.00009837274,0.000039576047,0.0000021511314,0.000011328259,0.0000071284812,0.0004814144,0.90920717,0.0004826165,0.0011193913,0.000015811353,0.088533014],"study_design_scores_gemma":[0.00081232074,0.00009920263,0.0002237762,0.000025966794,0.000006710939,0.00004498483,0.0000706531,0.9950897,0.0019329442,0.001204025,0.0000820574,0.00040766667],"about_ca_topic_score_codex":0.000035990764,"about_ca_topic_score_gemma":0.0000033519386,"teacher_disagreement_score":0.08812534,"about_ca_system_score_codex":0.0002833169,"about_ca_system_score_gemma":0.00010885103,"threshold_uncertainty_score":0.99993706},"labels":[],"label_agreement":null},{"id":"W1970596070","doi":"10.1115/detc2008-49702","title":"A Hybrid Relationship Modeling Scheme for Parametric Design Considering Uncertainties","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Parametric statistics; Mathematical optimization; Computer science; Fuzzy logic; Scheme (mathematics); Design of experiments; Optimal design; Mathematics; Artificial intelligence; Machine learning; Statistics","score_opus":0.13520862043381765,"score_gpt":0.29615701153399215,"score_spread":0.1609483911001745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970596070","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":[0.0019403591,0.0001021467,0.99647164,0.00016089025,0.00013709301,0.00045960327,0.0000010795587,0.0003959129,0.0003312763],"genre_scores_gemma":[0.36114305,0.000011815372,0.6383162,0.00009553512,0.000017205884,0.00006132256,0.0000014455072,0.00001061249,0.00034278238],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883825,0.000047036538,0.00025950812,0.000394335,0.00018524949,0.0002756311],"domain_scores_gemma":[0.9983767,0.0008139712,0.00007702945,0.0003389698,0.000314187,0.00007912541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022834673,0.00014204002,0.00015549958,0.00027910006,0.00040390843,0.0000650399,0.00030306718,0.000037103044,0.0000063169246],"category_scores_gemma":[0.0011014408,0.00014101953,0.00006158701,0.0005473186,0.00004963202,0.0007902658,0.00009080246,0.00010215669,0.000018260487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048425777,0.000018640863,0.00021445008,0.0000037319767,0.00000801107,0.0000049057294,0.0001463816,0.9821028,0.000019109282,0.016642937,0.000110254674,0.0007239011],"study_design_scores_gemma":[0.0004425171,0.000033368065,0.00003261876,0.00000621167,0.0000023101086,0.000058421192,0.000032574568,0.9835219,0.0011022815,0.014515633,0.00006204468,0.00019011844],"about_ca_topic_score_codex":0.000010169562,"about_ca_topic_score_gemma":4.8971094e-7,"teacher_disagreement_score":0.3592027,"about_ca_system_score_codex":0.00009111664,"about_ca_system_score_gemma":0.00013407787,"threshold_uncertainty_score":0.5750609},"labels":[],"label_agreement":null},{"id":"W1970883045","doi":"10.1243/14644207jmda260","title":"Multi-objective evolutionary optimization of polynomial neural networks for fatigue life modelling and prediction of unidirectional carbon-fibre-reinforced plastics composites","year":2010,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial neural network; Group method of data handling; Multi-objective optimization; Evolutionary algorithm; Pareto principle; Computer science; Materials science; Structural engineering; Mathematical optimization; Mathematics; Artificial intelligence; Engineering; Machine learning","score_opus":0.020856864684263652,"score_gpt":0.2288680963270359,"score_spread":0.20801123164277224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970883045","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":[0.016445193,0.00005021791,0.9825153,0.000042586853,0.00033878288,0.0005490614,0.000041498264,0.00001421182,0.0000031730358],"genre_scores_gemma":[0.55264866,0.00004993683,0.44720298,0.000003176404,0.0000652518,0.000020909347,0.0000025190805,0.0000053449753,0.0000012115426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988177,0.000017036598,0.00072387944,0.00014238273,0.00020201308,0.00009703135],"domain_scores_gemma":[0.9976382,0.0001869497,0.0009854014,0.00007494169,0.0010416576,0.00007281725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039580482,0.00012220175,0.0003006597,0.00014209213,0.00009573255,0.000017325287,0.00023243575,0.00011158847,7.6365325e-7],"category_scores_gemma":[0.0002183881,0.00010356368,0.00006027135,0.00023610763,0.00017256667,0.0003578336,0.00006722579,0.00012369822,7.3817072e-9],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105884545,0.00004727102,0.0000066488587,0.00005247284,0.000035570632,1.5227764e-8,0.000054028165,0.80758935,0.18105009,0.010986051,0.0000054247466,0.00006720189],"study_design_scores_gemma":[0.0006489689,0.00013200018,0.000026751164,0.00006355714,0.000045531757,0.000016598398,0.000029283983,0.79049313,0.20833349,0.00014807563,0.000005039671,0.000057595244],"about_ca_topic_score_codex":0.0000043270543,"about_ca_topic_score_gemma":8.3760455e-8,"teacher_disagreement_score":0.5362035,"about_ca_system_score_codex":0.000024577495,"about_ca_system_score_gemma":0.000087434506,"threshold_uncertainty_score":0.42232046},"labels":[],"label_agreement":null},{"id":"W1971004571","doi":"10.1145/1555386.1555389","title":"pyMDO","year":2009,"lang":"en","type":"article","venue":"ACM Transactions on Mathematical Software","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Christian Studies; University of Toronto","funders":"","keywords":"Computer science; Python (programming language); Fortran; Interface (matter); Object-oriented programming; Programming language; Focus (optics); Computational science; Parallel computing","score_opus":0.01763026921332817,"score_gpt":0.27442120303162154,"score_spread":0.2567909338182934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971004571","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":[0.00006875862,0.000013354679,0.996716,0.0017180996,0.000102442486,0.00020845258,0.000004497536,0.00083573814,0.0003326395],"genre_scores_gemma":[0.012316481,0.000008420627,0.9860886,0.000886906,0.000018311748,0.00002499911,0.0000013020922,0.0000134493785,0.0006415362],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987424,0.00003366641,0.0002455448,0.00037941927,0.0003184871,0.0002804895],"domain_scores_gemma":[0.99837935,0.00032586986,0.000052211653,0.0009884562,0.000100049845,0.00015407958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000974064,0.00018141768,0.00019246958,0.00011427445,0.00021554838,0.00008772352,0.0008414674,0.000081766244,0.00024321568],"category_scores_gemma":[0.00028433657,0.00016181619,0.000110595836,0.00048528294,0.00004278589,0.0004832647,0.000011341281,0.00022938264,0.0005814484],"study_design_candidate":"design_other","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":[0.0000062648724,0.0005091819,0.0000015245773,0.000011147572,0.000015141195,0.000013621824,0.0003833286,0.0135471225,0.000043855343,0.00868066,0.000021768577,0.9767664],"study_design_scores_gemma":[0.00054520194,0.00023790944,0.00018430888,0.00005031113,0.000013544772,0.000053543143,0.000032188836,0.02911868,0.0020048171,0.96686107,0.0005474538,0.0003509472],"about_ca_topic_score_codex":4.4119906e-7,"about_ca_topic_score_gemma":2.9067306e-7,"teacher_disagreement_score":0.97641546,"about_ca_system_score_codex":0.00007059443,"about_ca_system_score_gemma":0.000028461562,"threshold_uncertainty_score":0.7473536},"labels":[],"label_agreement":null},{"id":"W1971138856","doi":"10.1007/s00158-005-0523-3","title":"Hybrid evolutionary algorithm and application to structural optimization","year":2005,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","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":"Toronto Metropolitan University","funders":"","keywords":"Mathematical optimization; Truss; Evolutionary algorithm; Convergence (economics); Computer science; Local optimum; Algorithm; Benchmarking; Heuristic; Genetic algorithm; Global optimization; Engineering design process; Mathematics; Engineering","score_opus":0.005438449798877566,"score_gpt":0.25516550440984653,"score_spread":0.24972705461096897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971138856","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":[0.004880775,0.00031118598,0.991708,0.0014732364,0.00028893436,0.0008637555,0.000042085387,0.00033730242,0.00009475778],"genre_scores_gemma":[0.11098168,0.00011054816,0.8880747,0.00015883305,0.00022321373,0.000070821945,0.00019594509,0.000030377858,0.00015387725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978147,0.000075374,0.00043753054,0.0009565134,0.00031990535,0.0003959789],"domain_scores_gemma":[0.99871135,0.00005907498,0.0001944414,0.00040645193,0.0003214187,0.00030726407],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011119156,0.00037458737,0.00026873828,0.00028281173,0.0008735905,0.00018506244,0.00033351433,0.000102116865,0.000024695284],"category_scores_gemma":[0.000046503534,0.00035441297,0.00004417664,0.00048408183,0.00013006384,0.002225072,0.00056148117,0.00016571657,0.0000069831203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016924174,0.000010107689,0.00011739677,0.000010582334,0.000010402808,0.0000018761364,0.00046004463,0.8721667,0.00007407687,0.0011512473,0.00002039494,0.12596028],"study_design_scores_gemma":[0.00079629646,0.000099257435,0.004213208,0.000014109649,0.000019685998,0.00018092181,0.000066298584,0.99290013,0.00019362447,0.0009661308,0.00008622629,0.0004640914],"about_ca_topic_score_codex":0.000014256572,"about_ca_topic_score_gemma":0.0000028045704,"teacher_disagreement_score":0.12549618,"about_ca_system_score_codex":0.00016443754,"about_ca_system_score_gemma":0.000045141565,"threshold_uncertainty_score":0.9998908},"labels":[],"label_agreement":null},{"id":"W1973412675","doi":"10.1115/1.1561044","title":"Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points","year":2003,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":514,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Chonnam National University; Fonds National de la Recherche Luxembourg; University of Florida","keywords":"Latin hypercube sampling; Computation; Hypercube; Computer science; Set (abstract data type); Mathematical optimization; Optimal design; Mathematics; Algorithm; Parallel computing; Monte Carlo method; Statistics; Machine learning","score_opus":0.10890294536531425,"score_gpt":0.331500225385195,"score_spread":0.22259728001988077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973412675","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":[0.00075107085,0.00011643822,0.9980921,0.00021463977,0.0004258499,0.0003225953,0.000001335476,0.00005428789,0.000021674337],"genre_scores_gemma":[0.029372487,0.000018284565,0.9701764,0.00029689833,0.000034458095,0.0000017891388,7.9619575e-8,0.000034950852,0.00006466346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99169755,0.006040524,0.0007985281,0.00039036732,0.000674239,0.00039881628],"domain_scores_gemma":[0.99474347,0.0029984233,0.00070057943,0.0003952556,0.0008529661,0.000309319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007400583,0.0002710711,0.00050933176,0.0002265287,0.00016286268,0.00010978824,0.0007169214,0.00014747544,0.00004458849],"category_scores_gemma":[0.0037850079,0.00023910232,0.00016627462,0.0008835318,0.00003529699,0.00092975283,0.00009689294,0.00051909883,0.000015865844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011722062,0.00022089315,0.0000015768298,0.000002684201,0.000087333836,0.00035815215,0.00028129786,0.8354132,0.15280458,0.0049500936,0.00012717054,0.004580772],"study_design_scores_gemma":[0.0012886438,0.00076918013,0.000016954516,0.000052380605,0.000027069978,0.00081140763,0.00007277677,0.8588385,0.12268034,0.015095319,0.00009076845,0.00025666517],"about_ca_topic_score_codex":0.00000204531,"about_ca_topic_score_gemma":9.887598e-8,"teacher_disagreement_score":0.030124236,"about_ca_system_score_codex":0.00033939126,"about_ca_system_score_gemma":0.0005206827,"threshold_uncertainty_score":0.97503096},"labels":[],"label_agreement":null},{"id":"W1974923626","doi":"10.2514/1.45790","title":"Comparison of Surrogate Models in a Multidisciplinary Optimization Framework for Wing Design","year":2010,"lang":"en","type":"article","venue":"AIAA Journal","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Wing; Multidisciplinary design optimization; Surrogate model; Multidisciplinary approach; Computational fluid dynamics; Shape optimization; Wing configuration; Computer science; Aerospace engineering; Mathematical optimization; Engineering; Structural engineering; Mathematics; Finite element method","score_opus":0.06017781714268427,"score_gpt":0.36647302473989535,"score_spread":0.30629520759721107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974923626","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":[0.002321228,0.00005047253,0.9963892,0.0002461262,0.0006095519,0.00030686433,0.0000016417924,0.00003587674,0.000039006743],"genre_scores_gemma":[0.31453118,0.000012362945,0.68535405,0.000016079557,0.000053431308,0.000011799,0.0000010098759,0.000012434307,0.0000076283663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986813,0.00009169367,0.0004925299,0.00024100345,0.00022873333,0.00026475097],"domain_scores_gemma":[0.99843365,0.00047300587,0.0003918412,0.00025275975,0.00034914855,0.00009961719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006486396,0.00013388536,0.00025622663,0.00024490978,0.00018085312,0.00008604939,0.00051927933,0.000116111085,0.000012191239],"category_scores_gemma":[0.0003099897,0.00013036036,0.00007200422,0.0004223261,0.000047570105,0.001190921,0.000112273694,0.0005276861,0.000001145733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027006814,0.0001295499,0.0006469992,0.0000048032944,0.000007904089,0.0000033901747,0.0017558947,0.9825486,0.0007006258,0.006983759,0.000006972573,0.007184476],"study_design_scores_gemma":[0.00074572,0.00009798695,0.00031227036,0.000054336044,0.0000042270217,0.000029592002,0.00011688742,0.9488776,0.0021777689,0.047447126,0.000003890485,0.00013260866],"about_ca_topic_score_codex":0.000002037281,"about_ca_topic_score_gemma":0.0000031183108,"teacher_disagreement_score":0.31220993,"about_ca_system_score_codex":0.000051529303,"about_ca_system_score_gemma":0.00011735523,"threshold_uncertainty_score":0.53159416},"labels":[],"label_agreement":null},{"id":"W1975069415","doi":"10.1007/s11081-014-9271-9","title":"A new algorithm using front prediction and NSGA-II for solving two and three-objective optimization problems","year":2014,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Sorting; Multi-objective optimization; Mathematical optimization; Algorithm; Genetic algorithm; Evolutionary algorithm; Computer science; Mathematics","score_opus":0.008871635243027535,"score_gpt":0.21655461054962633,"score_spread":0.2076829753065988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975069415","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":[0.00016846259,0.00021903096,0.9984213,0.00007168141,0.00026427937,0.0005463123,0.000006599106,0.0002647035,0.000037618396],"genre_scores_gemma":[0.00422977,0.0001411686,0.99530643,0.000035753066,0.00013526858,0.00003795176,0.000019925745,0.000039610608,0.00005409889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879414,0.000018268762,0.00026245287,0.0005214588,0.00013931481,0.00026439523],"domain_scores_gemma":[0.99927807,0.00009051955,0.00011766614,0.00017200342,0.00016851563,0.0001732293],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023064345,0.00023866595,0.00022050212,0.00021798878,0.0003536545,0.00022349521,0.00009887458,0.0000889369,0.0000045617544],"category_scores_gemma":[0.00012579678,0.00025922147,0.000024686527,0.0002397887,0.000026531286,0.001185002,0.00016032389,0.00010018187,1.5866047e-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":[0.000003154399,0.000009828571,0.000069338144,0.000025757547,0.000017499293,1.9094092e-7,0.00054833497,0.9733542,0.00015142621,0.0005725787,0.000004717188,0.025242971],"study_design_scores_gemma":[0.0014146063,0.00010198614,0.00014852271,0.00007015937,0.000022323045,0.000029833078,0.000021024955,0.99757606,0.000108701206,0.00015195365,0.00008961714,0.00026519797],"about_ca_topic_score_codex":0.000028245897,"about_ca_topic_score_gemma":0.0000044987564,"teacher_disagreement_score":0.024977773,"about_ca_system_score_codex":0.00007240361,"about_ca_system_score_gemma":0.000031237905,"threshold_uncertainty_score":0.999986},"labels":[],"label_agreement":null},{"id":"W1976009969","doi":"10.1002/cjs.10049","title":"An adaptive sampling scheme guided by BART—with an application to predict processor performance","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Sampling (signal processing); Adaptive sampling; Bayesian probability; Adaptive design; Scheme (mathematics); Machine learning; Data mining; Computer engineering; Artificial intelligence; Statistics; Monte Carlo method; Mathematics","score_opus":0.01589734367511268,"score_gpt":0.26326589340128803,"score_spread":0.24736854972617536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976009969","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":[0.014270039,0.000009959726,0.9850146,0.000106845684,0.0001540799,0.00020284187,0.00016531284,0.000018938592,0.00005735728],"genre_scores_gemma":[0.25913265,0.0000024255394,0.7405583,0.00016878062,0.00007915871,0.0000082703045,0.000014516022,0.000015639156,0.000020259165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890304,0.000022552767,0.000300428,0.00023754846,0.00024891234,0.0002875101],"domain_scores_gemma":[0.9970657,0.000039297924,0.00026603168,0.0003108742,0.0012611334,0.0010569657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023092378,0.00013638688,0.00015025679,0.00019943155,0.00021676449,0.00015104775,0.0006645544,0.00005024039,0.000013157789],"category_scores_gemma":[0.0001348761,0.00012679574,0.000010593803,0.0003607337,0.00007512016,0.0010399473,0.000012240336,0.00029602693,0.000005779039],"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":[0.00021091582,0.00030742248,0.01714993,0.000081633,0.00012931677,0.00016583045,0.0079097375,0.36096942,0.014571746,0.042935565,0.0053564194,0.550212],"study_design_scores_gemma":[0.0005567745,0.0012972358,0.0057286243,0.00003180992,0.000011628224,0.0001875406,0.00013255342,0.9857433,0.0016073815,0.000645823,0.0037427854,0.0003145524],"about_ca_topic_score_codex":0.00024898714,"about_ca_topic_score_gemma":0.004260412,"teacher_disagreement_score":0.62477386,"about_ca_system_score_codex":0.0001283157,"about_ca_system_score_gemma":0.0011655813,"threshold_uncertainty_score":0.517058},"labels":[],"label_agreement":null},{"id":"W1977715424","doi":"10.1198/004017006000000228","title":"Variable Selection for Gaussian Process Models in Computer Experiments","year":2006,"lang":"en","type":"article","venue":"Technometrics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":173,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Los Alamos National Laboratory; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Frequentist inference; Computer science; Computer experiment; Process (computing); Gaussian process; Selection (genetic algorithm); Variable (mathematics); Bayesian probability; Machine learning; Gaussian; Data mining; Artificial intelligence; Algorithm; Bayesian inference; Simulation; Mathematics","score_opus":0.020139867867623826,"score_gpt":0.28212035035974664,"score_spread":0.2619804824921228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977715424","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":[0.0003852903,0.000072817435,0.997586,0.00003702585,0.00014043487,0.00052792905,0.0000035349274,0.00042141217,0.00082551443],"genre_scores_gemma":[0.15719722,0.0000020818457,0.84241897,0.000042278865,0.000042650794,0.00016102826,0.0000071767504,0.000015941243,0.000112675596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987929,0.000009963838,0.00025055662,0.00043092784,0.00021699358,0.0002986598],"domain_scores_gemma":[0.99937004,0.00005694338,0.000107245345,0.00023889882,0.00019134805,0.00003555013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001714888,0.00013819,0.00015481788,0.0012530086,0.000093038536,0.00008655318,0.00045046967,0.000096344476,0.0000027522553],"category_scores_gemma":[0.000038566206,0.00014759277,0.00002697536,0.006548614,0.000018955057,0.0008364788,0.00008996695,0.000103075945,0.0000032818073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041775183,0.00028373982,0.00049849437,0.000017750639,0.0000036874114,9.532232e-7,0.00006645357,0.8445214,0.00013565223,0.13383776,0.0000952327,0.02053467],"study_design_scores_gemma":[0.0005781932,0.000057192323,0.00012346469,0.000008232011,0.0000012833362,0.0000034298937,0.0000060090906,0.915687,0.0033131335,0.079876564,0.0001779946,0.00016749173],"about_ca_topic_score_codex":0.00003034848,"about_ca_topic_score_gemma":0.0000030165718,"teacher_disagreement_score":0.15681192,"about_ca_system_score_codex":0.00023861119,"about_ca_system_score_gemma":0.000049811548,"threshold_uncertainty_score":0.6018658},"labels":[],"label_agreement":null},{"id":"W1977899896","doi":"10.1007/s11590-013-0688-4","title":"A variance-based method to rank input variables of the Mesh Adaptive Direct Search algorithm","year":2013,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Algorithm; Variance (accounting); Mathematical optimization; Rank (graph theory); Mathematics; Function (biology); Divergence (linguistics); Computational intelligence; Computer science; Artificial intelligence","score_opus":0.012320311983936696,"score_gpt":0.25321201544526784,"score_spread":0.24089170346133115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977899896","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":[0.000012212942,0.000011369566,0.9907425,0.0067467405,0.0003368526,0.0011428072,0.00001686695,0.0001558861,0.0008347566],"genre_scores_gemma":[0.0018435881,0.000004141057,0.9918812,0.0057319878,0.000056248973,0.0001915058,0.000007949321,0.00003576869,0.000247609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973146,0.0006000544,0.00042404857,0.0006547432,0.0005946286,0.00041196778],"domain_scores_gemma":[0.9976881,0.00041786188,0.00023544593,0.00085434096,0.0006510319,0.00015320748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005281156,0.00026838068,0.00032240548,0.00027323485,0.00023376915,0.00016282756,0.0011189034,0.000085541295,0.00014902391],"category_scores_gemma":[0.00017289052,0.0002190563,0.00011686165,0.0020787735,0.00009987484,0.00085826806,0.00031727724,0.00019297973,0.000032577347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007017637,0.00005610789,0.000022429675,0.000005844693,0.000032040854,0.0000012647345,0.0003746543,0.978817,0.0012354203,0.0007176812,0.0007076225,0.018022904],"study_design_scores_gemma":[0.0006985439,0.000047251793,0.00019535793,0.000039419807,0.000011966229,0.0000032474122,0.000025308436,0.99000335,0.008413416,0.0000658188,0.0002409763,0.00025535622],"about_ca_topic_score_codex":0.0001437789,"about_ca_topic_score_gemma":0.0000010398295,"teacher_disagreement_score":0.017767547,"about_ca_system_score_codex":0.0001747671,"about_ca_system_score_gemma":0.0001658541,"threshold_uncertainty_score":0.89328563},"labels":[],"label_agreement":null},{"id":"W1978178229","doi":"10.2514/6.2013-4620","title":"Flight trajectories optimization under the influence of winds using genetic algorithms","year":2013,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation, and Control (GNC) Conference","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Genetic algorithm; Computer science; Algorithm; Machine learning","score_opus":0.010472018219662713,"score_gpt":0.23856844350328482,"score_spread":0.22809642528362212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978178229","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":[0.06866316,0.00055471406,0.9290845,0.0005211278,0.00021988842,0.00071378984,0.000009109866,0.00009701787,0.00013669337],"genre_scores_gemma":[0.8139782,0.00010637818,0.18525968,0.00037112107,0.000055861106,0.00010350052,0.0000060664665,0.000018497618,0.00010066645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977327,0.0001946023,0.0006855405,0.00059426104,0.0004181909,0.00037470766],"domain_scores_gemma":[0.9967562,0.00025311255,0.00047616396,0.0006440223,0.0017577228,0.000112803886],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024970208,0.0003178651,0.00037548528,0.00012694739,0.00043138172,0.00033286758,0.0007593858,0.00012966122,0.000023256503],"category_scores_gemma":[0.00009525451,0.00025603693,0.000063222375,0.0008384308,0.00048010345,0.0014971269,0.00011689541,0.00018820068,0.0000069432735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009364435,0.000043086235,0.002581523,0.000022053197,0.000056353863,0.0000013658606,0.0008515476,0.9678247,0.0037999596,0.018335301,0.000014792401,0.0064599635],"study_design_scores_gemma":[0.0011967276,0.000064221975,0.07143407,0.0001084171,0.000034374814,0.000017984643,0.00020765523,0.9189947,0.0011102239,0.006454048,0.00007818191,0.00029935423],"about_ca_topic_score_codex":0.00036297203,"about_ca_topic_score_gemma":0.000009454442,"teacher_disagreement_score":0.7453151,"about_ca_system_score_codex":0.000072190785,"about_ca_system_score_gemma":0.00027685933,"threshold_uncertainty_score":0.9999892},"labels":[],"label_agreement":null},{"id":"W1978266779","doi":"10.3758/bf03212150","title":"Evaluating the importance of the convex hull in solving the Euclidean version of the traveling salesperson problem: Reply to Lee and Vickers (2000)","year":2000,"lang":"en","type":"article","venue":"Perception & Psychophysics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Convex hull; Mathematics; Regular polygon; Euclidean geometry; Artifact (error); Point (geometry); Stimulus (psychology); Combinatorics; Computer science; Algorithm; Artificial intelligence; Psychology; Cognitive psychology; Geometry","score_opus":0.022654714745276194,"score_gpt":0.31189016500445216,"score_spread":0.28923545025917596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978266779","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":[0.92656195,0.000049342194,0.06666908,0.004706028,0.00023482248,0.001048105,0.000005165722,0.000030110556,0.00069540384],"genre_scores_gemma":[0.95138705,0.00006891401,0.046447117,0.0017331451,0.00006498369,0.00002891804,9.100153e-7,0.000018879302,0.00025009477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982617,0.00025981432,0.00039571032,0.0003944793,0.00047590898,0.00021240437],"domain_scores_gemma":[0.99862444,0.00016697487,0.000283289,0.0007234283,0.00016918566,0.000032665317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007176942,0.00015723007,0.00016509187,0.000030421075,0.00034127728,0.000048141705,0.0008015613,0.000039287654,0.000038317616],"category_scores_gemma":[0.00007780374,0.00008464656,0.000092726776,0.0009088281,0.0002017921,0.00028820414,0.00013182123,0.00025668397,0.000008359263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016533972,0.00021542773,0.011327474,0.000062396575,0.000040619394,5.193842e-7,0.12198211,0.39478058,0.10336231,0.0007651803,0.00035187078,0.3669462],"study_design_scores_gemma":[0.0012939649,0.00017751397,0.19469933,0.00032424417,0.000029721372,0.000008645797,0.004597824,0.7940083,0.0010958271,0.003107374,0.0003490862,0.00030817263],"about_ca_topic_score_codex":0.00007667026,"about_ca_topic_score_gemma":0.000034423498,"teacher_disagreement_score":0.39922774,"about_ca_system_score_codex":0.00010484107,"about_ca_system_score_gemma":0.00007975888,"threshold_uncertainty_score":0.34517866},"labels":[],"label_agreement":null},{"id":"W1980157939","doi":"10.2514/6.2012-5556","title":"On the Application of Differential Geometry to MDO","year":2012,"lang":"en","type":"article","venue":"12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Cambridge Trust; Cambridge Commonwealth Trust","keywords":"Differential (mechanical device); Geometry; Computer science; Mathematics; Engineering; Aerospace engineering","score_opus":0.012444671382451413,"score_gpt":0.2731564944199342,"score_spread":0.2607118230374828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980157939","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":[0.041397188,0.00011160605,0.9527178,0.004235862,0.00010172934,0.0010666759,0.000051416428,0.00015398131,0.00016375091],"genre_scores_gemma":[0.8367576,0.00035759923,0.16188993,0.0001463828,0.000039439903,0.00038812164,0.00021190871,0.000015364207,0.00019365511],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974454,0.00018398267,0.00080925383,0.0008145726,0.00037416682,0.0003726112],"domain_scores_gemma":[0.99693036,0.00025989427,0.00038000895,0.0007307797,0.001470946,0.00022802455],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048747813,0.00042437823,0.0005250237,0.0014199017,0.0010964671,0.00042544844,0.000492306,0.00028350254,0.00014850344],"category_scores_gemma":[0.00048553088,0.00033310367,0.00008437451,0.0030264081,0.0003713133,0.0015615007,0.00031525412,0.0003198892,0.000009153457],"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":[0.000039323382,0.00043872974,0.013830631,0.000027908854,0.000276118,4.3528314e-7,0.0046146726,0.11777029,0.0029399188,0.8169636,0.000043625445,0.04305473],"study_design_scores_gemma":[0.00047495548,0.00019586501,0.016931122,0.000046402918,0.00017937634,0.0000053400145,0.0014177944,0.9738438,0.0038882517,0.002557841,0.000049797432,0.0004094171],"about_ca_topic_score_codex":0.000089716305,"about_ca_topic_score_gemma":0.00022794402,"teacher_disagreement_score":0.85607356,"about_ca_system_score_codex":0.00006145528,"about_ca_system_score_gemma":0.0001236742,"threshold_uncertainty_score":0.9999121},"labels":[],"label_agreement":null},{"id":"W1980201624","doi":"10.1016/j.ejor.2007.12.014","title":"An exact -constraint method for bi-objective combinatorial optimization problems: Application to the Traveling Salesman Problem with Profits","year":2007,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":363,"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 de Montréal; Université du Québec à Montréal","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Travelling salesman problem; Mathematical optimization; Heuristics; Combinatorial optimization; 2-opt; Traveling purchaser problem; Vertex (graph theory); Mathematics; Multi-objective optimization; Bottleneck traveling salesman problem; Pareto principle; Constraint (computer-aided design); Computer science; Combinatorics; Graph","score_opus":0.04206008863009993,"score_gpt":0.3738648753142147,"score_spread":0.3318047866841148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980201624","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":[0.00035133387,0.000019753055,0.99470997,0.0015825181,0.0001503182,0.0019714485,0.0000067578117,0.000031397038,0.001176505],"genre_scores_gemma":[0.16591896,0.000005658586,0.83330715,0.00012257767,0.00049223175,0.0000539005,0.000012676767,0.000037230468,0.00004962219],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960355,0.0012209583,0.0006619585,0.00045515955,0.0012040177,0.00042239914],"domain_scores_gemma":[0.9932691,0.0006651412,0.00030163885,0.00035490407,0.0051406063,0.0002686247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014928142,0.00017655661,0.00019138138,0.00039146308,0.0007112113,0.00044407923,0.0011086032,0.00003462703,0.000006011826],"category_scores_gemma":[0.0005508286,0.00012084092,0.000055843488,0.0011285825,0.00010597519,0.0010900528,0.000104964456,0.00051203073,0.000009254066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021233698,0.0001767554,0.00002817423,0.000009210233,0.000034947112,0.000012470931,0.002242816,0.9271317,0.0030330385,0.026084041,0.00004688592,0.040987585],"study_design_scores_gemma":[0.0026900708,0.003175122,0.0012231707,0.0001077121,0.000012299306,0.00024942408,0.00072531286,0.98191166,0.0057407133,0.0010448367,0.0028080386,0.0003116186],"about_ca_topic_score_codex":0.000004599754,"about_ca_topic_score_gemma":0.0000069669686,"teacher_disagreement_score":0.16556764,"about_ca_system_score_codex":0.00026198788,"about_ca_system_score_gemma":0.000519883,"threshold_uncertainty_score":0.5470134},"labels":[],"label_agreement":null},{"id":"W1980754808","doi":"10.1016/s0895-7177(02)00047-x","title":"Balance space in airport construction: Application to the North Sea island option for Schiphol Airport","year":2002,"lang":"en","type":"article","venue":"Mathematical and Computer Modelling","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Meteorology; Space (punctuation); Balance (ability); Environmental science; Geography; Computer science","score_opus":0.019304876474895587,"score_gpt":0.22621137701853405,"score_spread":0.20690650054363846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980754808","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":[0.0050158235,0.000035104862,0.99281055,0.0011040627,0.00009243207,0.00072512636,0.0000035226342,0.00008394277,0.00012945208],"genre_scores_gemma":[0.13154984,0.000020489813,0.8678506,0.00021950887,0.00011220303,0.00014963489,0.0000036647402,0.000010363841,0.000083705614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988684,0.000022058823,0.00028450048,0.00042742022,0.00017877712,0.00021882061],"domain_scores_gemma":[0.99929273,0.00010410418,0.0000864458,0.00033268132,0.00009926255,0.00008477084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016676933,0.00014894176,0.00019000306,0.000072964365,0.0001629174,0.00011300632,0.0002792755,0.000044115342,0.000003312373],"category_scores_gemma":[0.00000850503,0.00011620056,0.00003801765,0.00030469336,0.00004247277,0.00027615696,0.00011577483,0.00011262642,0.000025735095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042169518,0.00006645055,0.00046560352,0.000031405387,0.0000060628768,0.0000013876925,0.00070066977,0.89036334,0.0000017549617,0.08588466,0.00005070319,0.022423744],"study_design_scores_gemma":[0.000300006,0.000035403897,0.00011126578,0.00002937135,0.0000036825704,0.000036276688,0.000011095896,0.9815618,0.000014789913,0.017122451,0.00062498817,0.00014887258],"about_ca_topic_score_codex":0.0000028415056,"about_ca_topic_score_gemma":0.000005930909,"teacher_disagreement_score":0.12653401,"about_ca_system_score_codex":0.00003218934,"about_ca_system_score_gemma":0.000008867296,"threshold_uncertainty_score":0.47385213},"labels":[],"label_agreement":null},{"id":"W1983072446","doi":"10.1177/1548512914547798","title":"A comparison of heuristics applied to the sensor deployment problem in two dimensions","year":2014,"lang":"en","type":"article","venue":"The Journal of Defense Modeling and Simulation Applications Methodology Technology","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Royal Military College of Canada; Royal Ottawa Mental Health Centre; St. Lawrence College","funders":"","keywords":"Heuristics; Differential evolution; Swap (finance); Metaheuristic; Software deployment; Computer science; Genetic algorithm; Mathematical optimization; Simulated annealing; Algorithm; Mathematics; Machine learning","score_opus":0.08073492339101117,"score_gpt":0.39973838490997843,"score_spread":0.31900346151896725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983072446","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":[0.018321399,0.00014413582,0.9781087,0.0028203002,0.000051394058,0.00047367153,8.1841586e-7,0.00004386431,0.00003568475],"genre_scores_gemma":[0.5076767,0.000016780603,0.4921721,0.000091480695,0.000014043444,0.000020397763,2.5488575e-7,0.000005558141,0.000002699533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982411,0.0005195887,0.00073155283,0.00018887792,0.00015451606,0.00016433295],"domain_scores_gemma":[0.9964957,0.002033801,0.00050297356,0.00050393614,0.00041996947,0.000043603934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023695931,0.00011823803,0.0003545885,0.0004537347,0.00019020237,0.000010754922,0.0005201617,0.00008290451,5.2327556e-7],"category_scores_gemma":[0.0005130425,0.00007780054,0.00003173156,0.000846634,0.00012615504,0.000048116253,0.00019303666,0.0003513708,0.0000019773113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024535377,0.000052004885,0.00019684683,0.0000028575291,0.000012579229,1.129677e-7,0.0008281493,0.93136966,0.0012582418,0.043654922,0.0000025762017,0.022597535],"study_design_scores_gemma":[0.00044413144,0.000073018804,0.000033655997,0.0000093664885,0.00002725642,0.000031085634,0.00036270832,0.9545168,0.0006447882,0.04342509,0.00036054038,0.00007152114],"about_ca_topic_score_codex":0.0000054323204,"about_ca_topic_score_gemma":0.000009828883,"teacher_disagreement_score":0.4893553,"about_ca_system_score_codex":0.00003592304,"about_ca_system_score_gemma":0.000032900676,"threshold_uncertainty_score":0.3172614},"labels":[],"label_agreement":null},{"id":"W1985753777","doi":"10.1007/s11081-008-9043-5","title":"Editorial—surrogate modeling and space mapping for engineering optimization","year":2008,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Surrogate model; Computer science; Financial engineering; Multidisciplinary design optimization; Engineering optimization; Systems engineering; Exploit; Optimization problem; Multidisciplinary approach; Kriging; Industrial engineering; Uncertainty quantification; Machine learning; Engineering","score_opus":0.01166669951559834,"score_gpt":0.20358950946015764,"score_spread":0.1919228099445593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985753777","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":[0.0001621808,0.00016501908,0.99453247,0.000108628024,0.00429612,0.000295716,0.0000029062924,0.00040624588,0.000030714586],"genre_scores_gemma":[0.00798536,0.0005667561,0.98970276,0.000023567614,0.0015663801,0.000052056388,0.000016228501,0.00004267087,0.000044190572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989005,0.000009466521,0.000245372,0.0004083071,0.00015730507,0.00027907832],"domain_scores_gemma":[0.9993278,0.00008362613,0.00006547947,0.00018297978,0.00020348755,0.00013662729],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014882509,0.00021963565,0.00019928203,0.00024270493,0.0002209779,0.00011639836,0.00013578216,0.00010515996,0.0000022814277],"category_scores_gemma":[0.0002000066,0.0002565137,0.000032180506,0.00036749075,0.000015443391,0.0009842025,0.00010040929,0.000110623536,5.07461e-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":[0.0000023484986,0.0000074995983,0.000010616829,0.000030179108,0.000011168623,9.607338e-7,0.00032995385,0.9984209,0.000118751566,0.0007678244,0.000062886094,0.00023691094],"study_design_scores_gemma":[0.0006639495,0.000019784404,0.000007168391,0.000035365585,0.000005010553,0.000026232434,0.00001829142,0.99770117,0.00006874499,0.000007513832,0.0011635349,0.00028320984],"about_ca_topic_score_codex":0.0000030958831,"about_ca_topic_score_gemma":1.0846044e-7,"teacher_disagreement_score":0.0078231795,"about_ca_system_score_codex":0.000054723936,"about_ca_system_score_gemma":0.000024854493,"threshold_uncertainty_score":0.99998873},"labels":[],"label_agreement":null},{"id":"W1987521740","doi":"10.1243/09544054jem1472","title":"Sequential metamodelling application to improve porthole die design","year":2009,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Metamodeling; Kriging; Design space exploration; Set (abstract data type); Computer science; Black box; Differential evolution; Conceptual design; Engineering design process; Differential (mechanical device); Mathematical optimization; Algorithm; Data mining; Engineering; Machine learning; Artificial intelligence; Mathematics; Mechanical engineering","score_opus":0.010981765125858276,"score_gpt":0.2259059312534517,"score_spread":0.21492416612759344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987521740","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":[0.0035098584,0.00007719744,0.9950013,0.00043507747,0.0005875909,0.00030993426,0.0000025969255,0.000050270744,0.000026144313],"genre_scores_gemma":[0.6059936,0.00002073761,0.3938343,0.00004295619,0.00008752942,0.000003976648,2.7282078e-7,0.000008277749,0.000008358006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99841744,0.0000069839443,0.000655806,0.00021171138,0.0004979678,0.00021010687],"domain_scores_gemma":[0.99848586,0.000040063307,0.00055719505,0.0001729956,0.00060263544,0.00014123485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058710785,0.00020380173,0.00035485168,0.0002366743,0.000050681665,0.000031523294,0.0008502661,0.000113313894,0.0000013087017],"category_scores_gemma":[0.00036097964,0.00015743606,0.0001912191,0.00040521674,0.000021156711,0.0006801025,0.000086714055,0.0003455134,6.862376e-7],"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":[0.000022133014,0.000040803337,3.5001176e-7,0.000024139432,0.000031679152,8.4521895e-7,0.00009193322,0.8549321,0.124558754,0.017382829,0.000022656532,0.0028917764],"study_design_scores_gemma":[0.00029033172,0.00016241705,0.000018018429,0.000094156894,0.000030250872,0.000026307795,0.000010406824,0.464328,0.53341806,0.0010414579,0.00045858655,0.00012199793],"about_ca_topic_score_codex":7.8713225e-7,"about_ca_topic_score_gemma":3.9194973e-8,"teacher_disagreement_score":0.60248375,"about_ca_system_score_codex":0.00010826495,"about_ca_system_score_gemma":0.00006076766,"threshold_uncertainty_score":0.6420057},"labels":[],"label_agreement":null},{"id":"W1988999677","doi":"10.1061/(asce)0733-9364(2003)129:6(706.3)","title":"Discussion of “Incorporating Practicability into Genetic Algorithm-Based Time-Cost Optimization” by Bryan Christopher Que","year":2003,"lang":"en","type":"article","venue":"Journal of Construction Engineering and Management","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Genetic algorithm; Operations research; Mathematical optimization; Mathematics; Machine learning","score_opus":0.0029201399319309885,"score_gpt":0.20517314820778343,"score_spread":0.20225300827585244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988999677","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":[0.00025692643,0.00010670949,0.9988576,0.0001617957,0.00031000545,0.00017692124,0.0000011268984,0.000029530072,0.000099372584],"genre_scores_gemma":[0.0117586125,0.000073855,0.98806554,0.000018360062,0.00001829782,0.0000059062763,0.0000011570929,0.000010284493,0.000048007794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898034,0.000063403444,0.00044291446,0.00017283046,0.0002331188,0.00010737386],"domain_scores_gemma":[0.99908155,0.00004174706,0.00040148452,0.0001730533,0.00021478864,0.0000873672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031270873,0.00012403705,0.00018440036,0.00018068586,0.00006512877,0.000048773538,0.00012789482,0.00003726061,0.000011894106],"category_scores_gemma":[0.00010156048,0.00010114643,0.000045608012,0.00029940452,0.000051518207,0.00042704603,0.000035749214,0.00011188523,4.6641617e-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":[0.0000034237423,0.000046721478,0.000047810518,0.00004054588,0.000028685285,0.0000050402004,0.00003773157,0.93475133,0.00022510515,0.00086784316,0.000025436731,0.0639203],"study_design_scores_gemma":[0.00069112616,0.00006630735,0.00014464377,0.000057786783,0.00002216314,0.0000822787,0.000054550877,0.99594516,0.0011638214,0.00016786967,0.0014797022,0.00012461639],"about_ca_topic_score_codex":0.0000018773421,"about_ca_topic_score_gemma":8.0529446e-8,"teacher_disagreement_score":0.06379568,"about_ca_system_score_codex":0.00009069919,"about_ca_system_score_gemma":0.00004146929,"threshold_uncertainty_score":0.41246316},"labels":[],"label_agreement":null},{"id":"W1989062954","doi":"10.1115/detc2011-48227","title":"A Rational Design Approach to Gaussian Process Modeling for Variable Fidelity Models","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Surrogate model; Computer science; Kriging; Variable (mathematics); Metric (unit); Gaussian process; Process (computing); Sampling (signal processing); Engineering design process; Mathematical optimization; Set (abstract data type); Data mining; Machine learning; Gaussian; Mathematics","score_opus":0.1235986684308437,"score_gpt":0.29333148559937405,"score_spread":0.16973281716853034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989062954","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":[0.0000061907854,0.0000063902135,0.98074687,0.000049505037,0.00007650448,0.0010427434,0.0000044351846,0.00024275569,0.017824603],"genre_scores_gemma":[0.05218257,6.444984e-7,0.946532,0.0004268741,0.0000305918,0.000415563,0.000006157698,0.000016540316,0.00038906586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854624,0.000039692997,0.00027163138,0.0006042013,0.00022813075,0.00031009119],"domain_scores_gemma":[0.99879104,0.00004883961,0.000059276554,0.0003717084,0.0005747368,0.00015437177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004098153,0.0001599846,0.00015614327,0.00010831074,0.00018664826,0.00008251299,0.0006131061,0.00005957933,0.00001401869],"category_scores_gemma":[0.00008700224,0.00014571144,0.000037177368,0.00046274267,0.000013813467,0.0014621684,0.00010484398,0.00006852605,0.000007781211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016489887,0.000104378756,6.956583e-7,0.0000075131393,0.000006703353,1.19129545e-7,0.0009956931,0.81498766,0.00002389177,0.1831819,0.0000379958,0.00063698215],"study_design_scores_gemma":[0.00029217856,0.00003856899,0.000001491087,0.000004385034,0.000002950752,0.0000034122847,0.00006128512,0.8528495,0.00059821765,0.14595905,0.0000072869707,0.00018171388],"about_ca_topic_score_codex":0.000021061956,"about_ca_topic_score_gemma":7.350307e-7,"teacher_disagreement_score":0.052176382,"about_ca_system_score_codex":0.00006443359,"about_ca_system_score_gemma":0.00020896908,"threshold_uncertainty_score":0.594194},"labels":[],"label_agreement":null},{"id":"W1989099368","doi":"10.1145/1254882.1254904","title":"Synthetic designs","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Design of experiments; Reliability engineering; Software; Sample size determination; Sample (material); Engineering; Statistics; Mathematics","score_opus":0.023705481332219582,"score_gpt":0.2839585832470893,"score_spread":0.2602531019148697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989099368","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":[0.00006775866,0.000014942103,0.9630418,0.0001382208,0.00017429261,0.000080537946,1.1422055e-7,0.00029557126,0.036186807],"genre_scores_gemma":[0.1115382,0.0000024154103,0.88613003,0.00033335434,0.000020703572,0.0000022783497,2.0352492e-7,0.000005661063,0.0019671496],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993044,0.00001399263,0.00011266221,0.00023012258,0.00013409922,0.00020469706],"domain_scores_gemma":[0.9993832,0.00011308207,0.000030096262,0.00031925316,0.00007678836,0.00007754924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002458301,0.00006855405,0.000059168888,0.00008136413,0.00006365737,0.00003680703,0.00034677377,0.000025679945,0.000058243317],"category_scores_gemma":[0.000062179366,0.000060291717,0.000023548928,0.00032646838,0.00002526469,0.00032111868,0.00008492845,0.00004910706,0.00019318725],"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":[0.0000069814246,0.0001743955,0.0002912691,0.0000042714955,0.00001636792,0.000084058076,0.00068286306,0.01367776,0.003712015,0.47994798,0.0004500105,0.500952],"study_design_scores_gemma":[0.0007555881,0.000114859446,0.0035127013,0.000011659688,0.000004204452,0.000107366126,0.000118099015,0.87880784,0.09399529,0.012099891,0.009882826,0.0005896648],"about_ca_topic_score_codex":0.0000032706687,"about_ca_topic_score_gemma":0.0000034894883,"teacher_disagreement_score":0.86513007,"about_ca_system_score_codex":0.000040159834,"about_ca_system_score_gemma":0.00001816141,"threshold_uncertainty_score":0.24830958},"labels":[],"label_agreement":null},{"id":"W1989286986","doi":"10.3182/20110828-6-it-1002.01726","title":"Casting Design Through Multi-Objective Optimization","year":2011,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flexibility (engineering); Sensitivity (control systems); Mathematical optimization; Multi-objective optimization; Casting; Engineering; Process (computing); Key (lock); Evolutionary algorithm; Computer science; Optimization problem; Mathematics","score_opus":0.06921899410958726,"score_gpt":0.26904380160671343,"score_spread":0.19982480749712617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989286986","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":[0.00018986786,0.000093033756,0.9913094,0.00004835964,0.0003564953,0.0006013303,0.0000022722454,0.0008645312,0.006534677],"genre_scores_gemma":[0.047800872,0.000034942663,0.9511886,0.00016967632,0.00007099963,0.000102287726,0.0000018818737,0.000047696536,0.0005830166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978558,0.000031171563,0.0003821908,0.00085464504,0.0003355143,0.00054066756],"domain_scores_gemma":[0.9983229,0.000065472355,0.00034088045,0.00024361441,0.0008982496,0.00012888608],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002860797,0.00033686994,0.0002810336,0.0001795969,0.00038639473,0.00020558495,0.00080997095,0.00013654135,0.000051102987],"category_scores_gemma":[0.0004958621,0.00034661428,0.00007814824,0.0009852289,0.00012235391,0.0037362033,0.0003262333,0.00023524989,0.000070041795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024585397,0.0021598192,0.01361212,0.00022029027,0.0005027319,0.0001363567,0.20170179,0.59916157,0.0040531266,0.05993589,0.0015030435,0.116767414],"study_design_scores_gemma":[0.00070281484,0.00013917498,0.00074957026,0.000039287246,0.00001343327,0.000054524593,0.00067494495,0.9837152,0.011867565,0.0014984539,0.000086890555,0.00045819237],"about_ca_topic_score_codex":0.000060489834,"about_ca_topic_score_gemma":0.0000015482212,"teacher_disagreement_score":0.38455358,"about_ca_system_score_codex":0.00017886498,"about_ca_system_score_gemma":0.000089940564,"threshold_uncertainty_score":0.9998986},"labels":[],"label_agreement":null},{"id":"W1990098741","doi":"10.1115/1.4003035","title":"Metamodel-Based Optimization for Problems With Expensive Objective and Constraint Functions","year":2011,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; Simon Fraser University","funders":"","keywords":"Metamodeling; Mathematical optimization; Constraint (computer-aided design); Computer science; Sample (material); Sampling (signal processing); Discriminative model; Function (biology); Mathematics; Machine learning","score_opus":0.05938132704092378,"score_gpt":0.2478318437322162,"score_spread":0.18845051669129242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990098741","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":[0.000029048575,0.000053970653,0.9988843,0.00013895199,0.00017564053,0.00061456993,0.0000029895832,0.000035901863,0.00006459939],"genre_scores_gemma":[0.06819209,0.000011457615,0.9315387,0.00015324865,0.000029480263,0.00004136759,7.0819124e-7,0.000016061567,0.000016860313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987935,0.00014892212,0.00037219646,0.00026014546,0.00023829889,0.00018696838],"domain_scores_gemma":[0.99758965,0.00036012338,0.00046941277,0.00017195732,0.0012463052,0.0001625609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005428465,0.00015799546,0.000280175,0.0001741315,0.00012381398,0.00005341773,0.00023955909,0.000072870454,0.00001774162],"category_scores_gemma":[0.0002564031,0.00011775048,0.00007589071,0.00025549665,0.00007318339,0.00067150674,0.000032791464,0.0001630007,8.477955e-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":[0.00030253554,0.00019787265,0.000002579295,0.000010040074,0.00010452567,0.000017475113,0.0004828424,0.98364604,0.0006652547,0.008824653,0.0000379446,0.005708232],"study_design_scores_gemma":[0.0019612354,0.0018459244,0.000010044063,0.000047576214,0.000057135054,0.00016357853,0.00016280392,0.9813774,0.008373779,0.005824547,0.00001569402,0.00016023929],"about_ca_topic_score_codex":0.0000017164937,"about_ca_topic_score_gemma":8.871731e-7,"teacher_disagreement_score":0.06816304,"about_ca_system_score_codex":0.000075091244,"about_ca_system_score_gemma":0.0002382056,"threshold_uncertainty_score":0.48017254},"labels":[],"label_agreement":null},{"id":"W1990178974","doi":"10.1049/iet-smt.2014.0204","title":"Competitive co‐evolutionary algorithm for constrained robust design","year":2015,"lang":"en","type":"article","venue":"IET Science Measurement & Technology","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Evolutionary algorithm; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.09124898709972785,"score_gpt":0.2974161950569839,"score_spread":0.20616720795725607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990178974","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":[0.0000131931365,0.00014593455,0.9932265,0.0018861,0.00068862876,0.0011468655,0.000014393712,0.00082605466,0.0020522794],"genre_scores_gemma":[0.04648923,0.000007283709,0.9529119,0.0002045829,0.000043331624,0.00026572237,0.00000316783,0.000015489195,0.00005929113],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963876,0.000079051715,0.00037154442,0.0010282433,0.0013408995,0.0007926473],"domain_scores_gemma":[0.9955257,0.00008367734,0.00022777803,0.0007236746,0.003183795,0.0002553465],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0025936265,0.00027115928,0.00029816502,0.0008457333,0.0005269179,0.000119565906,0.002038548,0.00014736653,0.0000053037443],"category_scores_gemma":[0.0012138832,0.00026875883,0.00005735085,0.0025953217,0.0020444298,0.0010584996,0.0003191412,0.00021520858,0.000048545648],"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":[0.00005576321,0.0010277464,0.00025825854,0.000016950831,0.00010212263,0.00006436053,0.0012355648,0.10118586,0.027905324,0.48123792,0.004210564,0.38269955],"study_design_scores_gemma":[0.0020396465,0.0006639171,0.00009282305,0.000031799733,0.000010244027,0.00011616385,0.00074410706,0.93886745,0.028713908,0.023165908,0.0050680046,0.00048604855],"about_ca_topic_score_codex":0.0000039986985,"about_ca_topic_score_gemma":0.0000029300386,"teacher_disagreement_score":0.8376816,"about_ca_system_score_codex":0.0012522826,"about_ca_system_score_gemma":0.001704391,"threshold_uncertainty_score":0.99997646},"labels":[],"label_agreement":null},{"id":"W1991019969","doi":"10.1115/1.4024470","title":"Maximizing Design Confidence in Sequential Simulation-Based Optimization","year":2013,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Process (computing); Bayesian optimization; Engineering design process; Task (project management); Mathematical optimization; Surrogate model; Cantilever; Design process; Simulation modeling; Machine learning; Engineering; Work in process; Mathematics; Systems engineering","score_opus":0.052987215292829175,"score_gpt":0.2932323241740303,"score_spread":0.2402451088812011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991019969","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":[0.000025415142,0.0000398366,0.99862707,0.00042331463,0.00035364676,0.00046800022,2.735653e-7,0.00004492612,0.000017533268],"genre_scores_gemma":[0.2495509,0.000010894468,0.7500867,0.0002654927,0.000048120164,0.000011187194,3.4799345e-7,0.00001405038,0.0000122811125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974763,0.0006237946,0.00079506234,0.00027565125,0.00054525153,0.00028397117],"domain_scores_gemma":[0.99653417,0.001601876,0.00059882936,0.00026696004,0.0008246319,0.0001735102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012741494,0.00017900449,0.00031013036,0.00035117212,0.000078060184,0.00019757937,0.00070137565,0.00012171448,0.00013637726],"category_scores_gemma":[0.0013368241,0.00016344828,0.00009352157,0.0006447004,0.000029604285,0.0016569485,0.00006310365,0.00030820764,0.000024434567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037163445,0.00008411741,0.0000026484881,0.0000032250884,0.000009058026,0.000034641147,0.000050545288,0.99193645,0.0020334579,0.0011463639,0.00002394032,0.004638367],"study_design_scores_gemma":[0.0012369648,0.00025307902,0.00001849589,0.000066328554,0.000007487109,0.000021197071,0.000014401139,0.9821091,0.008428351,0.007665164,0.00000658775,0.00017280228],"about_ca_topic_score_codex":0.000008006106,"about_ca_topic_score_gemma":3.4778387e-7,"teacher_disagreement_score":0.24952547,"about_ca_system_score_codex":0.0002180827,"about_ca_system_score_gemma":0.00029741443,"threshold_uncertainty_score":0.6665227},"labels":[],"label_agreement":null},{"id":"W1993699294","doi":"10.1108/03321640910959071","title":"Tolerance and multiobjective optimization in electromagnetic devices","year":2009,"lang":"en","type":"article","venue":"COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematical optimization; Robustness (evolution); Computer science; Originality; Representation (politics); Algorithm; Multi-objective optimization; Reduction (mathematics); Engineering design process; Mathematics; Engineering","score_opus":0.007204713441442482,"score_gpt":0.2622723122052971,"score_spread":0.25506759876385465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993699294","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":[0.025398508,0.0010979148,0.9721316,0.000999226,0.00009036784,0.00022553951,3.5824638e-7,0.000028802255,0.000027682201],"genre_scores_gemma":[0.6722997,0.00075362815,0.32666793,0.00020026903,0.00004394823,0.000011589698,0.0000016604774,0.000008074297,0.000013233125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907845,0.000021951262,0.00029224972,0.00019255895,0.0001642472,0.00025054804],"domain_scores_gemma":[0.99933624,0.00034002302,0.00010130983,0.000049637776,0.00012603089,0.000046761576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002997255,0.00012893703,0.000156367,0.00025281147,0.00008513176,0.00019570561,0.0002174439,0.00003847678,4.977852e-7],"category_scores_gemma":[0.00013847086,0.00010774403,0.00002214213,0.00026855923,0.000016890943,0.00031379957,0.00003756506,0.0002757903,1.2539591e-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":[0.000021081622,0.00008548652,0.000035293546,0.000010844584,0.000017956963,0.000004523919,0.00087737036,0.88425994,0.00040985024,0.06514449,0.0000042027086,0.049128965],"study_design_scores_gemma":[0.00090979855,0.0001612931,0.0012925962,0.00004174021,0.0000036536826,0.00025305434,0.00002892326,0.9676009,0.00007824967,0.029476162,0.000035897243,0.00011774042],"about_ca_topic_score_codex":0.000001677034,"about_ca_topic_score_gemma":0.000004776345,"teacher_disagreement_score":0.64690113,"about_ca_system_score_codex":0.00014961447,"about_ca_system_score_gemma":0.000039468625,"threshold_uncertainty_score":0.4393674},"labels":[],"label_agreement":null},{"id":"W1994554484","doi":"10.1287/opre.48.6.939.12393","title":"Global Stochastic Optimization with Low-Dispersion Point Sets","year":2000,"lang":"en","type":"article","venue":"Operations Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Smoothness; Basis (linear algebra); Euclidean space; Mathematics; Mathematical optimization; Bounded function; Domain (mathematical analysis); Rate function; Function (biology); Large deviations theory; Point (geometry); Space (punctuation); Stochastic optimization; Applied mathematics; Sample (material); Computer science; Statistics; Mathematical analysis","score_opus":0.021883795247685297,"score_gpt":0.3426205580910039,"score_spread":0.3207367628433186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994554484","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":[0.0026676902,0.000044725923,0.99149704,0.0010990608,0.000051940828,0.0005874885,0.000012916404,0.00014340408,0.0038957351],"genre_scores_gemma":[0.17808914,0.000052288367,0.8187493,0.00013277863,0.00005110739,0.00013386279,0.000052229556,0.000020734758,0.0027185266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977906,0.0002338694,0.00019654063,0.00056222785,0.00077793497,0.00043885657],"domain_scores_gemma":[0.998269,0.00006656008,0.000014133378,0.00062107964,0.00084857683,0.00018063538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038660286,0.00014554097,0.00012162552,0.00018388625,0.0008096981,0.00046127528,0.00059259223,0.000064718864,0.0008157895],"category_scores_gemma":[0.00011683309,0.00012490003,0.000027482096,0.0020369105,0.0001510528,0.0013719746,0.00014066095,0.00020871851,0.00034091392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018207258,0.00011463186,0.000009138012,0.0000023956727,0.00000721599,0.000009396861,0.00022666347,0.97586435,0.000022976381,0.0027262967,0.000119659875,0.02087904],"study_design_scores_gemma":[0.0005909013,0.00015145108,0.00019560897,0.000024518948,0.0000020140042,0.000039186696,0.00007138557,0.99845755,0.00006211525,0.00009747486,0.0001489347,0.00015883514],"about_ca_topic_score_codex":0.00011528704,"about_ca_topic_score_gemma":0.000111707996,"teacher_disagreement_score":0.17542146,"about_ca_system_score_codex":0.00035010386,"about_ca_system_score_gemma":0.00030558757,"threshold_uncertainty_score":0.8932323},"labels":[],"label_agreement":null},{"id":"W1995351961","doi":"10.1002/atr.5670430405","title":"Multi‐objective highway alignment optimization using a genetic algorithm","year":2009,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Process (computing); Genetic algorithm; Mathematical optimization; Multi-objective optimization; Set (abstract data type); Computer science; Earthworks; Function (biology); Engineering; Mathematics","score_opus":0.01230364714888291,"score_gpt":0.26886735016511776,"score_spread":0.2565637030162348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995351961","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":[0.003738168,0.0002567952,0.99488,0.00011636963,0.0006187032,0.00029739377,0.0000071314425,0.00007101525,0.000014415494],"genre_scores_gemma":[0.061309792,0.00017348997,0.93821347,0.00015543257,0.00010039465,0.000003543465,0.000008827895,0.000018578825,0.000016467864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979725,0.00006978072,0.00081388955,0.00033503846,0.0005372353,0.00027158894],"domain_scores_gemma":[0.9977817,0.00004555582,0.0009329711,0.00023991393,0.00084484706,0.00015504067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017563341,0.00023841849,0.00032434645,0.00033888782,0.00013791237,0.00006144627,0.00037069755,0.00008186123,0.000008347934],"category_scores_gemma":[0.00003521275,0.00023395474,0.00014866759,0.0006769011,0.000032459793,0.0020477946,0.0000055420537,0.0002031507,0.0000016040806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027530987,0.00016964068,0.000031612388,0.0000035423204,0.00002472103,0.00006332276,0.0012427261,0.92092866,0.00312242,0.00011017904,0.0000019538645,0.07427367],"study_design_scores_gemma":[0.002804025,0.0004768241,0.029538672,0.00007958751,0.00004873993,0.00009402335,0.00024070176,0.96068025,0.0049163676,0.00073109433,0.000068840294,0.00032088804],"about_ca_topic_score_codex":0.0000029105597,"about_ca_topic_score_gemma":0.0000014030026,"teacher_disagreement_score":0.07395278,"about_ca_system_score_codex":0.000288393,"about_ca_system_score_gemma":0.00013645679,"threshold_uncertainty_score":0.95403975},"labels":[],"label_agreement":null},{"id":"W1997615575","doi":"10.1061/40794(179)19","title":"A Framework for Simulation-Based Optimization with Application to Green Building Design","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Concordia University","funders":"","keywords":"Computer science; Mathematical optimization; Engineering optimization; Optimization problem; Continuous optimization; Genetic algorithm; Multidisciplinary design optimization; Simulation-based optimization; Topology optimization; Multi-swarm optimization; Algorithm; Engineering; Mathematics; Machine learning; Finite element method","score_opus":0.022834825727171108,"score_gpt":0.3119995981038571,"score_spread":0.289164772376686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997615575","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":[0.0000061866526,0.0000047986396,0.9963595,0.0017008493,0.000030448367,0.0014598815,0.0000017533366,0.00038106318,0.00005548551],"genre_scores_gemma":[0.06415918,3.31766e-7,0.9338706,0.0014038059,0.000086342785,0.0003702285,0.00000599547,0.00002399586,0.00007953207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886835,0.000031385058,0.0001952897,0.00047571634,0.00020820816,0.00022104515],"domain_scores_gemma":[0.99818987,0.0007685003,0.00010925291,0.00041672424,0.00040598225,0.000109691384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016526342,0.00014892462,0.0001223637,0.00016691055,0.00017228286,0.00009772399,0.00036185922,0.00006576224,0.000012023654],"category_scores_gemma":[0.00018179645,0.00013521466,0.000027859975,0.00077350176,0.000015210947,0.0005851102,0.000037614842,0.000058485988,0.000013888229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027073986,0.000037529,0.000012179748,0.0000028690808,0.0000048329416,1.0798368e-7,0.00009306261,0.93589354,0.000041069918,0.016884448,0.000005952524,0.046997346],"study_design_scores_gemma":[0.0004710422,0.000110256304,0.000016727552,0.0000136443105,0.0000047586827,4.3803212e-7,0.0000057878897,0.9947438,0.0022856907,0.0016280722,0.0005126031,0.00020718163],"about_ca_topic_score_codex":0.000005997645,"about_ca_topic_score_gemma":0.0000036229776,"teacher_disagreement_score":0.06415299,"about_ca_system_score_codex":0.00014015095,"about_ca_system_score_gemma":0.00007063959,"threshold_uncertainty_score":0.5513894},"labels":[],"label_agreement":null},{"id":"W1997995971","doi":"10.1115/1.4001597","title":"Metamodeling for High Dimensional Simulation-Based Design Problems","year":2010,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":179,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metamodeling; Curse of dimensionality; Black box; Computer science; Radial basis function; Engineering design process; Mathematical optimization; Function (biology); Process (computing); Nonlinear system; Algorithm; Engineering; Machine learning; Artificial intelligence; Artificial neural network; Mathematics","score_opus":0.051281716922726504,"score_gpt":0.2965239383016135,"score_spread":0.245242221378887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997995971","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":[0.00011361382,0.00002610465,0.9978022,0.00051223394,0.0009568549,0.0005332107,0.0000018234012,0.000051931762,0.0000020534865],"genre_scores_gemma":[0.29125613,0.0000012612143,0.708324,0.00024265693,0.00012663375,0.000014888337,6.60536e-7,0.000018048882,0.000015725564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979844,0.00021125792,0.00068258424,0.0002918922,0.0005583192,0.00027150894],"domain_scores_gemma":[0.9945517,0.003162097,0.0005806749,0.00029207868,0.0012071324,0.00020631586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021389707,0.0001860757,0.00033515642,0.00020180558,0.00016038008,0.00009979407,0.00066371396,0.00013642911,0.000025865656],"category_scores_gemma":[0.0017437707,0.0001504236,0.00016533068,0.00030053846,0.000025575813,0.00066569814,0.000052018317,0.00040094688,0.000005882171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008238159,0.00012445472,2.9264922e-7,0.000004396367,0.000027167942,0.000006493101,0.00002101883,0.96158075,0.025614923,0.0060759736,0.000036030622,0.0064260988],"study_design_scores_gemma":[0.0014725422,0.0004471319,0.000002670995,0.000023396142,0.000026013882,0.000016653137,0.000001427102,0.918486,0.029660016,0.049631625,0.00006611882,0.00016639761],"about_ca_topic_score_codex":0.000001075954,"about_ca_topic_score_gemma":4.1995688e-7,"teacher_disagreement_score":0.2911425,"about_ca_system_score_codex":0.000060202117,"about_ca_system_score_gemma":0.000299958,"threshold_uncertainty_score":0.61340964},"labels":[],"label_agreement":null},{"id":"W1999582351","doi":"10.1007/s00170-011-3324-4","title":"Incorporation of axiomatic design theory into design of a microchannel system for uniform and size-controllable microspheres","year":2011,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 Saskatchewan","funders":"","keywords":"Axiomatic design; Microchannel; Controllability; Conceptual design; Probabilistic design; Process (computing); Design process; Microsphere; Computer science; Systems design; Optimal design; Engineering design process; Systems engineering; Engineering; Process engineering; Mechanical engineering; Nanotechnology; Mathematics; Materials science; Manufacturing engineering; Applied mathematics; Chemical engineering","score_opus":0.015729390095554935,"score_gpt":0.23421178302054876,"score_spread":0.21848239292499383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999582351","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":[0.03504166,0.00028518893,0.96365446,0.00022979378,0.00030487264,0.00042005436,0.0000027995402,0.000045469424,0.000015690212],"genre_scores_gemma":[0.4786031,0.00003806015,0.5213005,0.000013605034,0.0000117181535,0.000012643122,1.7712168e-7,0.000008129548,0.000012073982],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987528,0.00007486155,0.0006458014,0.00016798326,0.00021569403,0.00014290937],"domain_scores_gemma":[0.9968342,0.0006988187,0.0014319953,0.00025444943,0.0007515284,0.000029015266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007718812,0.00014879362,0.00032283354,0.0002580869,0.00006733969,0.000019557248,0.0012707505,0.00008492743,0.0000027665083],"category_scores_gemma":[0.00036812318,0.000110040666,0.000063362706,0.00014569673,0.00024838423,0.00049129815,0.000209451,0.00012734433,4.764588e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025942998,0.00024316837,0.00002720585,0.0001705027,0.00070401607,0.00003040133,0.003776313,0.27717417,0.5291713,0.08556433,0.000016532142,0.10052776],"study_design_scores_gemma":[0.0013123314,0.00034227435,0.00003138336,0.00012657659,0.000020261872,0.00017127462,0.00081988104,0.019334689,0.79551584,0.18223497,0.000007147255,0.00008335953],"about_ca_topic_score_codex":0.000007936095,"about_ca_topic_score_gemma":0.0000021374397,"teacher_disagreement_score":0.44356143,"about_ca_system_score_codex":0.00012774389,"about_ca_system_score_gemma":0.00008860227,"threshold_uncertainty_score":0.4487328},"labels":[],"label_agreement":null},{"id":"W1999841373","doi":"10.1007/s00158-014-1186-8","title":"Differential geometry tools for multidisciplinary design optimization, Part I: Theory","year":2014,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Cambridge Trust; Cambridge Commonwealth Trust","keywords":"Multidisciplinary design optimization; Mathematical proof; Multidisciplinary approach; Differentiable function; Sensitivity (control systems); Limit (mathematics); Computer science; Function (biology); Differential (mechanical device); Engineering design process; Riemannian geometry; Translation (biology); Information geometry; Mathematics; Algebra over a field; Mathematical optimization; Geometry; Pure mathematics; Engineering; Mathematical analysis; Curvature; Mechanical engineering; Aerospace engineering","score_opus":0.021270214954355996,"score_gpt":0.2709732141313832,"score_spread":0.2497029991770272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999841373","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":[0.001546126,0.000097837044,0.9945941,0.00042370247,0.0012693146,0.0014562465,0.0000461789,0.0004488296,0.00011764241],"genre_scores_gemma":[0.05921672,0.000111331494,0.9391851,0.00007597317,0.0003626964,0.00021459063,0.00036975564,0.000065929205,0.00039793595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968772,0.00035275167,0.0006498358,0.0011543791,0.00035016247,0.0006156549],"domain_scores_gemma":[0.9972336,0.0009759555,0.00037968333,0.00064761104,0.0004904669,0.00027268188],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00044957627,0.0005396828,0.00047116153,0.00031447236,0.0013577708,0.000466543,0.0006424088,0.0002283681,0.00008636214],"category_scores_gemma":[0.0005593141,0.0004724211,0.0001400007,0.00060520397,0.00022158008,0.0027339389,0.00062752917,0.00020128278,0.0000040205045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001456605,0.000047988087,0.00006383479,0.000046735895,0.00003644357,0.0000015135398,0.0005730539,0.96521336,0.00012929493,0.011284327,0.000038177808,0.022419594],"study_design_scores_gemma":[0.0021921287,0.00031874154,0.00073373143,0.000037468108,0.0000610573,0.00003351583,0.00015391743,0.9882931,0.00055891986,0.0069135483,0.000049572318,0.0006542979],"about_ca_topic_score_codex":0.000002044725,"about_ca_topic_score_gemma":9.729572e-7,"teacher_disagreement_score":0.057670593,"about_ca_system_score_codex":0.00009481994,"about_ca_system_score_gemma":0.00006690397,"threshold_uncertainty_score":0.9999423},"labels":[],"label_agreement":null},{"id":"W2003910800","doi":"10.1115/1.4002978","title":"Turning Black-Box Functions Into White Functions","year":2011,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Black box; Metamodeling; White box; Variable (mathematics); Process (computing); Radial basis function; Representation (politics); Computer science; Function (biology); Algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Machine learning; Artificial neural network","score_opus":0.043489530459787026,"score_gpt":0.2581433939509434,"score_spread":0.21465386349115634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003910800","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":[0.000081410326,0.000066647364,0.99717414,0.0003508282,0.0012990179,0.00013325317,7.683546e-7,0.000087960936,0.0008059875],"genre_scores_gemma":[0.071623206,0.000025852394,0.92708457,0.00019860959,0.00019329411,0.0000059718145,3.7391354e-7,0.00002069506,0.0008474362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981586,0.00026376807,0.000603115,0.0002738399,0.00043073294,0.00026994944],"domain_scores_gemma":[0.99794316,0.00023418148,0.00049522833,0.00036577837,0.00068197405,0.0002796884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008582292,0.00017846984,0.0002766639,0.0002899429,0.00021045885,0.000078861085,0.00069281796,0.000109466666,0.0002336589],"category_scores_gemma":[0.0005294933,0.00015171076,0.00018262053,0.0006763337,0.00005183427,0.0011802576,0.00014582514,0.0004957591,0.00015995458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009675593,0.0034111196,0.0002775397,0.000058046186,0.0011077193,0.0009808528,0.014731094,0.5692208,0.056176234,0.07823763,0.028184833,0.24664661],"study_design_scores_gemma":[0.002683075,0.0025385,0.0007140097,0.00013609508,0.00014722282,0.00092069345,0.0008485266,0.9174676,0.0156799,0.053502277,0.0045865383,0.0007755385],"about_ca_topic_score_codex":0.0000028675063,"about_ca_topic_score_gemma":0.0000015924032,"teacher_disagreement_score":0.34824687,"about_ca_system_score_codex":0.00014254254,"about_ca_system_score_gemma":0.00016377572,"threshold_uncertainty_score":0.61865854},"labels":[],"label_agreement":null},{"id":"W2005144156","doi":"10.1007/s12293-011-0072-9","title":"DMEA: a direction-based multiobjective evolutionary algorithm","year":2011,"lang":"en","type":"article","venue":"Memetic Computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Population; Benchmark (surveying); Computer science; Evolutionary algorithm; Multi-objective optimization; Ideal (ethics); Selection (genetic algorithm); Mathematical optimization; Pareto principle; Convergence (economics); Point (geometry); Algorithm; Current (fluid); Artificial intelligence; Mathematics; Machine learning; Geography; Geology","score_opus":0.02228156810671539,"score_gpt":0.24519089203241773,"score_spread":0.22290932392570234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005144156","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":[0.00045481912,0.00013197388,0.9922481,0.00006629641,0.0011060426,0.00036467475,0.0000034564314,0.0008172663,0.0048073283],"genre_scores_gemma":[0.17681248,0.000002667458,0.8226802,0.00018937438,0.00011577958,0.000023078559,0.0000043063687,0.000025081506,0.0001470259],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977852,0.00017511034,0.00039452597,0.0007702656,0.00039173602,0.00048312277],"domain_scores_gemma":[0.998296,0.0002752505,0.0002292949,0.0005757575,0.00046915383,0.00015451579],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003408817,0.0002791375,0.0002657098,0.00032722385,0.0004528721,0.00006280506,0.00065015885,0.000082106046,0.00006957176],"category_scores_gemma":[0.00023171052,0.00029473854,0.00012737227,0.0011370383,0.000108538996,0.000416747,0.00030999992,0.0002521263,0.00009337353],"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":[0.000014398662,0.00060125766,0.0012057024,0.000019412122,0.00007998939,0.00006279615,0.0038622147,0.047975007,0.00021649136,0.0026633483,0.00014489242,0.9431545],"study_design_scores_gemma":[0.00077534444,0.00009049932,0.016133169,0.000034026187,0.000010347191,0.000029641247,0.00008540515,0.9794652,0.0016986616,0.0009120843,0.000418893,0.0003467674],"about_ca_topic_score_codex":0.00012323793,"about_ca_topic_score_gemma":0.0000041362437,"teacher_disagreement_score":0.94280773,"about_ca_system_score_codex":0.00027782883,"about_ca_system_score_gemma":0.00015355516,"threshold_uncertainty_score":0.99995047},"labels":[],"label_agreement":null},{"id":"W2006143075","doi":"10.1080/02331888.2012.694440","title":"Pareto analysis based on records","year":2012,"lang":"en","type":"article","venue":"Statistics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Pareto principle; Econometrics; Lomax distribution; Statistics","score_opus":0.015709524405966735,"score_gpt":0.28814200654450883,"score_spread":0.2724324821385421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006143075","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":[0.000026565109,0.0000107176,0.9968524,0.00006265833,0.00038376576,0.00007296291,0.00010797569,0.00009943337,0.0023835308],"genre_scores_gemma":[0.10627808,0.0000036817883,0.89289385,0.00040633648,0.00005029574,0.000009903521,0.00003823864,0.000008655679,0.00031097126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999065,0.00006485435,0.0001497758,0.00021125982,0.00024736844,0.00026178907],"domain_scores_gemma":[0.9989383,0.00026855507,0.00008637224,0.00043979206,0.00013404858,0.00013288252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015341252,0.0001072663,0.0001382023,0.00019280211,0.000084307656,0.00004457085,0.0002531785,0.000030110437,0.00009248299],"category_scores_gemma":[0.00022577489,0.00010383088,0.000038580827,0.0009389851,0.000028116992,0.00021268305,0.000046322148,0.00008196613,0.00010338492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021314434,0.00055090815,0.042576343,0.000017503235,0.0002652692,0.000028705774,0.0009059305,0.5344535,0.000017444861,0.2346516,0.006596253,0.17991519],"study_design_scores_gemma":[0.00016343378,0.00004125118,0.016300924,0.0000020109292,0.00003920779,4.9276e-7,0.000008958138,0.9804844,0.00008183211,0.0008087004,0.0019259324,0.00014283424],"about_ca_topic_score_codex":0.000009588907,"about_ca_topic_score_gemma":0.000009726605,"teacher_disagreement_score":0.44603088,"about_ca_system_score_codex":0.00007749122,"about_ca_system_score_gemma":0.000034645487,"threshold_uncertainty_score":0.42341003},"labels":[],"label_agreement":null},{"id":"W2007555958","doi":"10.1016/j.ces.2007.01.030","title":"Design stage optimization of an industrial low-density polyethylene tubular reactor for multiple objectives using NSGA-II and its jumping gene adaptations","year":2007,"lang":"en","type":"article","venue":"Chemical Engineering Science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Mathematical optimization; Multi-objective optimization; Constraint (computer-aided design); Maximization; Optimization problem; Mathematics; Genetic algorithm; Sorting; Penalty method; Pareto principle; Algorithm","score_opus":0.039647753271969954,"score_gpt":0.27215985685618066,"score_spread":0.2325121035842107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007555958","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":[0.28004965,0.000028986937,0.7193758,0.0000070106034,0.00013460893,0.00030487526,0.000005652671,0.00009210565,0.0000013092172],"genre_scores_gemma":[0.46704322,0.000002081643,0.532892,0.0000055831056,0.0000375993,0.000004939084,0.000002373792,0.000009872723,0.0000023635805],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841,0.000016027925,0.0003043448,0.00056227803,0.0003232712,0.00038411934],"domain_scores_gemma":[0.9986206,0.00033335466,0.00014860863,0.00026006188,0.00043274707,0.0002046086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083794625,0.00017277304,0.00019586636,0.00027324416,0.0002167885,0.00006470725,0.00044607584,0.000097822536,8.725594e-7],"category_scores_gemma":[0.0015556719,0.00019038991,0.000030506508,0.0010641838,0.00013595754,0.0014162603,0.00019955762,0.00013900537,1.1045038e-7],"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":[0.000008633131,0.00002422679,0.000004408671,0.000005217968,0.000002517416,7.3240113e-7,0.0002366611,0.49366888,0.5054042,0.00010987099,4.164028e-8,0.0005346621],"study_design_scores_gemma":[0.00031745364,0.000030824387,0.00004851822,0.000016724602,0.0000029250762,0.000004414997,0.000015326086,0.51257783,0.48686785,0.000007714472,6.875545e-7,0.00010972677],"about_ca_topic_score_codex":0.000006990781,"about_ca_topic_score_gemma":1.9243751e-7,"teacher_disagreement_score":0.18699355,"about_ca_system_score_codex":0.00018610436,"about_ca_system_score_gemma":0.00016820406,"threshold_uncertainty_score":0.7763875},"labels":[],"label_agreement":null},{"id":"W2008003451","doi":"10.1115/1.4026281","title":"Value-Based Global Optimization","year":2013,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Kriging; Computer science; Mathematical optimization; Surrogate model; Global optimization; Metric (unit); Algorithm; Process (computing); Sampling (signal processing); Mathematics; Machine learning; Engineering","score_opus":0.01972188803488294,"score_gpt":0.2595366694631564,"score_spread":0.23981478142827345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008003451","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":[0.000019645458,0.00004826605,0.9978524,0.0011694847,0.0005002276,0.00021781707,5.834303e-7,0.00005552449,0.00013606192],"genre_scores_gemma":[0.023147566,0.000012662621,0.9759979,0.000718319,0.00008374957,0.0000061845926,3.4341127e-7,0.0000096157855,0.000023662655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831855,0.00027154366,0.0004898605,0.00019634246,0.00050293095,0.00022078831],"domain_scores_gemma":[0.99814856,0.00021209872,0.00043556577,0.00025637267,0.0007301259,0.00021730605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055335107,0.000138109,0.00022870733,0.00009174219,0.000070934766,0.00015048479,0.00073182525,0.00008957058,0.00010670017],"category_scores_gemma":[0.00045789196,0.00011353987,0.0001164276,0.0005128108,0.00002141075,0.0010641676,0.000069349466,0.00017106628,0.00004955556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013769337,0.0000976861,0.0000026502107,0.0000019327206,0.000013953883,0.000016120523,0.000008450882,0.9776001,0.00040336154,0.009141946,0.00047352765,0.012226487],"study_design_scores_gemma":[0.0008701771,0.00032596054,0.0000396009,0.000021879052,0.000009125941,0.000074619915,0.000005566658,0.98082894,0.0029098887,0.014736389,0.000054030294,0.0001238177],"about_ca_topic_score_codex":0.0000032252524,"about_ca_topic_score_gemma":7.6162955e-8,"teacher_disagreement_score":0.023127919,"about_ca_system_score_codex":0.00020052331,"about_ca_system_score_gemma":0.00020263936,"threshold_uncertainty_score":0.46300215},"labels":[],"label_agreement":null},{"id":"W2008533091","doi":"10.1007/s11081-006-0351-3","title":"Variable-fidelity optimization: Efficiency and robustness","year":2006,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Robustness (evolution); Solver; Fidelity; Computer science; Mathematical optimization; Algorithm; Mathematics","score_opus":0.004538927013477352,"score_gpt":0.19152137529082455,"score_spread":0.1869824482773472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008533091","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":[0.000115376235,0.00020698742,0.99810123,0.00011300169,0.00019175342,0.00015553353,0.0000022946092,0.0003523079,0.0007614817],"genre_scores_gemma":[0.010505766,0.00008542327,0.98911136,0.000039458715,0.000055512213,0.000016280399,0.000017073156,0.000020593237,0.00014852166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893516,0.000020158966,0.00024897663,0.0004139469,0.0001507963,0.00023094505],"domain_scores_gemma":[0.9993926,0.00005812097,0.00007001597,0.00023109984,0.00015666967,0.000091447284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015450236,0.00018319784,0.00015780552,0.00015413579,0.00017049874,0.00020926197,0.00015760463,0.000076624114,0.000021498381],"category_scores_gemma":[0.00006674236,0.00019740286,0.00001644931,0.0006253688,0.000033170647,0.00080342835,0.00012527856,0.000092296126,7.100797e-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":[0.0000010436825,0.000020534428,0.00005119681,0.000014964099,0.0000035009584,0.0000018224193,0.000026880796,0.9833694,0.000027779006,0.01608523,0.000012238258,0.00038541667],"study_design_scores_gemma":[0.00041933867,0.0000134596685,0.00020212283,0.000016647171,0.0000051651627,0.000028923807,0.0000071095856,0.99882656,0.00004214115,0.00004686707,0.00016128895,0.00023036788],"about_ca_topic_score_codex":0.000011046651,"about_ca_topic_score_gemma":4.0686206e-7,"teacher_disagreement_score":0.016038362,"about_ca_system_score_codex":0.000040439692,"about_ca_system_score_gemma":0.000023913344,"threshold_uncertainty_score":0.80498546},"labels":[],"label_agreement":null},{"id":"W2010014459","doi":"10.1080/03052150903386674","title":"Trends, features, and tests of common and recently introduced global optimization methods","year":2010,"lang":"en","type":"article","venue":"Engineering Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Global optimization; Engineering optimization; Test functions for optimization; Multidisciplinary design optimization; Computer science; Benchmark (surveying); Optimization problem; Metaheuristic; Mathematical optimization; Metamodeling; Continuous optimization; Multi-swarm optimization; Multidisciplinary approach; Artificial intelligence; Algorithm; Mathematics","score_opus":0.0058888076438176895,"score_gpt":0.28086194795187835,"score_spread":0.27497314030806064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010014459","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":[0.0004833205,0.0002102371,0.99799156,0.00027165998,0.00049876526,0.00014767128,0.0000068188183,0.00022191729,0.00016803686],"genre_scores_gemma":[0.018818714,0.00012489845,0.98087406,0.000030455562,0.000051326944,0.000011215887,0.000032446576,0.000020282057,0.000036603567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892664,0.000054643995,0.00026893464,0.00040567332,0.00015232188,0.00019179929],"domain_scores_gemma":[0.9991066,0.00011058358,0.00014880169,0.00035184657,0.00017228242,0.000109892724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026512705,0.0001987451,0.0002288939,0.00021090255,0.000075629636,0.00009685253,0.00021128841,0.00012662458,0.000009803195],"category_scores_gemma":[0.00037109456,0.00021235508,0.000024390418,0.0008471513,0.00004683541,0.00065380277,0.00015599605,0.00018880331,2.2267264e-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":[0.0000042106512,0.000028048476,0.00013746186,0.000013121606,0.0000113696,7.447164e-7,0.00009965425,0.9575661,0.0009777087,0.00294953,0.000022957043,0.038189128],"study_design_scores_gemma":[0.0004528735,0.000039691397,0.004339144,0.000014489796,0.00001088023,0.000050439496,0.0000049865353,0.99370235,0.0010392966,0.000047555048,0.00009948092,0.00019878458],"about_ca_topic_score_codex":0.000010889647,"about_ca_topic_score_gemma":0.0000046349733,"teacher_disagreement_score":0.037990343,"about_ca_system_score_codex":0.00004040151,"about_ca_system_score_gemma":0.000021204927,"threshold_uncertainty_score":0.86595887},"labels":[],"label_agreement":null},{"id":"W2011178729","doi":"10.1145/1570256.1570259","title":"A new multi-objective algorithm, pareto archived DDS","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Economic Affairs","keywords":"Benchmark (surveying); Pareto principle; Computer science; Maxima and minima; Mathematical optimization; Multi-objective optimization; Algorithm; Set (abstract data type); Mathematics","score_opus":0.014631091310723822,"score_gpt":0.2730592972777367,"score_spread":0.2584282059670129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011178729","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":[0.000017460772,0.000054515385,0.98791105,0.0007824234,0.0003169212,0.00040464444,0.0000038711883,0.00080130325,0.009707833],"genre_scores_gemma":[0.0053873635,0.000022468124,0.98672044,0.001362844,0.00012505303,0.000013711748,0.000005974859,0.000018033797,0.0063441265],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978931,0.000087535744,0.0003169886,0.0008140582,0.00036040245,0.0005279336],"domain_scores_gemma":[0.9984792,0.00010821725,0.00012579998,0.0007266529,0.00020125457,0.00035887325],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012425143,0.00031035815,0.0002846443,0.00023034893,0.00018208656,0.00016777248,0.0009599886,0.00008142588,0.000076921635],"category_scores_gemma":[0.00010229613,0.0002868099,0.00011836852,0.0006011098,0.00004296687,0.0009990727,0.00019316348,0.00024235742,0.00022032537],"study_design_candidate":"design_other","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":[0.000008251054,0.00018061047,0.000054825425,0.0000011780787,0.000024879151,0.000039255156,0.0015449991,0.00509215,0.0004999202,0.009917449,0.00079319096,0.9818433],"study_design_scores_gemma":[0.0020169816,0.00026454256,0.01199999,0.000014963633,0.0000069044213,0.00004761442,0.00009518086,0.9684465,0.0045831,0.00999368,0.0019577725,0.0005727856],"about_ca_topic_score_codex":0.00006725545,"about_ca_topic_score_gemma":0.000028059903,"teacher_disagreement_score":0.9812705,"about_ca_system_score_codex":0.00013130237,"about_ca_system_score_gemma":0.00020151745,"threshold_uncertainty_score":0.9999584},"labels":[],"label_agreement":null},{"id":"W2012569716","doi":"10.1007/s10957-014-0576-9","title":"A Weighted Sequential Sampling Method Considering Influences of Sample Qualities in Input and Output Parameter Spaces for Global Optimization","year":2014,"lang":"en","type":"article","venue":"Journal of Optimization Theory and Applications","topic":"Advanced Multi-Objective Optimization Algorithms","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 Calgary","funders":"","keywords":"Metamodeling; Weighting; Sampling (signal processing); Adaptive sampling; Sample (material); Mathematics; Mathematical optimization; Sample space; Quality (philosophy); Process (computing); Algorithm; Computer science; Statistics; Monte Carlo method","score_opus":0.031821269869103566,"score_gpt":0.3417841240154184,"score_spread":0.30996285414631486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012569716","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":[0.0011294626,0.0001711185,0.99805075,0.0001755823,0.00005200785,0.00034439616,0.000015750953,0.000020909169,0.000040033487],"genre_scores_gemma":[0.045244638,0.00013809821,0.95441675,0.000090167836,0.00004408402,0.000042969394,0.0000071001286,0.000008252524,0.000007942933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860704,0.00028866326,0.0006035078,0.0002231288,0.00014369002,0.00013397935],"domain_scores_gemma":[0.996683,0.0019589316,0.0006808854,0.00015430845,0.00044978154,0.00007306654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012696672,0.00013114235,0.00028707084,0.00020173965,0.00013901865,0.00012314407,0.0001900562,0.000070101356,0.000003774501],"category_scores_gemma":[0.0007761383,0.00012257717,0.000046349072,0.0003875351,0.00012561562,0.00074561767,0.000070235044,0.000080989244,5.961424e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000337608,0.000032338357,0.00030519033,0.000025076753,0.000020308838,8.317664e-8,0.0004328532,0.8280587,0.000025831765,0.15851136,7.4959064e-7,0.012553714],"study_design_scores_gemma":[0.0007809686,0.000069459034,0.0001257872,0.00004479829,0.000026173499,0.00002817464,0.00033118782,0.90173835,0.00032731457,0.096225366,0.00017407729,0.00012835495],"about_ca_topic_score_codex":0.0000053978806,"about_ca_topic_score_gemma":0.00000199406,"teacher_disagreement_score":0.0736796,"about_ca_system_score_codex":0.00003718427,"about_ca_system_score_gemma":0.00006334766,"threshold_uncertainty_score":0.4998552},"labels":[],"label_agreement":null},{"id":"W2012884505","doi":"10.1007/s00158-011-0666-3","title":"pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization","year":2011,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":422,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Python (programming language); Computer science; Optimization problem; Mathematical optimization; Nonlinear programming; Object-oriented programming; Engineering optimization; Programming language; Theoretical computer science; Nonlinear system; Algorithm; Mathematics","score_opus":0.020096411531526432,"score_gpt":0.2790700468690601,"score_spread":0.2589736353375337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012884505","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":[0.0010049392,0.000068227746,0.99581873,0.00025555174,0.0008266184,0.0012318586,0.00009038292,0.00050151115,0.00020218249],"genre_scores_gemma":[0.037727695,0.000031495005,0.9614224,0.000115484596,0.00012353701,0.00012895983,0.00033022757,0.000051904048,0.00006828449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774384,0.000088352506,0.0005184889,0.0009043475,0.00026445044,0.00048053297],"domain_scores_gemma":[0.9981375,0.00021213159,0.0003502848,0.00048845826,0.00058459345,0.00022705505],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017015619,0.00042315415,0.0003495238,0.00025932837,0.00074609515,0.00011141641,0.00039061354,0.00023609838,0.000065371685],"category_scores_gemma":[0.0002667666,0.00038943137,0.000114991104,0.00072005857,0.00023385778,0.0011112419,0.00021506453,0.00020153404,0.0000021318517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014625871,0.00006105395,0.00018567321,0.00004466846,0.000025352056,0.000005209479,0.0014903006,0.98161817,0.000036757916,0.01268982,0.0000051602824,0.0036915736],"study_design_scores_gemma":[0.0018772492,0.0003044174,0.00041889018,0.000051179577,0.000041494342,0.000023378272,0.00022225668,0.99262714,0.0008162389,0.003096282,0.000016316326,0.0005051477],"about_ca_topic_score_codex":0.000008535767,"about_ca_topic_score_gemma":0.0000025854313,"teacher_disagreement_score":0.036722757,"about_ca_system_score_codex":0.00008559123,"about_ca_system_score_gemma":0.00013271428,"threshold_uncertainty_score":0.99985576},"labels":[],"label_agreement":null},{"id":"W2014288437","doi":"10.1504/ijpd.2009.026178","title":"Approximated unimodal region elimination-based global optimisation method for engineering design","year":2009,"lang":"en","type":"article","venue":"International Journal of Product Development","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Benchmark (surveying); Computation; Global optimization; Mathematical optimization; Computer science; Field (mathematics); Ideal (ethics); Function (biology); Algorithm; Mathematics","score_opus":0.024607933549310606,"score_gpt":0.3084243863992482,"score_spread":0.28381645284993756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014288437","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":[0.00013308213,0.000051723582,0.9937846,0.0046309778,0.0009698887,0.00033379407,0.0000013443828,0.0000650113,0.000029558969],"genre_scores_gemma":[0.06394194,0.000005452098,0.9355641,0.00025256528,0.0001754726,0.000014907826,0.000009513347,0.000008208274,0.000027831788],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830645,0.000059345086,0.00057197636,0.00028143305,0.000600613,0.0001801884],"domain_scores_gemma":[0.9970074,0.000122853,0.00050475897,0.00014744618,0.0021379096,0.00007963173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007956442,0.00016489743,0.0001727992,0.0002927851,0.00006671204,0.00012578601,0.0007557128,0.000041304647,0.000002435975],"category_scores_gemma":[0.00061137904,0.00015925165,0.00006890484,0.0003188172,0.000009946696,0.0006989315,0.000035833098,0.00009706421,0.0000016324198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008876543,0.00014331947,0.000019916466,0.0000049472737,0.00006492079,0.00001979798,0.00027783655,0.73046154,0.00089784054,0.0049636546,0.0001391734,0.26291826],"study_design_scores_gemma":[0.0013521223,0.00013948455,0.0023283102,0.00006721314,0.000008858284,0.0001841771,0.000012280062,0.9401788,0.051699653,0.0016958051,0.002106431,0.00022687115],"about_ca_topic_score_codex":6.216266e-7,"about_ca_topic_score_gemma":1.3545372e-7,"teacher_disagreement_score":0.2626914,"about_ca_system_score_codex":0.00079363905,"about_ca_system_score_gemma":0.0005288806,"threshold_uncertainty_score":0.6494094},"labels":[],"label_agreement":null},{"id":"W2015261375","doi":"10.1115/1.4028883","title":"Error Metrics and the Sequential Refinement of Kriging Metamodels","year":2014,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metamodeling; Kriging; Computer science; Resampling; Engineering design process; Computer experiment; Reliability (semiconductor); Set (abstract data type); Process (computing); Data mining; Algorithm; Machine learning; Simulation; Engineering","score_opus":0.04480847137402148,"score_gpt":0.28783372277078895,"score_spread":0.24302525139676745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015261375","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":[0.00008921868,0.00016245805,0.99850684,0.0007214013,0.00028421707,0.00012532777,3.912121e-7,0.000010337309,0.000099796554],"genre_scores_gemma":[0.18998492,0.000073277966,0.8096529,0.0001955391,0.00005527024,0.0000020066466,6.749491e-8,0.000006631747,0.000029391646],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816597,0.00053020136,0.000533605,0.00014358855,0.00048705196,0.00013959093],"domain_scores_gemma":[0.9978879,0.0008428652,0.00060265214,0.00022493988,0.00035130675,0.00009033631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034199944,0.00010120184,0.00032604922,0.00016284492,0.00006890083,0.00004937323,0.00053627236,0.00004659278,0.000009616625],"category_scores_gemma":[0.0009247369,0.000060861843,0.000096894684,0.00038230393,0.000057402147,0.00033738842,0.00014373157,0.00021368812,0.0000010745593],"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":[0.0003322956,0.00020388668,0.0000022994118,0.000022857042,0.00024035081,0.000018777348,0.0004340823,0.25322488,0.008647091,0.63053644,0.00031950598,0.106017515],"study_design_scores_gemma":[0.0019277077,0.0002358496,0.000003501298,0.000018154893,0.0000432356,0.000063460604,0.000016416021,0.9544952,0.01152005,0.031279452,0.00032439202,0.00007259583],"about_ca_topic_score_codex":0.000002329793,"about_ca_topic_score_gemma":2.6582657e-7,"teacher_disagreement_score":0.7012703,"about_ca_system_score_codex":0.00003219781,"about_ca_system_score_gemma":0.000056083125,"threshold_uncertainty_score":0.2481874},"labels":[],"label_agreement":null},{"id":"W2017110647","doi":"10.1063/1.3452150","title":"Global Optimization Using Mixed Surrogate Models for Computation Intensive Designs","year":2010,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Benchmark (surveying); Latin hypercube sampling; Global optimization; Surrogate model; Mathematical optimization; Computation; Computer science; Optimization problem; Hypercube; Field (mathematics); Algorithm; Mathematics; Parallel computing; Monte Carlo method","score_opus":0.06332828907830652,"score_gpt":0.3098062357448717,"score_spread":0.2464779466665652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017110647","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":[0.0047439784,0.000006353186,0.99286497,0.00031034873,0.00061474007,0.00072545925,0.000012739308,0.0003220043,0.00039938744],"genre_scores_gemma":[0.44992065,0.0000032121393,0.54982257,0.00014131845,0.000037139936,0.000037284157,0.000009994385,0.000012503759,0.000015289055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834734,0.000012682585,0.00032745546,0.0006559552,0.00026437623,0.00039217254],"domain_scores_gemma":[0.99453956,0.000073689116,0.00030875008,0.00014776798,0.0047829095,0.0001473115],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024115136,0.0002554106,0.00024541345,0.00012887667,0.0002703659,0.0004192557,0.0005468074,0.00014078728,0.0000056709678],"category_scores_gemma":[0.00039578936,0.00027199238,0.00006638532,0.00062855316,0.000107352076,0.0024746181,0.00018131617,0.00016294561,0.0000037107975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003859502,0.00007347389,0.00078879704,0.00003394994,0.000031508764,0.000001731872,0.0017146687,0.8606104,0.005867064,0.11971922,0.00011700346,0.011003584],"study_design_scores_gemma":[0.0007057605,0.00007581107,0.00012503912,0.000025931397,0.000014764111,0.000031264037,0.0002884626,0.96755224,0.00074265036,0.030120268,0.000015420923,0.00030237075],"about_ca_topic_score_codex":0.000021007896,"about_ca_topic_score_gemma":0.00000888801,"teacher_disagreement_score":0.4451767,"about_ca_system_score_codex":0.00013406145,"about_ca_system_score_gemma":0.0002180367,"threshold_uncertainty_score":0.99997324},"labels":[],"label_agreement":null},{"id":"W2017226599","doi":"10.1093/biomet/asn064","title":"Construction of orthogonal and nearly orthogonal Latin hypercubes","year":2009,"lang":"en","type":"article","venue":"Biometrika","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Hypercube; Mathematics; Latin hypercube sampling; Library science; Combinatorics; Statistics; Computer science","score_opus":0.011571705788514589,"score_gpt":0.24103963182670712,"score_spread":0.22946792603819255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017226599","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":[0.12982512,0.00028994476,0.86828303,0.00026225997,0.00020291535,0.00012998848,0.000012922121,0.000100617675,0.00089318963],"genre_scores_gemma":[0.43194896,0.000043717482,0.5678203,0.000071872295,0.000034650693,0.0000016220313,0.0000040262944,0.0000043258397,0.00007048308],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99899626,0.000034686756,0.00023466231,0.00030760877,0.0002615952,0.00016517194],"domain_scores_gemma":[0.9992916,0.000087968736,0.00012496975,0.00021684461,0.00019338234,0.00008522723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014385658,0.00011526035,0.00016814934,0.0006024466,0.00007624653,0.000052623698,0.00020344656,0.00006259458,0.00001883369],"category_scores_gemma":[0.00013960799,0.00011042204,0.00003987747,0.0020482333,0.00012487239,0.0004606788,0.00006667635,0.00007465718,0.000007084783],"study_design_candidate":"design_other","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":[0.000022159049,0.00014001883,0.01171421,0.00001009247,0.000024667437,0.000009464619,0.00024239726,0.00035940067,0.011551149,0.077636994,0.000038466653,0.898251],"study_design_scores_gemma":[0.0029270954,0.0010773974,0.7891611,0.000056535686,0.000024988536,0.00030781465,0.00013662511,0.15947811,0.025176428,0.017620426,0.003170704,0.0008627482],"about_ca_topic_score_codex":0.000003354696,"about_ca_topic_score_gemma":7.2466344e-7,"teacher_disagreement_score":0.8973882,"about_ca_system_score_codex":0.000024658553,"about_ca_system_score_gemma":0.000053247884,"threshold_uncertainty_score":0.45028803},"labels":[],"label_agreement":null},{"id":"W2017760786","doi":"10.1115/1.3290763","title":"Lagrangian Relaxation Approach for Decentralized Decision Making in Engineering Design","year":2010,"lang":"en","type":"article","venue":"Journal of Computing and Information Science in Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lagrangian relaxation; Context (archaeology); Relaxation (psychology); Convergence (economics); Engineering design process; Workflow; Process (computing); Factor (programming language); Computer science; Mathematical optimization; Augmented Lagrangian method; Industrial engineering; Systems engineering; Engineering; Mechanical engineering; Mathematics","score_opus":0.009994730464558732,"score_gpt":0.2637967835799094,"score_spread":0.25380205311535065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017760786","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":[0.046283856,0.000016288885,0.95292664,0.000022131984,0.00053437054,0.00016685697,1.785501e-7,0.000028781486,0.00002091002],"genre_scores_gemma":[0.47890556,0.000007576915,0.5210574,0.000010586777,0.000014932219,0.0000012387771,1.6073939e-7,0.0000023798857,1.0671745e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987064,0.000009790075,0.0006043003,0.00011400229,0.00031880796,0.0002467283],"domain_scores_gemma":[0.99888885,0.00033750726,0.00031110056,0.00010850034,0.00028451427,0.00006953965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024268979,0.00010125587,0.00015863929,0.0012062081,0.00007462297,0.00025481038,0.00040156598,0.000049450206,3.027155e-7],"category_scores_gemma":[0.0016008768,0.00009658693,0.000028553766,0.0012677312,0.000023522609,0.0062766434,0.00006816066,0.00027838553,2.2476074e-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":[0.0000059928666,0.000008110853,0.00025637573,0.000016719629,8.4436965e-7,5.343945e-7,0.0012846787,0.95744777,0.00091495446,0.0015414525,7.092539e-7,0.038521882],"study_design_scores_gemma":[0.00065111043,0.000027139466,0.009101584,0.00012246368,9.1529637e-7,0.00006490186,0.000058126087,0.98928463,0.00046874094,0.00006132083,0.000053474112,0.000105606945],"about_ca_topic_score_codex":7.6002954e-7,"about_ca_topic_score_gemma":1.9418023e-7,"teacher_disagreement_score":0.43262172,"about_ca_system_score_codex":0.000104509505,"about_ca_system_score_gemma":0.00008691456,"threshold_uncertainty_score":0.4550415},"labels":[],"label_agreement":null},{"id":"W2018636595","doi":"10.1007/s00158-004-0396-x","title":"Optimization of the new Saab 9-3 exposed to impact load using a space mapping technique","year":2004,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nordic Life Science Pipeline (Canada)","funders":"Saab; Stiftelsen för Strategisk Forskning","keywords":"Crashworthiness; Space mapping; Surrogate model; Mathematical optimization; Finite element method; Computer science; Surface (topology); Space (punctuation); Optimization problem; Response surface methodology; Algorithm; Mathematics; Engineering; Structural engineering; Machine learning","score_opus":0.016611778548438445,"score_gpt":0.28438292930843856,"score_spread":0.26777115076000013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018636595","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":[0.018292109,0.00006593311,0.97944206,0.0006112541,0.00028831416,0.0011135619,0.000009880925,0.00012438498,0.000052529565],"genre_scores_gemma":[0.23347713,0.000016270551,0.76634055,0.000029139328,0.00004847724,0.000012565739,0.000007912399,0.000020544801,0.0000473927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983547,0.000068053145,0.00039197906,0.0005294526,0.00034960755,0.0003062437],"domain_scores_gemma":[0.9986543,0.00003230035,0.00030643828,0.00048038855,0.0003423856,0.00018419702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001317893,0.0002943176,0.00026122204,0.00019378052,0.00045529785,0.00009331973,0.0004628614,0.00011279208,0.000013606829],"category_scores_gemma":[0.00011615421,0.00021638155,0.00009877311,0.0013007901,0.00008811232,0.0010358193,0.00057754765,0.0001444009,6.4984727e-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":[0.000023587272,0.00001423864,0.00012278998,0.000015564246,0.000012952573,0.0000014709286,0.0023017193,0.9856337,0.010459489,0.00039900574,0.0000025594181,0.0010129742],"study_design_scores_gemma":[0.0009796248,0.00010283162,0.0013641414,0.00011056019,0.000015940954,0.000073445895,0.0001583294,0.98416007,0.01166568,0.0010762223,0.000002281631,0.0002908505],"about_ca_topic_score_codex":0.00010607687,"about_ca_topic_score_gemma":0.000007674587,"teacher_disagreement_score":0.21518503,"about_ca_system_score_codex":0.0004168302,"about_ca_system_score_gemma":0.00036868246,"threshold_uncertainty_score":0.88237834},"labels":[],"label_agreement":null},{"id":"W2019722917","doi":"10.1115/detc2010-28355","title":"Constraint Importance Mode Pursuing Sampling for Continuous Global Optimization","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; Simon Fraser University","funders":"","keywords":"Mathematical optimization; Sampling (signal processing); Computer science; Constraint (computer-aided design); Metamodeling; Global optimization; Mode (computer interface); Sample (material); Optimization problem; Constrained optimization; Computation; Domain (mathematical analysis); Function (biology); Algorithm; Mathematics","score_opus":0.016696692885559673,"score_gpt":0.3089574854628433,"score_spread":0.2922607925772836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019722917","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":[0.0005914339,0.000012034927,0.9942829,0.00033581225,0.0007585348,0.00053373683,0.00002180132,0.0004370069,0.003026729],"genre_scores_gemma":[0.069365636,0.0000034174618,0.93005097,0.0003050754,0.00008422706,0.000055846675,0.000011333238,0.000012990122,0.00011053176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986569,0.00001368592,0.00030054353,0.00050745247,0.00017939137,0.0003420007],"domain_scores_gemma":[0.9987831,0.000112115886,0.00016690436,0.00042129375,0.00039425772,0.00012233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018963503,0.00017078801,0.00018403083,0.000046819263,0.00018101683,0.00019428419,0.0005040071,0.000091817354,0.00003941101],"category_scores_gemma":[0.00030357018,0.00016978417,0.00006838343,0.00029998037,0.000079048164,0.0007308556,0.00010962681,0.00012773357,0.000005381769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006549312,0.00004655412,0.0009735967,0.0000050807457,0.000012440277,0.0000021880503,0.0000776181,0.7944892,0.00072140084,0.17237212,0.00005018779,0.031243056],"study_design_scores_gemma":[0.00064110936,0.000028798022,0.00015744114,0.0000049651976,0.0000039612983,0.00003176669,0.00004212706,0.9931478,0.0007084579,0.004715343,0.00029322258,0.00022500523],"about_ca_topic_score_codex":0.000010917032,"about_ca_topic_score_gemma":0.00004027157,"teacher_disagreement_score":0.1986586,"about_ca_system_score_codex":0.00007518705,"about_ca_system_score_gemma":0.000098979996,"threshold_uncertainty_score":0.6923597},"labels":[],"label_agreement":null},{"id":"W2020700108","doi":"10.1115/1.4001599","title":"On the Performance of the PSP Method for Mixed-Variable Multi-Objective Design Optimization","year":2010,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Benchmark (surveying); Robustness (evolution); Multi-objective optimization; Black box; Pareto principle; Computer science; Closeness; Continuous variable; Continuous optimization; Variable (mathematics); Engineering design process; Set (abstract data type); Optimization problem; Mathematics; Engineering; Artificial intelligence; Multi-swarm optimization","score_opus":0.03057773855711829,"score_gpt":0.28751245423751787,"score_spread":0.2569347156803996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020700108","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":[0.00010532101,0.000013099069,0.99720037,0.00053218927,0.0012760188,0.0008270093,0.00000275984,0.000020712225,0.000022505501],"genre_scores_gemma":[0.01954747,0.000015435851,0.9798803,0.00032839004,0.00008598886,0.000036085097,1.8466146e-7,0.000022716098,0.00008344471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99761033,0.0007561009,0.00060366833,0.00026254286,0.0005093002,0.00025807158],"domain_scores_gemma":[0.99384874,0.0034977796,0.0009358489,0.00053409836,0.0010899584,0.000093593895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004007036,0.00019498951,0.0003196895,0.00011386988,0.00027814516,0.00006924519,0.0014361505,0.00014453505,0.000024089266],"category_scores_gemma":[0.0031148838,0.00010793019,0.00018125269,0.00061800674,0.000058934693,0.0005132281,0.00013150802,0.00060722034,0.0000021054982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013699432,0.00016388224,8.640542e-7,0.000005733873,0.00004232042,7.5120806e-7,0.00010583633,0.9460875,0.019710192,0.02948923,0.00020704666,0.0040496434],"study_design_scores_gemma":[0.00087294413,0.0005678565,0.000016391488,0.00004125018,0.000025635589,0.000044569184,0.000019163828,0.82008874,0.1720121,0.0061660693,0.000038369435,0.000106898435],"about_ca_topic_score_codex":0.000001543174,"about_ca_topic_score_gemma":5.222758e-7,"teacher_disagreement_score":0.15230192,"about_ca_system_score_codex":0.00007804413,"about_ca_system_score_gemma":0.00030173152,"threshold_uncertainty_score":0.44012654},"labels":[],"label_agreement":null},{"id":"W2020821169","doi":"10.1016/j.envsoft.2011.09.010","title":"Numerical assessment of metamodelling strategies in computationally intensive optimization","year":2011,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":139,"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":"Metamodeling; Computer science; Mathematical optimization; Optimization problem; Emulation; Kriging; Function (biology); Machine learning; Mathematics; Algorithm","score_opus":0.029692641945791635,"score_gpt":0.2555625978735644,"score_spread":0.22586995592777276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020821169","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":[0.003104599,0.00008108815,0.9962049,0.00001243308,0.00010033415,0.0002424787,0.0000071370687,0.000094403666,0.00015258785],"genre_scores_gemma":[0.4671539,0.00003361629,0.5327172,0.000037474583,0.000005395902,0.000012769648,0.000019816714,0.000013420177,0.0000063845973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838567,0.00007966497,0.00045982414,0.00048404717,0.00035687294,0.00023394886],"domain_scores_gemma":[0.999246,0.00009854058,0.00023307792,0.00028322684,0.000068911475,0.000070245726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001390544,0.00020828054,0.00026633023,0.00016561887,0.00007327574,0.000032380864,0.00038638507,0.000070180424,0.000045397606],"category_scores_gemma":[0.000013136476,0.00022364393,0.000071868126,0.00022581697,0.00009051333,0.0010263784,0.00013271575,0.00019096097,0.0000056134363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006670843,0.0001684538,0.0017473346,0.0000069751313,0.000016694186,0.000008392676,0.0015399153,0.99331486,0.000021421212,0.002085813,3.5807471e-7,0.0010831075],"study_design_scores_gemma":[0.00037891127,0.00006745938,0.001608474,0.000026644751,0.0000069375856,0.0000057290135,0.0003528583,0.9931415,0.00027149334,0.003923598,0.0000017420754,0.00021465731],"about_ca_topic_score_codex":0.000035850175,"about_ca_topic_score_gemma":6.3726037e-7,"teacher_disagreement_score":0.4640493,"about_ca_system_score_codex":0.0001819471,"about_ca_system_score_gemma":0.00006394096,"threshold_uncertainty_score":0.9119935},"labels":[],"label_agreement":null},{"id":"W2021601290","doi":"10.1007/s10898-011-9732-z","title":"An experimental methodology for response surface optimization methods","year":2011,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"University of Alberta","funders":"","keywords":"Mathematics; Mathematical optimization; Gaussian process; Global optimization; Gaussian; Set (abstract data type); Function (biology); Dimension (graph theory); Test functions for optimization; Scaling; Surface (topology); Optimization problem; Algorithm; Computer science; Multi-swarm optimization","score_opus":0.09512033817134276,"score_gpt":0.41913846528555154,"score_spread":0.3240181271142088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021601290","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":[0.0008555923,0.00021395716,0.9970936,0.00013697085,0.001067607,0.00035569334,0.000008274096,0.00008967541,0.00017859043],"genre_scores_gemma":[0.012530423,0.000030782736,0.9871235,0.00018053308,0.00007544477,0.000008376967,0.000005888873,0.000024204353,0.000020868241],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966507,0.0016180591,0.00077162887,0.00037218825,0.00028962025,0.0002978116],"domain_scores_gemma":[0.996714,0.0003687977,0.0009913543,0.00040312106,0.0013008792,0.00022186311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027925048,0.00023485957,0.00039855187,0.0001412109,0.0001647279,0.0000991649,0.0008133389,0.0001654262,0.000056612735],"category_scores_gemma":[0.0009168217,0.00022586554,0.00015579294,0.0007355941,0.00007372097,0.002492047,0.00009118057,0.00012304557,0.0000013477587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010337126,0.00027336346,0.000057100424,0.0000028678064,0.00003515154,0.000007744018,0.0006826948,0.990315,0.0024390675,0.002560806,0.000031788553,0.0025606954],"study_design_scores_gemma":[0.0015108828,0.0012973794,0.00012738802,0.000011515057,0.000027921633,0.00019775105,0.000254233,0.9747539,0.020811623,0.0007052987,0.00006889735,0.00023322327],"about_ca_topic_score_codex":0.000005097842,"about_ca_topic_score_gemma":4.7101196e-7,"teacher_disagreement_score":0.018372556,"about_ca_system_score_codex":0.00042685005,"about_ca_system_score_gemma":0.0002404446,"threshold_uncertainty_score":0.92105293},"labels":[],"label_agreement":null},{"id":"W2021984249","doi":"10.1142/s0129626407003034","title":"COMPARING MINIMUM NEIGHBORHOOD EVALUATION SCHEMES FOR FINDING SPATIALLY ROBUST SOLUTIONS","year":2007,"lang":"en","type":"article","venue":"Parallel Processing Letters","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Science Council","keywords":"Robustness (evolution); Fitness function; Mathematical optimization; Neighbourhood (mathematics); Computer science; Algorithm; Mathematics; Genetic algorithm","score_opus":0.08517098334736357,"score_gpt":0.3201502356648206,"score_spread":0.23497925231745703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021984249","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":[0.0025825673,0.00018710936,0.99368423,0.0017057031,0.00036342462,0.00065050053,0.0000011325873,0.00028467516,0.00054065493],"genre_scores_gemma":[0.42699286,0.0000016683252,0.5721979,0.00055304554,0.0001253642,0.000068743146,0.000015712152,0.000019340376,0.000025356649],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776924,0.00004964727,0.00042867588,0.00060483266,0.00049195485,0.0006556584],"domain_scores_gemma":[0.9986734,0.0001808428,0.00031995165,0.00030916318,0.00040285193,0.00011380379],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011661847,0.00023493315,0.000225791,0.00025643257,0.0007150995,0.00029796181,0.0005552707,0.00007148706,0.000006286121],"category_scores_gemma":[0.00028074635,0.00025535835,0.0000812293,0.0005390343,0.00007866017,0.001270179,0.00014156858,0.0001670365,0.000012527869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038242306,0.00013063895,0.0019461364,0.000075932294,0.000040568037,0.000005047578,0.0015012397,0.870895,0.005394638,0.002522692,0.00033321392,0.11711662],"study_design_scores_gemma":[0.0013111786,0.00002427762,0.0014097065,0.00006286675,0.000023179051,0.000007766935,0.00005594665,0.9951935,0.00072915637,0.00057338347,0.00028138407,0.0003276709],"about_ca_topic_score_codex":0.000007063233,"about_ca_topic_score_gemma":0.000015254031,"teacher_disagreement_score":0.4244103,"about_ca_system_score_codex":0.00027991898,"about_ca_system_score_gemma":0.00015464854,"threshold_uncertainty_score":0.99998987},"labels":[],"label_agreement":null},{"id":"W2023062871","doi":"10.1108/ijicc-07-2014-0034","title":"Simultaneous knowledge-based identification and optimization of PHEV fuel economy using hyper-level Pareto-based chaotic Lamarckian immune algorithm, MSBA and fuzzy programming","year":2015,"lang":"en","type":"article","venue":"International Journal of Intelligent Computing and Cybernetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Identifier; Identification (biology); Fuzzy logic; Exploit; Pareto principle; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.03317434205134592,"score_gpt":0.29949965574047865,"score_spread":0.2663253136891327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023062871","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":[0.0194025,0.0013510815,0.9783021,0.00019425165,0.00051435264,0.00016360205,0.000005012473,0.000024331337,0.000042729174],"genre_scores_gemma":[0.54225886,0.000058253114,0.45755535,0.00003247992,0.00006776071,6.968568e-7,0.0000054624525,0.000011508313,0.000009598128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826425,0.00010775257,0.0008342568,0.00028458735,0.0003333864,0.00017577446],"domain_scores_gemma":[0.99628896,0.0003714537,0.00090389996,0.00014991479,0.0021159814,0.00016978639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061448844,0.0001975195,0.0002826901,0.0003484951,0.000074913136,0.00026360634,0.00039515388,0.00007366115,0.0000014042537],"category_scores_gemma":[0.000360154,0.00019898382,0.00005765603,0.00020134977,0.00016023437,0.00028202214,0.00016492751,0.00017679775,6.33633e-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":[0.000022473725,0.00013501885,0.0006540646,0.000024836112,0.000052255677,0.000011753078,0.00097180007,0.758322,0.00006545969,0.00032640653,0.00000120628,0.23941271],"study_design_scores_gemma":[0.0009620437,0.00017016167,0.00022299538,0.00024002513,0.000028074714,0.00016415321,0.00029067026,0.99434024,0.0027144426,0.0005383251,0.00014436683,0.00018452745],"about_ca_topic_score_codex":0.000018951843,"about_ca_topic_score_gemma":0.0000025589623,"teacher_disagreement_score":0.52285635,"about_ca_system_score_codex":0.00015469542,"about_ca_system_score_gemma":0.00020387108,"threshold_uncertainty_score":0.8114324},"labels":[],"label_agreement":null},{"id":"W2023835690","doi":"10.1080/03052150108940935","title":"MULTIOBJECTIVE DESIGN OPTIMIZATION BASED ON SATISFACTION METRICS","year":2001,"lang":"en","type":"article","venue":"Engineering Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Multi-objective optimization; Solver; Computer science; Pareto principle; Mathematical optimization; Context (archaeology); Implementation; Constraint satisfaction problem; Engineering design process; Mathematics; Engineering; Software engineering; Artificial intelligence; Probabilistic logic","score_opus":0.014408971302169258,"score_gpt":0.23113274333323688,"score_spread":0.21672377203106763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023835690","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":[0.000008186896,0.000028902175,0.99695915,0.00016241075,0.0006756726,0.0005986712,0.0000028639631,0.0010459718,0.0005181738],"genre_scores_gemma":[0.04135258,0.000069550406,0.9580097,0.0002012948,0.000101703394,0.00009468652,0.00003519913,0.00006161308,0.00007364318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807274,0.00010832089,0.0003489581,0.000627091,0.00047571812,0.0003671947],"domain_scores_gemma":[0.99841106,0.00034614207,0.00018553091,0.00053145905,0.00038815677,0.0001376373],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030799827,0.00032987611,0.00022240928,0.0009073958,0.00020025554,0.00019481825,0.00031906087,0.00015449362,0.000052688272],"category_scores_gemma":[0.00071795395,0.00037041004,0.00007170517,0.0025745437,0.000018685474,0.0012684626,0.00005301294,0.00022249503,0.000023134251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021331984,0.0000652823,0.0000821086,0.0000049237988,0.000012660037,0.000007936885,0.00007497412,0.9934859,0.000025074913,0.00039162894,0.00003013217,0.005798005],"study_design_scores_gemma":[0.0009301469,0.00013710136,0.00032750927,0.000027885424,0.000010197121,0.000011357562,0.0000075459393,0.9975854,0.0004609743,0.00001296705,0.00010004466,0.00038884766],"about_ca_topic_score_codex":0.00000965456,"about_ca_topic_score_gemma":4.1383254e-7,"teacher_disagreement_score":0.041344393,"about_ca_system_score_codex":0.0004932303,"about_ca_system_score_gemma":0.000073264346,"threshold_uncertainty_score":0.9998748},"labels":[],"label_agreement":null},{"id":"W2024598862","doi":"10.1007/s00158-011-0661-8","title":"Application of SEUMRE global optimization algorithm in automotive magnetorheological brake design","year":2011,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","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":"Simon Fraser University; University of Victoria","funders":"","keywords":"Magnetorheological fluid; Brake; Simulated annealing; Automotive industry; Global optimization; Torque; Genetic algorithm; Computation; Computer science; Automotive engineering; Engineering design process; Control theory (sociology); Algorithm; Engineering; Control engineering; Mechanical engineering","score_opus":0.01839155456451052,"score_gpt":0.26087887969492024,"score_spread":0.24248732513040971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024598862","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":[0.0012806697,0.00010471801,0.9970698,0.00006860896,0.00020644217,0.0007868276,0.00002309974,0.00017325739,0.000286612],"genre_scores_gemma":[0.14923008,0.00006864201,0.85051286,0.00002133781,0.000024198012,0.00004828571,0.000058741156,0.000012855874,0.00002297917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980535,0.0001836562,0.0005194853,0.00069623545,0.00023777815,0.00030937942],"domain_scores_gemma":[0.9988052,0.00006199753,0.00032073376,0.00032801495,0.00036604502,0.00011800802],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019379903,0.00029157382,0.00031246743,0.00017322534,0.00018968857,0.000034593675,0.00039105292,0.0001813572,0.000033842654],"category_scores_gemma":[0.00006952136,0.00026339796,0.0000498496,0.0009390709,0.00019401967,0.0011928425,0.00033748802,0.00013412947,0.0000016921239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046862708,0.000063532505,0.0018817659,0.0000142362205,0.000009802921,0.0000062017803,0.0010491435,0.96074885,0.000024552013,0.002452364,0.0000012581551,0.03370142],"study_design_scores_gemma":[0.0009657044,0.00022726766,0.027952114,0.000015980138,0.000015158337,0.000040865092,0.0001519626,0.96691054,0.0002709313,0.0031659089,5.740179e-7,0.00028296397],"about_ca_topic_score_codex":0.000050695813,"about_ca_topic_score_gemma":0.000005717217,"teacher_disagreement_score":0.14794941,"about_ca_system_score_codex":0.0001292625,"about_ca_system_score_gemma":0.000058999798,"threshold_uncertainty_score":0.9999818},"labels":[],"label_agreement":null},{"id":"W2025463696","doi":"10.1109/mwsym.2007.380266","title":"Coarse and Surrogate Model Assessment for Engineering Design Optimization with Space Mapping","year":2007,"lang":"en","type":"article","venue":"IEEE MTT-S International Microwave Symposium digest","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Surrogate model; Space mapping; Computer science; Space (punctuation); Systems engineering; Reliability engineering; Engineering; Machine learning; Algorithm","score_opus":0.015771252785468145,"score_gpt":0.2575394444583826,"score_spread":0.24176819167291447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025463696","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":[0.0006352318,0.00001850769,0.99593633,0.00085933745,0.0006586448,0.0006236751,0.000016646662,0.00017784578,0.0010737567],"genre_scores_gemma":[0.117319465,0.00004443457,0.8819138,0.00013350084,0.00009412879,0.000054956476,0.000024682315,0.000041193754,0.00037387013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838233,0.000021734377,0.00032872817,0.000580914,0.0003178055,0.00036851713],"domain_scores_gemma":[0.9985921,0.00027929092,0.00020696275,0.00025651272,0.00053411175,0.00013102782],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004456382,0.0002740799,0.00019440854,0.00031267773,0.0001399519,0.00024861665,0.00044950997,0.00008053837,0.0000017253609],"category_scores_gemma":[0.000038408307,0.00027763384,0.000048247133,0.00025159182,0.0000548696,0.0008979537,0.00010429478,0.00014675011,0.0000020277455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032233213,0.00005928078,0.00008598073,0.000011656726,0.000051750525,0.00001104678,0.00031092562,0.93180865,0.060641903,0.0061610984,0.000020069425,0.0008053969],"study_design_scores_gemma":[0.0010674786,0.0000743545,0.00006854574,0.00005919369,0.00000970003,0.00006223112,0.000031214757,0.95953816,0.038356323,0.0001291693,0.00028222013,0.0003214265],"about_ca_topic_score_codex":0.0000070161464,"about_ca_topic_score_gemma":0.0000068006866,"teacher_disagreement_score":0.116684236,"about_ca_system_score_codex":0.00033864085,"about_ca_system_score_gemma":0.00010477965,"threshold_uncertainty_score":0.9999676},"labels":[],"label_agreement":null},{"id":"W2026717679","doi":"10.2514/1.j052180","title":"Development of a Multilevel Multidisciplinary-Optimization Capability for an Industrial Environment","year":2013,"lang":"en","type":"article","venue":"AIAA Journal","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Multidisciplinary design optimization; Multidisciplinary approach; Systems engineering; Software deployment; Engineering optimization; Computer science; Frame (networking); Industrial engineering; Engineering; Optimization problem; Software engineering; Mechanical engineering","score_opus":0.05361191833676478,"score_gpt":0.28441759231733366,"score_spread":0.2308056739805689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026717679","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":[0.018350553,0.000010335028,0.9804729,0.00013889672,0.00029444345,0.0006629424,0.000004286596,0.000032426324,0.00003321874],"genre_scores_gemma":[0.093710445,0.00000610957,0.9060187,0.000020285606,0.00009534064,0.00008003634,0.000007872496,0.000014145802,0.00004707121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983918,0.00008558389,0.00060162996,0.00032833611,0.00031673672,0.00027589992],"domain_scores_gemma":[0.9986867,0.00009303443,0.00040878195,0.00031459698,0.00029311466,0.0002037915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004640593,0.00016513924,0.00020184852,0.0001249276,0.00032116228,0.000091768234,0.00049592066,0.000095262665,0.0001254256],"category_scores_gemma":[0.00012577367,0.00014874092,0.00006734712,0.00011738105,0.000060790517,0.0013645143,0.0001644083,0.00018579833,0.000011746934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023973784,0.00048806114,0.0004825785,0.0000071156514,0.000034092933,8.4782306e-7,0.0036366365,0.74130553,0.0026509634,0.00017645006,0.000051305644,0.25114247],"study_design_scores_gemma":[0.0017920631,0.00016470095,0.00279437,0.000015369798,0.0000055179835,0.000020628479,0.00019575018,0.9897982,0.0040496658,0.0007103703,0.00025351805,0.00019983818],"about_ca_topic_score_codex":0.0000056411695,"about_ca_topic_score_gemma":0.000001618933,"teacher_disagreement_score":0.25094262,"about_ca_system_score_codex":0.0002312435,"about_ca_system_score_gemma":0.00020797929,"threshold_uncertainty_score":0.60654783},"labels":[],"label_agreement":null},{"id":"W2030030592","doi":"10.1115/interpack2009-89341","title":"Multi-Objective Optimization of Graphite Heat Spreader for Portable Systems Applications","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Advanced Micro Devices (Canada)","funders":"","keywords":"Graphite; Heat sink; Heat spreader; Miniaturization; Thermal conductivity; Materials science; Heat transfer; Electronics; Mechanical engineering; Power density; Process engineering; Nuclear engineering; Computer science; Electrical engineering; Power (physics); Composite material; Nanotechnology; Engineering; Mechanics; Thermodynamics; Physics","score_opus":0.01652862787372476,"score_gpt":0.2753230982392943,"score_spread":0.2587944703655695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030030592","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":[0.00000977025,0.00017281949,0.9961725,0.00013389582,0.00012656483,0.0017669438,0.00001913255,0.00026753702,0.0013308292],"genre_scores_gemma":[0.037013024,0.000033813612,0.9615425,0.00014400874,0.000040563616,0.0003425693,0.000030138723,0.000015443877,0.0008379721],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985935,0.000034246117,0.00040275638,0.0005014821,0.00020009227,0.00026793065],"domain_scores_gemma":[0.9983703,0.00009614599,0.00016933371,0.00054979924,0.0007203903,0.00009404068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016123118,0.00018086933,0.0002597228,0.00025164045,0.00015112998,0.00006095147,0.00043397574,0.00008320694,0.0000066749694],"category_scores_gemma":[0.000043742417,0.00017331145,0.00009084137,0.00090118963,0.000045857967,0.00077386154,0.00004233766,0.000065608125,0.000004017306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059741665,0.000193491,0.00004682333,0.000012932483,0.000017430382,2.886644e-7,0.0001458042,0.9685611,0.0010477713,0.028159901,0.000053188734,0.001755302],"study_design_scores_gemma":[0.0008125248,0.000104387815,0.00031058586,0.000013818619,0.000010493782,0.0000069412267,0.00010541667,0.99505585,0.0024089995,0.00055412704,0.00041486093,0.00020196263],"about_ca_topic_score_codex":0.000025134426,"about_ca_topic_score_gemma":0.0000020067994,"teacher_disagreement_score":0.037003253,"about_ca_system_score_codex":0.0000637338,"about_ca_system_score_gemma":0.000059126316,"threshold_uncertainty_score":0.7067436},"labels":[],"label_agreement":null},{"id":"W2031907338","doi":"10.1115/1.4003840","title":"Integrating Least Square Support Vector Regression and Mode Pursuing Sampling Optimization for Crashworthiness Design","year":2011,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Manitoba","funders":"","keywords":"Crashworthiness; Metamodeling; Support vector machine; Kriging; Engineering; Multivariate adaptive regression splines; Computer science; Radial basis function; Artificial neural network; Mathematical optimization; Polynomial regression; Machine learning; Regression analysis; Mathematics; Structural engineering; Finite element method","score_opus":0.10116708821314256,"score_gpt":0.3205314552529235,"score_spread":0.2193643670397809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031907338","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":[0.000072814924,0.0000843675,0.99871814,0.00011457581,0.00049269974,0.00044118825,0.0000018758387,0.00006048872,0.000013837133],"genre_scores_gemma":[0.06650437,0.000046494893,0.93321484,0.000078132405,0.00008995557,0.00001738086,0.000001014295,0.000030561565,0.000017236724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980372,0.00025915165,0.0006994893,0.00034323212,0.00036915514,0.00029172917],"domain_scores_gemma":[0.99741566,0.0005938345,0.0007669606,0.00022446734,0.0008220683,0.00017697985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014552639,0.00023058022,0.00037984265,0.00021243146,0.00021946567,0.00013502497,0.0005304269,0.00013640206,0.000024715466],"category_scores_gemma":[0.0012229374,0.00017989495,0.000108818924,0.00031546305,0.000029148194,0.0013047565,0.00011387504,0.0002937366,9.940536e-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":[0.00042160772,0.00019337704,0.000014496792,0.000039767674,0.000055860517,0.00004121805,0.0016721082,0.8860436,0.0063751144,0.006196223,0.00006676307,0.09887984],"study_design_scores_gemma":[0.0009251398,0.00070712104,0.000024786685,0.00021472327,0.000027285618,0.00017170231,0.00011279552,0.97735,0.01395732,0.006291757,0.000009342819,0.0002080224],"about_ca_topic_score_codex":0.0000025697593,"about_ca_topic_score_gemma":5.5478324e-7,"teacher_disagreement_score":0.09867182,"about_ca_system_score_codex":0.000106548745,"about_ca_system_score_gemma":0.00015851314,"threshold_uncertainty_score":0.73359036},"labels":[],"label_agreement":null},{"id":"W2034562492","doi":"10.1080/03052150903325540","title":"Metamodelling and search using space exploration and unimodal region elimination for design optimization","year":2010,"lang":"en","type":"article","venue":"Engineering Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Metamodeling; Maxima and minima; Benchmark (surveying); Mathematical optimization; Computation; Reduction (mathematics); Global optimization; Kriging; Computer science; Latin hypercube sampling; Dimensionality reduction; Design space exploration; Subspace topology; Mathematics; Algorithm; Artificial intelligence; Machine learning","score_opus":0.04073800279990948,"score_gpt":0.26091610046440744,"score_spread":0.22017809766449797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034562492","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":[0.0003649015,0.000071167626,0.99780464,0.00051868317,0.00034093013,0.0006571095,0.0000014217416,0.00023291339,0.000008203691],"genre_scores_gemma":[0.033307485,0.00014113187,0.9663049,0.000018607907,0.00008350863,0.000050128136,0.00002459067,0.000040349867,0.000029309056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989113,0.000046873633,0.00022182017,0.0004296314,0.00016392743,0.00022644197],"domain_scores_gemma":[0.99906856,0.00018525637,0.00007554525,0.0002104233,0.00036377038,0.00009646869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036349014,0.00019215903,0.00015068766,0.0003035904,0.00022477738,0.00022476066,0.0001263882,0.00012812033,0.0000016006322],"category_scores_gemma":[0.00024162405,0.00022371972,0.000022661041,0.00044440263,0.00003557226,0.0024054209,0.000065972374,0.00015232948,2.5494512e-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":[0.000008294494,0.00001461674,0.000004976284,0.000022842138,0.000008080506,7.2480907e-7,0.00034982897,0.97922873,0.0018833322,0.015959717,0.0000018267689,0.0025170103],"study_design_scores_gemma":[0.0004902396,0.000049378636,0.000010922873,0.000021479995,0.000016450853,0.000027684247,0.000026275071,0.9957546,0.0031415573,0.00020613322,0.000021279237,0.00023400944],"about_ca_topic_score_codex":0.0000053959184,"about_ca_topic_score_gemma":5.113367e-7,"teacher_disagreement_score":0.032942582,"about_ca_system_score_codex":0.00006768169,"about_ca_system_score_gemma":0.000041073592,"threshold_uncertainty_score":0.9123025},"labels":[],"label_agreement":null},{"id":"W2034606896","doi":"10.1115/detc2010-29030","title":"Improving Multi-Response Metamodels With Upper/Lower Bound Information Using Multi-Stage, Non-Stationary Covariance Functions","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Pennsylvania Department of Community and Economic Development","keywords":"Metamodeling; Computer science; Covariance; A priori and a posteriori; Upper and lower bounds; Context (archaeology); Mathematical optimization; Design of experiments; Algorithm; Mathematics; Statistics","score_opus":0.025827108999124757,"score_gpt":0.2876470708421782,"score_spread":0.26181996184305345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034606896","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":[0.014745483,0.000006631313,0.98302233,0.00013357865,0.0008875803,0.00065240293,0.000033097996,0.00037070015,0.00014818386],"genre_scores_gemma":[0.08445227,0.0000014893166,0.91269684,0.00044304255,0.000033335455,0.00006737091,0.000022760705,0.00002728742,0.0022555937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807113,0.00009427267,0.00046385566,0.00051914196,0.00041830447,0.00043329742],"domain_scores_gemma":[0.9975752,0.0002220019,0.0003462528,0.00077330944,0.000894792,0.00018842182],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00054162025,0.00032095687,0.000229901,0.00039363554,0.0006416211,0.0004687893,0.00052810507,0.00013013216,0.00008305409],"category_scores_gemma":[0.0003130052,0.0002825175,0.000074441734,0.000886685,0.00014244436,0.008357224,0.00021819642,0.0004167003,0.00009838116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003149955,0.00039039453,0.00046915753,0.000023899633,0.000073727366,0.000018144312,0.0016407213,0.9484738,0.03224749,0.0026354806,0.000026299087,0.0136858905],"study_design_scores_gemma":[0.0019788137,0.00008032127,0.0032439132,0.000010327951,0.000013932533,0.000044153017,0.00020655534,0.99148196,0.0016468748,0.000016929145,0.0008635975,0.00041262453],"about_ca_topic_score_codex":0.00017335222,"about_ca_topic_score_gemma":0.00010653862,"teacher_disagreement_score":0.070325494,"about_ca_system_score_codex":0.00014932344,"about_ca_system_score_gemma":0.0004919763,"threshold_uncertainty_score":0.9999627},"labels":[],"label_agreement":null},{"id":"W2036348784","doi":"10.1243/09544054jem1482","title":"Robust design of finned-tube evaporators: A scalable product platform approach","year":2009,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 Windsor","funders":"","keywords":"Evaporator; Maximization; Minification; Scalability; Product design; Computer science; Compromise; Product (mathematics); Industrial engineering; Mathematical optimization; Reliability engineering; Engineering; Mathematics; Mechanical engineering","score_opus":0.020883981296362406,"score_gpt":0.208919073836967,"score_spread":0.1880350925406046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036348784","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":[0.0049068537,0.0001784775,0.99376446,0.00021103816,0.0004830779,0.00032906246,0.0000037271766,0.00004792638,0.00007540356],"genre_scores_gemma":[0.57642376,0.00003338741,0.42342085,0.000016353826,0.0000805697,0.0000027260255,4.7912846e-7,0.000010685068,0.000011216924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979138,0.000008596849,0.0008869123,0.00024517745,0.0006831539,0.00026238878],"domain_scores_gemma":[0.99778664,0.000057210567,0.0009139655,0.00022751212,0.00089035474,0.00012430143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008244613,0.00026703585,0.0005530044,0.00031221923,0.000051768275,0.000028299928,0.0011042291,0.00014280876,0.0000032208675],"category_scores_gemma":[0.0008477213,0.0001960093,0.00021311261,0.00080159254,0.000058796693,0.0010775824,0.00010262479,0.00048676343,2.9878126e-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":[0.000041082254,0.00014091007,0.0000018952788,0.00011809149,0.00006117694,8.875381e-7,0.00014259337,0.95783854,0.025020793,0.015842237,0.00015534295,0.00063647184],"study_design_scores_gemma":[0.00063310866,0.00028465173,0.000049479466,0.00030725455,0.00004254358,0.00009175442,0.000033047778,0.5399697,0.4575332,0.0006745747,0.00020008357,0.00018062771],"about_ca_topic_score_codex":7.6692726e-7,"about_ca_topic_score_gemma":1.678544e-8,"teacher_disagreement_score":0.5715169,"about_ca_system_score_codex":0.000117825526,"about_ca_system_score_gemma":0.00012568511,"threshold_uncertainty_score":0.79930276},"labels":[],"label_agreement":null},{"id":"W2036627938","doi":"10.1007/s00158-014-1086-y","title":"Reallocation of testing resources in validating optimal designs using local domains","year":2014,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Domain (mathematical analysis); Process (computing); Engineering design process; Computer science; Set (abstract data type); Sequential analysis; Mathematical optimization; Design of experiments; Sizing; Mathematics; Engineering; Statistics","score_opus":0.029034778066000903,"score_gpt":0.2877439807964628,"score_spread":0.2587092027304619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036627938","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":[0.18760297,0.000030128514,0.8118295,0.000040482493,0.00009206036,0.00020809824,0.0000021264007,0.00006098063,0.0001336243],"genre_scores_gemma":[0.48414955,0.000005110831,0.5157883,0.0000056719146,0.000024036764,0.000003989377,0.0000070095525,0.000008978612,0.0000073272467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847436,0.00016405035,0.00044504265,0.00045908664,0.00020803833,0.000249438],"domain_scores_gemma":[0.99895275,0.00021501583,0.00030573577,0.00024166922,0.00020918284,0.0000756765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027549814,0.00019544309,0.00023145608,0.00022753292,0.0002946039,0.00006170975,0.00024235903,0.00008423487,0.000004424713],"category_scores_gemma":[0.00021589146,0.00018434811,0.000027774386,0.00071639,0.00014587745,0.00092952413,0.00027076824,0.000120812,3.6208172e-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":[0.000010413216,0.000013655375,0.00162978,0.000028661216,0.000003815577,0.0000014023678,0.0016858969,0.9650621,0.0017754203,0.00069690356,2.1568569e-7,0.02909173],"study_design_scores_gemma":[0.00068525365,0.00011735583,0.004525114,0.00006924635,0.000010162412,0.000030371808,0.00033342533,0.9924748,0.0010171075,0.0005230371,6.6598545e-7,0.00021344058],"about_ca_topic_score_codex":0.000059062328,"about_ca_topic_score_gemma":0.000007855572,"teacher_disagreement_score":0.29654658,"about_ca_system_score_codex":0.00008319,"about_ca_system_score_gemma":0.000023411385,"threshold_uncertainty_score":0.7517498},"labels":[],"label_agreement":null},{"id":"W2037704679","doi":"10.1109/icsm.2007.4362625","title":"Multi-Objective Genetic Algorithm to Support Class Responsibility Assignment","year":2007,"lang":"en","type":"article","venue":"Proceedings/Proceedings - Conference on Software Maintenance","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Class (philosophy); Computer science; Genetic algorithm; Cohesion (chemistry); Influence diagram; Domain (mathematical analysis); Class diagram; Machine learning; Mathematical optimization; Artificial intelligence; Management science; Algorithm; Decision tree; Mathematics; Engineering; Programming language; Unified Modeling Language","score_opus":0.027895364276304038,"score_gpt":0.2929770724549701,"score_spread":0.2650817081786661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037704679","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":[0.0064685564,0.000024112283,0.9826858,0.0007038672,0.0008069569,0.0021112503,0.000042524716,0.0016882757,0.005468635],"genre_scores_gemma":[0.23196182,0.000029768506,0.7635106,0.0020453334,0.00017685798,0.00031712046,0.000005301225,0.00011233308,0.0018408697],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99113756,0.000025350291,0.0014068385,0.003354056,0.0017354271,0.0023407883],"domain_scores_gemma":[0.99182063,0.00024110878,0.00085120136,0.0006798301,0.0051789386,0.0012282631],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0023860848,0.0011730272,0.0009926729,0.000971355,0.00068746845,0.0008746004,0.0027653947,0.00042794997,0.00008025338],"category_scores_gemma":[0.0030751652,0.0011489826,0.0002659792,0.0027930383,0.00034059703,0.0019493835,0.0011559098,0.0010956888,0.00041000696],"study_design_candidate":"design_other","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":[0.0015773498,0.0029441835,0.0194375,0.00038354093,0.0003314196,0.00036156797,0.01913597,0.00082301314,0.024997141,0.108600534,0.008898348,0.8125094],"study_design_scores_gemma":[0.016912874,0.013661854,0.22478098,0.0025308507,0.00021755093,0.0014167683,0.013535053,0.51437587,0.13261151,0.031326834,0.034984235,0.013645629],"about_ca_topic_score_codex":0.00002314757,"about_ca_topic_score_gemma":0.000009071014,"teacher_disagreement_score":0.7988638,"about_ca_system_score_codex":0.0016594093,"about_ca_system_score_gemma":0.0004578171,"threshold_uncertainty_score":0.99909604},"labels":[],"label_agreement":null},{"id":"W2038557532","doi":"10.1145/1570256.1570387","title":"Improving NSGA-II with an adaptive mutation operator","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Operator (biology); Mutation; Adaptive mutation; Value (mathematics); Multi-objective optimization; Pareto principle","score_opus":0.010122225837221147,"score_gpt":0.2459347438610009,"score_spread":0.23581251802377975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038557532","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":[0.002779728,0.000008739382,0.99413824,0.00022020191,0.00005650218,0.00020772041,7.9402514e-7,0.00033277195,0.0022552928],"genre_scores_gemma":[0.37147817,5.871451e-7,0.62769604,0.00048068858,0.000025371322,0.0000063703765,0.0000021648314,0.000005527921,0.00030511216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905187,0.000037867365,0.00012313132,0.00040365223,0.00019127884,0.00019220788],"domain_scores_gemma":[0.99923366,0.000018685841,0.00006729697,0.00031629737,0.00026083412,0.00010325228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000087352906,0.00012732748,0.00009812678,0.0000805504,0.00021558753,0.000110247005,0.00031113368,0.000032495696,0.000015393227],"category_scores_gemma":[0.00002452986,0.000099554636,0.00001522428,0.00038882624,0.000022611865,0.0021058782,0.00004079403,0.00007985432,0.000013588626],"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":[0.00008201887,0.00037435914,0.00003336631,0.0000022571617,0.000017709064,0.00007313429,0.0029441253,0.22263238,0.0031837546,0.07815712,0.00003586852,0.69246393],"study_design_scores_gemma":[0.000545144,0.0014836978,0.00066637795,0.0000043823525,0.0000023916684,0.00002720248,0.00014514988,0.9887057,0.0075905374,0.0006018239,0.0000315536,0.00019606827],"about_ca_topic_score_codex":0.000020054475,"about_ca_topic_score_gemma":0.000016919757,"teacher_disagreement_score":0.7660733,"about_ca_system_score_codex":0.00006569804,"about_ca_system_score_gemma":0.00007553984,"threshold_uncertainty_score":0.405972},"labels":[],"label_agreement":null},{"id":"W2038744423","doi":"10.1145/2483028.2483115","title":"A self-tuning multi-objective optimization framework for geometric programming with gate sizing applications","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures; Western Canada Research Grid; Compute Canada","keywords":"Geometric programming; Sizing; Skew; Mathematical optimization; Reduction (mathematics); Computer science; Multi-objective optimization; Pareto principle; Optimization problem; Mathematics","score_opus":0.01527717656386362,"score_gpt":0.26962289663514877,"score_spread":0.25434572007128514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038744423","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":[0.00001405089,0.000079172954,0.9942222,0.00019718145,0.00007459304,0.0039220396,0.0000020784728,0.0011576503,0.00033102886],"genre_scores_gemma":[0.008758032,0.000021527632,0.98633283,0.00018535416,0.00006472067,0.0043803887,0.000013592974,0.000052276933,0.00019126832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980419,0.000046181394,0.00032168184,0.0007837952,0.00027500317,0.0005313938],"domain_scores_gemma":[0.99742836,0.0005119317,0.00028237235,0.0005625053,0.0010361444,0.00017868642],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001848361,0.00028101262,0.00024104641,0.000491433,0.0005062046,0.0004572975,0.00058059994,0.00012005471,0.000019412333],"category_scores_gemma":[0.0002218309,0.00024584468,0.0000685411,0.0030232838,0.000051767656,0.0017187584,0.00017210339,0.00020638097,0.000032966855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059022505,0.0003108115,0.00023427248,0.000042743868,0.000080921534,9.848166e-7,0.00090053247,0.83730525,0.000018816649,0.022285938,0.000006730538,0.13880712],"study_design_scores_gemma":[0.00079245743,0.00012896139,0.00013409842,0.000027129592,0.00001612144,0.000010275723,0.0003217796,0.9962248,0.00032006597,0.0010727502,0.00057770597,0.0003738493],"about_ca_topic_score_codex":0.000022911288,"about_ca_topic_score_gemma":0.000003613205,"teacher_disagreement_score":0.15891957,"about_ca_system_score_codex":0.00020105788,"about_ca_system_score_gemma":0.00009654461,"threshold_uncertainty_score":0.9999994},"labels":[],"label_agreement":null},{"id":"W2039188123","doi":"10.6112/kscfe.2012.17.4.032","title":"A STUDY ON CONSTRAINED EGO METHOD FOR NOISY CFD DATA","year":2012,"lang":"en","type":"article","venue":"Journal of computational fluids engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Kriging; Mathematical optimization; Global optimization; Interpolation (computer graphics); Computer science; Function (biology); Metamodeling; Mathematics; Algorithm; Artificial intelligence; Machine learning; Image (mathematics)","score_opus":0.048706465481120335,"score_gpt":0.3532155430837276,"score_spread":0.30450907760260726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039188123","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":[0.0013534011,0.00008204322,0.9971852,0.00017832973,0.0008412597,0.00025779472,0.0000151280565,0.000050431212,0.000036470967],"genre_scores_gemma":[0.2020066,0.0000010643626,0.79756594,0.00007639583,0.00031177554,0.000005500741,0.000007047911,0.000015603977,0.000010110901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861634,0.00005257626,0.00046933175,0.00018826804,0.00043875558,0.00023471974],"domain_scores_gemma":[0.9979871,0.00096740894,0.00018990877,0.00028139687,0.0004214674,0.00015274939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010303982,0.00015479371,0.00024687665,0.00027819493,0.00006703168,0.00006918123,0.0007332277,0.000032694894,0.0000032920398],"category_scores_gemma":[0.0004662504,0.00014471514,0.00006782089,0.00029708215,0.00001044271,0.0011527478,0.00014787012,0.00017389187,0.0000036948247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013002742,0.00023539539,0.000081554426,0.0000073737465,0.00007625346,0.000007882181,0.00048262163,0.984314,0.0003429815,0.008623273,0.00014342456,0.005672216],"study_design_scores_gemma":[0.0013226032,0.0002743153,0.0025604889,0.000022855984,0.000019195259,0.00017824379,0.00007509011,0.9942735,0.00021823101,0.00041548317,0.00048453233,0.0001554882],"about_ca_topic_score_codex":3.43355e-7,"about_ca_topic_score_gemma":4.920172e-8,"teacher_disagreement_score":0.2006532,"about_ca_system_score_codex":0.00008956057,"about_ca_system_score_gemma":0.000096399315,"threshold_uncertainty_score":0.5901312},"labels":[],"label_agreement":null},{"id":"W2039252507","doi":"10.1145/2463372.2463456","title":"Revisiting the NSGA-II crowding-distance computation","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Crowding; Instability; Convergence (economics); Benchmark (surveying); Pareto principle; Computation; Mathematics; Mathematical optimization; Position (finance); Multi-objective optimization; Computer science; Algorithm; Physics; Economics; Biology; Geography","score_opus":0.012532856668329715,"score_gpt":0.2583710644320654,"score_spread":0.24583820776373572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039252507","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":[0.0004892425,0.000054629458,0.9868443,0.0036102347,0.00017709273,0.0002683278,3.5929358e-7,0.00028393202,0.008271879],"genre_scores_gemma":[0.29269788,0.0000067960705,0.70463485,0.0008421271,0.000110467816,0.000031740652,0.000001780511,0.00000936092,0.001664989],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901885,0.000062090716,0.00020169276,0.0003016282,0.00020450735,0.00021124237],"domain_scores_gemma":[0.99906,0.00016991037,0.000116521485,0.00031884108,0.00028297925,0.00005176923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018172368,0.00011040294,0.00009438543,0.00004163798,0.0004557536,0.00024919515,0.0005028621,0.000025622114,0.000070313516],"category_scores_gemma":[0.00011056755,0.000076236436,0.000035755682,0.0004477622,0.00004674035,0.00102554,0.0002459033,0.00010356385,0.0001891968],"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":[0.0000017059002,0.000036902282,0.0004081973,0.000013872204,0.000020490746,0.000004071485,0.0017097848,0.16736467,0.0005888523,0.15501565,0.002381288,0.67245454],"study_design_scores_gemma":[0.0001582865,0.000011914743,0.0019533262,0.00001184011,0.0000012105171,0.000007758488,0.00007594563,0.9911229,0.00034672086,0.0043643834,0.0018192804,0.00012640447],"about_ca_topic_score_codex":0.000023941693,"about_ca_topic_score_gemma":0.0000012725299,"teacher_disagreement_score":0.82375824,"about_ca_system_score_codex":0.0000536871,"about_ca_system_score_gemma":0.000020769292,"threshold_uncertainty_score":0.35053343},"labels":[],"label_agreement":null},{"id":"W2040684881","doi":"10.1109/cec.2014.6900592","title":"MODEL: Multi-objective differential evolution with leadership enhancement","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Ontario Tech University","funders":"","keywords":"Particle swarm optimization; Mathematical optimization; Acceleration; Differential evolution; Generalization; Computer science; Convergence (economics); Metaheuristic; Process (computing); Differential (mechanical device); Multi-swarm optimization; Mathematics; Engineering","score_opus":0.039289988803120006,"score_gpt":0.2564670842114165,"score_spread":0.2171770954082965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040684881","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":[0.00047938467,0.000014054284,0.9960115,0.00022493664,0.00017119541,0.0003262338,9.1980917e-7,0.00036664557,0.0024051338],"genre_scores_gemma":[0.50244933,0.0000014020306,0.49554104,0.00015320156,0.000028962277,0.000038831928,0.0000024159474,0.000012614443,0.0017721883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982955,0.00008833315,0.00019855633,0.00064064085,0.000369831,0.00040717682],"domain_scores_gemma":[0.9989297,0.000056760517,0.00011480768,0.0005154391,0.0002584733,0.00012481485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000120706674,0.00023452981,0.00019138723,0.00013643883,0.0001782915,0.00009019691,0.00046917648,0.00006508497,0.00003306049],"category_scores_gemma":[0.00004593631,0.0001870936,0.000048314258,0.00034498228,0.000086408756,0.0007743542,0.00014581521,0.00015313462,0.00006506009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009435441,0.0008937663,0.00049179024,0.000032094547,0.0001042919,0.000003990683,0.0024336895,0.73725057,0.007520671,0.20036964,0.00014162395,0.050663505],"study_design_scores_gemma":[0.0012740218,0.00016961353,0.0008044973,0.000014422771,0.000007017522,0.0000044594244,0.00009091321,0.9891352,0.007290397,0.00089237024,0.000027203083,0.00028987793],"about_ca_topic_score_codex":0.000018110553,"about_ca_topic_score_gemma":0.000045052693,"teacher_disagreement_score":0.50196993,"about_ca_system_score_codex":0.00022948036,"about_ca_system_score_gemma":0.000067418805,"threshold_uncertainty_score":0.76294553},"labels":[],"label_agreement":null},{"id":"W2041749499","doi":"10.1016/j.compstruc.2006.10.013","title":"Particle swarm approach for structural design optimization","year":2007,"lang":"en","type":"article","venue":"Computers & Structures","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":703,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Multi-swarm optimization; Mathematical optimization; Metaheuristic; Benchmark (surveying); Particle swarm optimization; Meta-optimization; Derivative-free optimization; Computer science; Scope (computer science); Engineering optimization; Continuous optimization; Optimization problem; Imperialist competitive algorithm; Constraint (computer-aided design); Algorithm; Mathematics","score_opus":0.025396158069988194,"score_gpt":0.28298057101651747,"score_spread":0.2575844129465293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041749499","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":[0.00047356024,0.00013852394,0.99721867,0.00006121933,0.00084366667,0.0007749654,0.000003998373,0.0004000343,0.00008537988],"genre_scores_gemma":[0.16310197,0.000003780001,0.83631766,0.00030515302,0.00017879625,0.000019446385,0.000021167154,0.000024965715,0.000027040118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981041,0.0000671772,0.00035607402,0.0006501229,0.00028175872,0.0005407951],"domain_scores_gemma":[0.99860185,0.0002766832,0.00018320883,0.0005015647,0.00025927668,0.00017742623],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031314822,0.00027271334,0.000236843,0.000129474,0.00032730223,0.00021760823,0.0008324319,0.00009836084,0.0000051348297],"category_scores_gemma":[0.00007876101,0.00025110238,0.000092515475,0.0005018966,0.0000918544,0.0006610108,0.00018986402,0.00012912006,0.0000014358087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031586227,0.00001449608,0.000039390252,0.000009368351,0.000022109569,0.0000031986624,0.00044977243,0.9512786,0.00012545976,0.011151907,0.00015776318,0.036716305],"study_design_scores_gemma":[0.00092526013,0.0000920092,0.0007105228,0.0000036071158,0.000008409936,0.000034518922,0.00003559439,0.9845149,0.008775418,0.0044772234,0.000110712484,0.00031184556],"about_ca_topic_score_codex":0.0000034515426,"about_ca_topic_score_gemma":4.648696e-7,"teacher_disagreement_score":0.16262841,"about_ca_system_score_codex":0.00011299453,"about_ca_system_score_gemma":0.000049715345,"threshold_uncertainty_score":0.9999941},"labels":[],"label_agreement":null},{"id":"W2042139369","doi":"10.1080/03610926.2010.533230","title":"A Bayesian Meta-Modeling Approach for Gaussian Stochastic Process Models Using a Non Informative Prior","year":2012,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Constructive; Bayesian probability; Artificial intelligence; Machine learning; Gaussian process; Gaussian; Process (computing)","score_opus":0.10647305690187177,"score_gpt":0.4337829574225255,"score_spread":0.32730990052065373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042139369","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":[0.00003145301,0.0008450922,0.99779534,0.000013756178,0.000052851174,0.00086744607,0.000029354149,0.000044991328,0.0003197181],"genre_scores_gemma":[0.20264234,0.000029309236,0.79683024,0.0000705771,0.000010955923,0.0003611454,0.000021460302,0.000017954695,0.000016018748],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755925,0.0012457751,0.00047929402,0.00025421655,0.00013004229,0.00033141198],"domain_scores_gemma":[0.9970503,0.0015776017,0.00028440874,0.00068411307,0.0002752791,0.00012830111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004607005,0.00021660204,0.0003846065,0.00021466853,0.0003583278,0.00009020843,0.0005955042,0.00008158681,0.0000029377236],"category_scores_gemma":[0.00074007286,0.00019767301,0.00004592072,0.00037056615,0.00013197782,0.0017408488,0.00026826336,0.00023628492,2.1530938e-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":[0.000034547145,0.000072127084,0.0000022544812,0.000060817547,0.000059426442,4.6042643e-8,0.014840031,0.64219594,0.000012318855,0.31786433,4.3296626e-7,0.0248577],"study_design_scores_gemma":[0.00040946595,0.00001732922,0.000003925865,0.00002306906,0.00008850897,0.000007116622,0.0011037234,0.7790001,0.000074585856,0.21908763,0.0000025460265,0.00018197931],"about_ca_topic_score_codex":0.000005890045,"about_ca_topic_score_gemma":8.601013e-7,"teacher_disagreement_score":0.2026109,"about_ca_system_score_codex":0.000082556006,"about_ca_system_score_gemma":0.00008246022,"threshold_uncertainty_score":0.80608714},"labels":[],"label_agreement":null},{"id":"W2043096963","doi":"10.1016/j.jcp.2010.03.005","title":"A method for stochastic constrained optimization using derivative-free surrogate pattern search and collocation","year":2010,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Burroughs Wellcome Fund","keywords":"Mathematical optimization; Surrogate model; Collocation (remote sensing); Stochastic optimization; Computer science; Kriging; Probabilistic logic; Uncertainty quantification; Optimization problem; Monte Carlo method; Set (abstract data type); Derivative-free optimization; Mathematics; Multi-swarm optimization; Machine learning; Artificial intelligence","score_opus":0.02563932248109915,"score_gpt":0.3296106729543946,"score_spread":0.3039713504732955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043096963","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":[0.0041265823,0.000008996913,0.99495184,0.0003638601,0.00024292234,0.0002644563,0.000015502083,0.00001920229,0.000006645821],"genre_scores_gemma":[0.19778723,9.069562e-7,0.8019431,0.00009249865,0.00014982925,0.000003400535,0.000007554813,0.00001226156,0.0000031805996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885446,0.000083565355,0.0003668623,0.00018676746,0.00036368304,0.0001446387],"domain_scores_gemma":[0.99672735,0.0007394678,0.00039650736,0.00013172271,0.0019106969,0.00009425872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045659704,0.00012760899,0.00020734103,0.00013235265,0.00016844978,0.00012453,0.00030232183,0.000044724336,0.0000025557765],"category_scores_gemma":[0.0002164823,0.00012799237,0.0000614166,0.00034006187,0.00008174098,0.00085969234,0.00008918773,0.0002025146,3.14181e-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":[0.000014862215,0.0000574542,0.000021813881,0.000010446263,0.00003327274,0.0000012225833,0.00036649787,0.96389776,0.0007360845,0.0071408423,0.0000056724025,0.02771409],"study_design_scores_gemma":[0.0015497567,0.00009272669,0.00027982716,0.000021171803,0.000015604926,0.000095920026,0.00003882279,0.96289283,0.00037481837,0.03451491,0.0000033470685,0.00012027596],"about_ca_topic_score_codex":0.0000032467244,"about_ca_topic_score_gemma":0.0000010500152,"teacher_disagreement_score":0.19366065,"about_ca_system_score_codex":0.00005431529,"about_ca_system_score_gemma":0.0003235209,"threshold_uncertainty_score":0.5219377},"labels":[],"label_agreement":null},{"id":"W2045463058","doi":"","title":"Algorithm Runtime Prediction: Methods & Evaluation","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Parameterized complexity; Generalization; Range (aeronautics); Algorithm; Variety (cybernetics); Machine learning; Artificial intelligence; Mathematics","score_opus":0.11003129838507508,"score_gpt":0.26929776936074895,"score_spread":0.15926647097567387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045463058","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":[0.00025389792,0.00022828091,0.99218035,0.00006813701,0.0030030308,0.0007930355,0.000044309174,0.0006024047,0.0028265766],"genre_scores_gemma":[0.070341825,0.00012741753,0.92717755,0.000075502154,0.00030896955,0.0000108941,0.00008667781,0.000039445196,0.0018317414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966387,0.00081824575,0.00032377453,0.0014562167,0.00027265155,0.00049039605],"domain_scores_gemma":[0.99654365,0.00017118802,0.0004643294,0.0016352689,0.0009110346,0.00027453413],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001427443,0.0004442247,0.00040462476,0.00040492843,0.00026695032,0.00013717267,0.0015493509,0.00042219728,0.00017904208],"category_scores_gemma":[0.00012881172,0.0005403012,0.00023445945,0.00097254827,0.00012482423,0.0013388376,0.002095869,0.0007157575,0.00014198468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000074890345,0.0001663541,0.00018985552,0.000022221995,0.00015308645,0.000026523645,0.00036243245,0.83482385,0.000031007225,0.018665861,0.00010244258,0.14544888],"study_design_scores_gemma":[0.0006601833,0.000026295524,0.0010770388,0.000028004051,0.0001598216,0.000010897731,0.000042108022,0.97053707,0.00021640272,0.025735894,0.0010361727,0.00047010405],"about_ca_topic_score_codex":0.000026905396,"about_ca_topic_score_gemma":0.0000021279739,"teacher_disagreement_score":0.14497878,"about_ca_system_score_codex":0.00081451534,"about_ca_system_score_gemma":0.00035437453,"threshold_uncertainty_score":0.99970484},"labels":[],"label_agreement":null},{"id":"W2047311030","doi":"10.1115/detc2007-35554","title":"Non-Dominated Sorting Genetic Quantum Algorithm for Multi-Objective Optimization","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 Manitoba","funders":"","keywords":"Sorting; Benchmark (surveying); Genetic algorithm; Computer science; Mathematical optimization; Algorithm; Multi-objective optimization; Meta-optimization; Optimization problem; Quantum; Mathematics; Machine learning","score_opus":0.019080527160186773,"score_gpt":0.2972423854904822,"score_spread":0.27816185833029544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047311030","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":[0.00012277103,0.000039078495,0.9969456,0.00004065262,0.00063456234,0.0012097167,0.0000055391897,0.0004403837,0.0005616921],"genre_scores_gemma":[0.008871569,0.000012701511,0.9899717,0.00024054323,0.00010921051,0.000090331814,0.000018168153,0.000051142815,0.00063463615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975309,0.000036228612,0.00061677553,0.0008305703,0.00029764857,0.00068791077],"domain_scores_gemma":[0.9978676,0.00024140166,0.00033617695,0.00048546854,0.0008770831,0.00019230977],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00059198256,0.0003137865,0.00029570595,0.00034373451,0.00035241837,0.00013355532,0.00060758635,0.00014582704,0.00001965998],"category_scores_gemma":[0.00018058749,0.0003135366,0.00012296565,0.00096780347,0.000071658294,0.0008279567,0.00019937269,0.00015637117,0.000022739836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021765036,0.0002995619,0.00014524489,0.000013478519,0.000056367127,0.000024570285,0.0009781056,0.61685747,0.00074967346,0.002086279,0.00004694331,0.37872055],"study_design_scores_gemma":[0.0021237638,0.00013055895,0.0010429712,0.000012727954,0.000010841012,0.000021540418,0.00022856698,0.986984,0.0086667,0.00031351772,0.000048482692,0.00041630416],"about_ca_topic_score_codex":0.000032179687,"about_ca_topic_score_gemma":0.00001426992,"teacher_disagreement_score":0.37830424,"about_ca_system_score_codex":0.00022973455,"about_ca_system_score_gemma":0.00009892156,"threshold_uncertainty_score":0.9999317},"labels":[],"label_agreement":null},{"id":"W2048711666","doi":"10.1115/1.2429697","title":"Review of Metamodeling Techniques in Support of Engineering Design Optimization","year":2006,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1680,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metamodeling; Scope (computer science); Computer science; Engineering design process; Process (computing); Computation; Systems engineering; Industrial engineering; Management science; Engineering; Software engineering; Algorithm; Mechanical engineering","score_opus":0.027315505147018405,"score_gpt":0.2721950032825603,"score_spread":0.24487949813554188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048711666","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":[0.0000021905594,0.001872508,0.99760866,0.00012026822,0.000081883634,0.00026882222,6.3621377e-7,0.000024176761,0.000020869898],"genre_scores_gemma":[0.0032214962,0.0029060438,0.9937039,0.00011517643,0.000028229062,0.0000058305873,5.061723e-7,0.0000139269905,0.0000048884203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997932,0.0002154056,0.0011759663,0.00015355027,0.00037034514,0.00015269048],"domain_scores_gemma":[0.9979876,0.00028462205,0.00082950905,0.00020624028,0.0006405325,0.00005144574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00238092,0.00012995407,0.0005024311,0.00031754485,0.000012687114,0.000011525624,0.0004805815,0.00007344259,0.000013463499],"category_scores_gemma":[0.00055245194,0.00011547039,0.000120306555,0.00065496005,0.000012650695,0.0005637346,0.000058936184,0.00018209392,3.5134147e-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":[0.000013887149,0.00009240767,5.098133e-7,0.00016736171,0.0000106456655,0.000011268581,0.00001033841,0.98578614,0.009265513,0.0016000386,0.00012893075,0.002912947],"study_design_scores_gemma":[0.00026693728,0.00024793792,0.0000015032193,0.0011591131,0.000018724704,0.000050041035,0.0000013914868,0.8522086,0.1446533,0.0012547638,0.000042781645,0.00009486867],"about_ca_topic_score_codex":0.000002901969,"about_ca_topic_score_gemma":8.939613e-8,"teacher_disagreement_score":0.1353878,"about_ca_system_score_codex":0.00008402428,"about_ca_system_score_gemma":0.00013334418,"threshold_uncertainty_score":0.47087458},"labels":[],"label_agreement":null},{"id":"W2049743496","doi":"10.1007/s00158-014-1219-3","title":"Efficient adaptive response surface method using intelligent space exploration strategy","year":2015,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Latin hypercube sampling; Computer science; Mathematical optimization; Robustness (evolution); Adaptive sampling; Global optimization; Benchmark (surveying); Algorithm; Mathematics","score_opus":0.0666897497182106,"score_gpt":0.342705415130482,"score_spread":0.2760156654122714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049743496","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":[0.060959864,0.00016275588,0.9373067,0.00030471178,0.0004731393,0.0005262665,0.000010354395,0.00018979014,0.00006643232],"genre_scores_gemma":[0.2870634,0.000013134135,0.7127274,0.000012265474,0.000040138857,0.000006976149,0.000016611342,0.000018791243,0.00010122807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779284,0.00046013328,0.000361311,0.0006968839,0.00036429035,0.0003245665],"domain_scores_gemma":[0.99844635,0.00014230229,0.0002467851,0.00034620464,0.00055111543,0.00026722424],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005478447,0.0003086786,0.00024904357,0.00016298026,0.00042006868,0.00016546134,0.00026411907,0.00010929966,0.000006516139],"category_scores_gemma":[0.00012981746,0.00027438859,0.00004743638,0.0006770023,0.00009586121,0.0010896085,0.0004007545,0.00016185967,0.0000041224985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031995508,0.000022914563,0.000025546244,0.000005674817,0.0000122627425,0.000008939393,0.0036007983,0.99043536,0.0008733546,0.0018838905,0.0000033222418,0.0028080042],"study_design_scores_gemma":[0.00079289253,0.00026175872,0.0002318104,0.00001927172,0.000019020648,0.00006321939,0.0021570667,0.99295217,0.0018287394,0.001318085,0.000004614446,0.0003513623],"about_ca_topic_score_codex":0.000029499517,"about_ca_topic_score_gemma":0.0000031244028,"teacher_disagreement_score":0.22610354,"about_ca_system_score_codex":0.00027140896,"about_ca_system_score_gemma":0.00017352928,"threshold_uncertainty_score":0.99997085},"labels":[],"label_agreement":null},{"id":"W2051457218","doi":"10.1109/cec.2013.6557570","title":"A scalability study of multi-objective particle swarm optimizers","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Brock University","funders":"","keywords":"Particle swarm optimization; Scalability; Mathematical optimization; Computer science; Multi-swarm optimization; Multi-objective optimization; Swarm behaviour; Optimization problem; Mathematics","score_opus":0.02082616173945717,"score_gpt":0.2775070820002674,"score_spread":0.25668092026081024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051457218","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":[0.22654118,0.000009879183,0.77133334,0.00009325743,0.000117040516,0.0011166496,6.852962e-7,0.00021131577,0.0005766632],"genre_scores_gemma":[0.5891397,0.0000012092063,0.410423,0.000052240823,0.0000057799525,0.00011597983,2.5684244e-7,0.00000827373,0.00025352204],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816465,0.0001735574,0.00041395027,0.00060533115,0.00032221092,0.0003203116],"domain_scores_gemma":[0.9980708,0.00017829513,0.00015576681,0.0008268515,0.0006126095,0.00015568468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002341087,0.00018783138,0.00027862785,0.00008332081,0.00010609322,0.00007351005,0.0006308995,0.000046903573,0.00012146073],"category_scores_gemma":[0.00022520524,0.0001602494,0.00006435364,0.0007660745,0.00010494009,0.0011775423,0.0003715571,0.00012224387,0.000112659356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007996933,0.023594009,0.09990663,0.000045258483,0.0004070213,0.000021584226,0.040416136,0.6851474,0.006182134,0.005159924,0.00028334596,0.13875656],"study_design_scores_gemma":[0.0022554384,0.00030888405,0.046931617,0.0000034526104,0.000006430754,0.0000023497712,0.002370762,0.93695736,0.010657351,0.00029225936,0.000004537148,0.00020954016],"about_ca_topic_score_codex":0.0006901331,"about_ca_topic_score_gemma":0.000039733357,"teacher_disagreement_score":0.36259854,"about_ca_system_score_codex":0.00009549415,"about_ca_system_score_gemma":0.000048814392,"threshold_uncertainty_score":0.6534781},"labels":[],"label_agreement":null},{"id":"W2051514052","doi":"10.1007/s00158-009-0439-4","title":"Data Mining based mutation function for engineering problems with mixed continuous-discrete design variables","year":2009,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","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":"Toronto Metropolitan University","funders":"","keywords":"Mutation; Mathematical optimization; Discrete optimization; Continuous optimization; Convergence (economics); Engineering design process; Computer science; Genetic algorithm; Pareto principle; Frame (networking); Multi-objective optimization; Optimization problem; Data mining; Mathematics; Engineering; Multi-swarm optimization","score_opus":0.022898595106653766,"score_gpt":0.25711352233214346,"score_spread":0.2342149272254897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051514052","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":[0.00081186433,0.00007495988,0.997205,0.0002769955,0.00025066404,0.0010371207,0.00004206187,0.00028594534,0.000015346728],"genre_scores_gemma":[0.10793894,0.0000063796538,0.8912289,0.00003435097,0.00006260854,0.00004657456,0.00063007255,0.000020735832,0.000031427313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847263,0.0000520899,0.00028817003,0.0007080607,0.00018851948,0.0002905436],"domain_scores_gemma":[0.9988603,0.00016097845,0.00020716379,0.00042267816,0.00025363514,0.00009522859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022491213,0.0002665898,0.00021791396,0.00015393077,0.00042381792,0.000189619,0.00033119431,0.00008277412,0.0000031878574],"category_scores_gemma":[0.000088441644,0.00022160303,0.000023373132,0.0003891739,0.000036161957,0.001943655,0.00010768087,0.000081934166,2.5264728e-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":[0.000109486384,0.000012991545,0.000027794658,0.000031609507,0.000015909567,0.0000022468603,0.00023531227,0.98592234,0.00038738287,0.0006352614,0.000010664429,0.01260901],"study_design_scores_gemma":[0.0017715074,0.000580543,0.0012915015,0.000060281094,0.000050888877,0.000025941383,0.00008226274,0.9949592,0.00028397067,0.0005408428,0.00001922474,0.0003338806],"about_ca_topic_score_codex":0.0000019860206,"about_ca_topic_score_gemma":0.0000021280366,"teacher_disagreement_score":0.10712708,"about_ca_system_score_codex":0.00004562198,"about_ca_system_score_gemma":0.000059416474,"threshold_uncertainty_score":0.9036709},"labels":[],"label_agreement":null},{"id":"W2051720317","doi":"10.1115/detc2007-34839","title":"Approximated Unimodal Region Elimination Based Global Optimization Method for Engineering Design","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Benchmark (surveying); Global optimization; Computation; Mathematical optimization; Computer science; Optimization problem; Algorithm; Field (mathematics); Mathematics","score_opus":0.022981830394992908,"score_gpt":0.29921564732940176,"score_spread":0.27623381693440885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051720317","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":[0.0000023727948,0.000008989656,0.99778175,0.0002695082,0.00022655183,0.0007559513,0.0000017179565,0.00065230124,0.0003008371],"genre_scores_gemma":[0.005122715,0.0000014791605,0.99451137,0.00018173069,0.000038206435,0.000047910664,0.00002196014,0.000018347499,0.00005628554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869734,0.000055546665,0.0002769348,0.0004339951,0.0002127732,0.00032339446],"domain_scores_gemma":[0.99868006,0.00035065823,0.00012669899,0.0002883265,0.00045682208,0.000097441654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007603374,0.00017640146,0.00014200821,0.00017289686,0.00011097853,0.00008533455,0.00032437072,0.00010280528,0.0000040768496],"category_scores_gemma":[0.00031230127,0.00018195262,0.000053061056,0.0009460888,0.000012551342,0.00068640645,0.000047719772,0.000056798915,0.0000019889414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026081585,0.00004185698,0.0000054085426,0.000008395797,0.000006125714,0.000002534625,0.000029897794,0.94699186,0.0001550996,0.026881874,0.000027406968,0.025823466],"study_design_scores_gemma":[0.00089324865,0.00008159256,0.000103980936,0.00000907748,0.0000065637164,0.000011487813,0.000013119621,0.9886771,0.0095022395,0.00039036354,0.00009596619,0.00021527603],"about_ca_topic_score_codex":0.0000054471006,"about_ca_topic_score_gemma":0.0000010962513,"teacher_disagreement_score":0.04168523,"about_ca_system_score_codex":0.000313816,"about_ca_system_score_gemma":0.00006519381,"threshold_uncertainty_score":0.74198127},"labels":[],"label_agreement":null},{"id":"W2055163119","doi":"10.1016/s0378-3758(02)00464-0","title":"Component-wise dimension reduction","year":2003,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Queen's University","funders":"","keywords":"Mathematics; Sufficient dimension reduction; Component (thermodynamics); Sliced inverse regression; Dimensionality reduction; Dimension (graph theory); Statistics; Reduction (mathematics); Applied mathematics; Combinatorics; Regression analysis; Geometry; Artificial intelligence; Regression","score_opus":0.023628036533946517,"score_gpt":0.307558646949925,"score_spread":0.2839306104159785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055163119","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":[0.0059461994,0.0001747764,0.99302554,0.00007072741,0.00030960437,0.00003147948,0.0000019816523,0.000015288279,0.0004243751],"genre_scores_gemma":[0.523984,0.000026490536,0.47592375,0.000028054603,0.000016348426,3.2702854e-7,5.8814703e-7,0.0000025101099,0.000017907973],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999181,0.00008180429,0.00027637495,0.0001306961,0.00020309234,0.000127047],"domain_scores_gemma":[0.9990951,0.00027322708,0.00019790587,0.000086409695,0.00021602554,0.00013137299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023866755,0.00008260139,0.00015347918,0.00009099831,0.0000947574,0.000070107075,0.00010756138,0.00003151332,0.000011131288],"category_scores_gemma":[0.0006050482,0.00006806022,0.000015457954,0.00012350167,0.00006573108,0.00047774115,0.000029575,0.00019781537,0.0000033843883],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012884232,0.00045256305,0.005932271,0.00005703071,0.00008701979,0.00052928855,0.0034112532,0.08204319,0.010433412,0.7758853,0.0021508974,0.11888897],"study_design_scores_gemma":[0.00415855,0.002026681,0.05262014,0.0006719568,0.000065566805,0.0043935343,0.0008000416,0.79902714,0.006067684,0.12420783,0.0049438966,0.0010169602],"about_ca_topic_score_codex":0.0000013621977,"about_ca_topic_score_gemma":2.9436801e-8,"teacher_disagreement_score":0.716984,"about_ca_system_score_codex":0.000028040042,"about_ca_system_score_gemma":0.00005209363,"threshold_uncertainty_score":0.2775415},"labels":[],"label_agreement":null},{"id":"W2056931841","doi":"10.1109/ictai.2006.51","title":"Discover Gene Specific Local Co-regulations Using Progressive Genetic Algorithm","year":2006,"lang":"en","type":"article","venue":"Proceedings - International Conference on Tools with Artificial Intelligence, TAI","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Genetic algorithm; Position (finance); Window (computing); Computer science; Gene; Population; Algorithm; Data mining; Computational biology; Biology; Genetics; Machine learning; Medicine","score_opus":0.06341183955686304,"score_gpt":0.3179801793595039,"score_spread":0.2545683398026409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056931841","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":[0.0059979325,0.000039703362,0.9844512,0.00050819776,0.000513279,0.00071407756,0.00006664674,0.00025839254,0.0074506225],"genre_scores_gemma":[0.5153612,0.000022493963,0.48351887,0.00011451599,0.00048968464,0.00011584097,0.00006150678,0.000041896143,0.00027399056],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963005,0.000027280434,0.00078828825,0.001142537,0.0011330304,0.00060833036],"domain_scores_gemma":[0.99703014,0.0000783083,0.0005384459,0.00031178552,0.0018616283,0.00017967827],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00017811648,0.0004915591,0.00033701956,0.0004995352,0.00041226248,0.0014779505,0.0012490507,0.00014295905,0.00029424438],"category_scores_gemma":[0.000056044333,0.00044301487,0.0001076687,0.0008994155,0.0005003158,0.0020454251,0.00019137637,0.0003690729,0.00017323166],"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":[0.00012699208,0.0005775906,0.00038798546,0.000009398084,0.000085413376,0.000065210734,0.0004072011,0.08871,0.0040905974,0.67278475,0.00011906207,0.2326358],"study_design_scores_gemma":[0.00017152811,0.0002316554,0.0006098123,0.000099774414,0.000014015523,0.00011972747,0.00043984113,0.89974487,0.055208728,0.042179808,0.00055419974,0.00062606885],"about_ca_topic_score_codex":0.000048159556,"about_ca_topic_score_gemma":0.0000065417894,"teacher_disagreement_score":0.81103486,"about_ca_system_score_codex":0.00041352594,"about_ca_system_score_gemma":0.00026517746,"threshold_uncertainty_score":0.9998022},"labels":[],"label_agreement":null},{"id":"W2057639762","doi":"10.1007/s00366-014-0358-x","title":"Comparative study on influencing factors in adaptive metamodeling","year":2014,"lang":"en","type":"article","venue":"Engineering With Computers","topic":"Advanced Multi-Objective Optimization Algorithms","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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metamodeling; Kriging; Computer science; Multivariate statistics; Sample size determination; Polynomial; Sample (material); Noise (video); Quality (philosophy); Mathematical optimization; Algorithm; Data mining; Mathematics; Machine learning; Artificial intelligence; Statistics","score_opus":0.019708510550921394,"score_gpt":0.24391569033848232,"score_spread":0.2242071797875609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057639762","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":[0.2273922,0.000004774812,0.771758,0.000010793172,0.00024483772,0.00028531766,3.4614632e-7,0.00024044678,0.00006328074],"genre_scores_gemma":[0.6701311,1.6619771e-7,0.32978758,0.000029333147,0.00001851513,0.000015714912,6.315256e-7,0.000013838911,0.0000031328937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857795,0.00007429392,0.00023507957,0.0005131458,0.00029379956,0.00030573903],"domain_scores_gemma":[0.99896467,0.00039266978,0.00008192531,0.00038051847,0.000084691266,0.00009555659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019327136,0.0002773584,0.0003507706,0.0003751953,0.00006386998,0.00007961265,0.00046446262,0.000031132924,5.1406664e-7],"category_scores_gemma":[0.000037045316,0.00023941003,0.00003163435,0.00068402593,0.000016489981,0.00050294586,0.00012129442,0.0002729577,0.000006073624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006712575,0.00007900378,0.0010214867,0.0000021043368,0.000030316407,0.000007240764,0.006058653,0.99114615,0.000042233325,0.0011814779,0.0000016006235,0.00042301632],"study_design_scores_gemma":[0.00080482365,0.0004699026,0.019813588,0.00007020388,0.0000037283378,0.0000022022377,0.00039053548,0.9776383,0.00048608883,0.000016494125,0.000014923343,0.00028920162],"about_ca_topic_score_codex":0.000020091196,"about_ca_topic_score_gemma":0.0000066957873,"teacher_disagreement_score":0.4427389,"about_ca_system_score_codex":0.00016813651,"about_ca_system_score_gemma":0.000023202403,"threshold_uncertainty_score":0.9762858},"labels":[],"label_agreement":null},{"id":"W2059013163","doi":"10.1115/1.4006996","title":"Multiresponse Metamodeling in Simulation-Based Design Applications","year":2012,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Metamodeling; Engineering design process; Computer science; Set (abstract data type); Function (biology); Design of experiments; Sampling (signal processing); Process (computing); Bayesian probability; Machine learning; Artificial intelligence; Engineering; Mathematics; Statistics","score_opus":0.07561615083097034,"score_gpt":0.3316012940338979,"score_spread":0.25598514320292753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059013163","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":[0.000051046547,0.0002100058,0.9989157,0.00016231913,0.00018350461,0.00042882265,5.8567275e-7,0.000040532235,0.0000074996983],"genre_scores_gemma":[0.38798288,0.0000067603432,0.61174816,0.00015809776,0.00006394111,0.000020702011,1.7615268e-7,0.000011126566,0.000008190792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976209,0.0007417361,0.00068845856,0.00019169752,0.00043662515,0.00032062354],"domain_scores_gemma":[0.99511886,0.0034941253,0.00043604037,0.000309304,0.00040341393,0.00023827888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00298569,0.00015655223,0.00028524653,0.00037569675,0.000083611136,0.000053064738,0.000606177,0.00009819468,0.00001600301],"category_scores_gemma":[0.0012438708,0.00013851213,0.00011043951,0.00071825774,0.00001874616,0.0010300943,0.000052414907,0.00029474378,0.00001999849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009291534,0.00025049006,0.000009064982,0.0000023125044,0.000011197414,0.000006513713,0.00008539204,0.9816301,0.0023464495,0.001740509,0.000006156175,0.013818893],"study_design_scores_gemma":[0.0009790118,0.00010796098,0.000048379115,0.000021544727,0.000013204306,0.000013060583,0.000013175701,0.98752177,0.008139649,0.0028477907,0.00014587335,0.00014859022],"about_ca_topic_score_codex":9.195019e-7,"about_ca_topic_score_gemma":1.4348159e-7,"teacher_disagreement_score":0.38793182,"about_ca_system_score_codex":0.00019619879,"about_ca_system_score_gemma":0.0002121438,"threshold_uncertainty_score":0.5648361},"labels":[],"label_agreement":null},{"id":"W2059486227","doi":"10.1115/1.4028756","title":"Statistical Surrogate Formulations for Simulation-Based Design Optimization","year":2014,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; McGill University; Group for Research in Decision Analysis","funders":"","keywords":"Benchmark (surveying); Surrogate model; Mathematical optimization; Computer science; Simulation-based optimization; Multidisciplinary design optimization; Algorithm; Mathematics; Multidisciplinary approach","score_opus":0.04705848767742186,"score_gpt":0.312561158192595,"score_spread":0.2655026705151731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059486227","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":[0.0000022616753,0.000010089517,0.9986927,0.0003537598,0.00035394562,0.0005080738,0.000004809257,0.000061875304,0.000012518029],"genre_scores_gemma":[0.10622634,0.000002916659,0.89335185,0.00025315565,0.0001059719,0.000016125025,0.0000041096046,0.000022578733,0.000016950018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782753,0.00048092642,0.0007130462,0.00025687032,0.00045309262,0.0002685196],"domain_scores_gemma":[0.9904167,0.007399996,0.00056523515,0.00026629306,0.0011404293,0.00021133308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018778178,0.00017390064,0.00031519923,0.00021832401,0.00018681883,0.00012380871,0.0004898,0.000109024164,0.000029443854],"category_scores_gemma":[0.0043768673,0.00015422651,0.00012092458,0.00035733223,0.000026904889,0.00071991194,0.000033968117,0.0001651204,0.0000058577652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013713731,0.00009898371,8.9413237e-7,0.00000551316,0.000016873435,0.0000025283762,0.000021037336,0.9649766,0.00016102256,0.0246309,0.00007557301,0.0098729],"study_design_scores_gemma":[0.0018494306,0.0007587414,0.000011922621,0.000022943637,0.000028791337,0.0000071478753,0.0000026058897,0.9646192,0.0018941924,0.03047741,0.00015990016,0.00016767417],"about_ca_topic_score_codex":4.3511903e-7,"about_ca_topic_score_gemma":1.6584552e-7,"teacher_disagreement_score":0.10622408,"about_ca_system_score_codex":0.00012536386,"about_ca_system_score_gemma":0.00022023097,"threshold_uncertainty_score":0.62891746},"labels":[],"label_agreement":null},{"id":"W2060780342","doi":"10.1016/j.jspi.2011.12.030","title":"Projection array based designs for computer experiments","year":2012,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Advanced Multi-Objective Optimization Algorithms","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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Los Alamos National Laboratory; Natural Sciences and Engineering Research Council of Canada; National Nuclear Security Administration; U.S. Department of Energy","keywords":"Latin hypercube sampling; Projection (relational algebra); Orthogonal array; Orthographic projection; Computer experiment; Hypercube; Mathematics; Sampling (signal processing); Design of experiments; Focus (optics); Class (philosophy); Sample (material); Algorithm; Space (punctuation); Mathematical optimization; Computer science; Monte Carlo method; Artificial intelligence; Geometry; Discrete mathematics; Statistics; Computer vision","score_opus":0.08170966478275682,"score_gpt":0.37518087932356353,"score_spread":0.2934712145408067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060780342","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":[0.000482019,0.00008295379,0.9988292,0.00004530522,0.0003767525,0.00009411933,0.0000058653313,0.000016899996,0.00006690682],"genre_scores_gemma":[0.309367,0.0000019685497,0.69041914,0.00009747959,0.000099377154,0.000003907654,0.0000014107965,0.0000036345289,0.0000060437496],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992483,0.000047774465,0.00024769406,0.00010663653,0.00016112225,0.00018847089],"domain_scores_gemma":[0.99878645,0.0005997585,0.00019032902,0.00006741492,0.0002112529,0.00014477747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027721666,0.00008921775,0.00015008086,0.00009071766,0.00009536368,0.00007689865,0.00012434492,0.00003382934,0.000005134415],"category_scores_gemma":[0.00025347766,0.00007228812,0.000021100797,0.000084917745,0.000040999465,0.000651475,0.000020504955,0.00011524353,0.0000011877563],"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":[0.00092719693,0.0025448906,0.073228866,0.00036258713,0.00033845904,0.00009322804,0.01754844,0.1391366,0.019865675,0.16806312,0.007873557,0.5700174],"study_design_scores_gemma":[0.0012826766,0.00090898207,0.014320537,0.0001220167,0.000015417801,0.000074439784,0.0000777861,0.97636276,0.004011741,0.001679798,0.0009116032,0.0002322447],"about_ca_topic_score_codex":9.0789746e-7,"about_ca_topic_score_gemma":2.4121652e-8,"teacher_disagreement_score":0.83722615,"about_ca_system_score_codex":0.000035381858,"about_ca_system_score_gemma":0.00005759252,"threshold_uncertainty_score":0.2947824},"labels":[],"label_agreement":null},{"id":"W2060810048","doi":"10.1109/mesa.2012.6275565","title":"Metamodel multi-objective optimization tool for mechatronic system design","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Multi-objective optimization; Metamodeling; Benchmark (surveying); Mathematical optimization; Computer science; Pareto principle; Engineering optimization; Computation; Mechatronics; Black box; Optimization problem; Test functions for optimization; Multi-swarm optimization; Artificial intelligence; Algorithm; Mathematics","score_opus":0.03645320878998011,"score_gpt":0.2789788759735975,"score_spread":0.2425256671836174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060810048","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":[0.000005906115,0.00021443887,0.99625385,0.00005367153,0.000683414,0.0016585921,0.0000068092563,0.00077589275,0.00034742392],"genre_scores_gemma":[0.049145706,0.000010559836,0.94927746,0.00011251538,0.00010075637,0.0005040007,0.0000087160715,0.00004084077,0.00079947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980294,0.00017111005,0.0003626641,0.0005331032,0.000266469,0.00063721364],"domain_scores_gemma":[0.99831194,0.00028769486,0.00020013397,0.000569286,0.00047448382,0.00015643763],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00074733765,0.00028007777,0.0002916075,0.00019649259,0.00026904687,0.000118325406,0.0005554919,0.00011490661,0.000016249429],"category_scores_gemma":[0.00016441682,0.00025326686,0.00012767527,0.0004975016,0.000029399613,0.0025778678,0.00016170641,0.00010134365,0.000043581214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012526372,0.00011031677,0.000013671883,0.000015929945,0.00004399296,3.6609313e-7,0.0003141411,0.9343594,0.000102240796,0.061668385,0.000050746163,0.0033083078],"study_design_scores_gemma":[0.0011745668,0.00006435659,0.000026308056,0.000010876868,0.000025808398,0.000016489397,0.00014975786,0.9929128,0.0050712284,0.00012362769,0.00008318419,0.0003409403],"about_ca_topic_score_codex":0.000005748019,"about_ca_topic_score_gemma":7.147085e-7,"teacher_disagreement_score":0.061544757,"about_ca_system_score_codex":0.00046133905,"about_ca_system_score_gemma":0.00010933093,"threshold_uncertainty_score":0.99999195},"labels":[],"label_agreement":null},{"id":"W2060879653","doi":"10.1093/biomet/asu042","title":"Nearly orthogonal arrays mappable into fully orthogonal arrays","year":2014,"lang":"en","type":"article","venue":"Biometrika","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Orthogonal array; Mathematics; Column (typography); Set (abstract data type); Orthogonal basis; Orthogonal transformation; Orthogonal functions; Algorithm; Orthogonal matrix; Empirical orthogonal functions; Orthogonality; Combinatorics; Topology (electrical circuits); Geometry; Computer science; Mathematical analysis; Statistics; Connection (principal bundle)","score_opus":0.010759778961211157,"score_gpt":0.24406219784249653,"score_spread":0.23330241888128536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060879653","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":[0.0038445264,0.00015146054,0.9892723,0.00054513436,0.0010400077,0.00027911077,0.000011800797,0.0004720725,0.0043836166],"genre_scores_gemma":[0.11434156,0.00002477065,0.88320035,0.0006425079,0.00038670297,0.00004621674,0.00003346544,0.000045690394,0.0012787584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99682957,0.00015308002,0.00049528916,0.0010010488,0.00083254423,0.00068845483],"domain_scores_gemma":[0.99761957,0.00028768298,0.0002698334,0.0009773666,0.0004726797,0.0003728858],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007364274,0.000372003,0.00038486143,0.0012748832,0.00037359193,0.0003514031,0.0013169746,0.00017268264,0.00015812792],"category_scores_gemma":[0.0005703681,0.00036345786,0.00016697073,0.005311534,0.00017249897,0.0011209259,0.0004255899,0.00027745764,0.00068677875],"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":[0.00010336829,0.0010210142,0.0059004114,0.0001086681,0.00023258489,0.00008226743,0.001719327,0.023863567,0.020561697,0.24565451,0.004352986,0.69639957],"study_design_scores_gemma":[0.004061452,0.0009804905,0.014740692,0.000072754985,0.00003319158,0.00013753104,0.00010990988,0.72016406,0.015670452,0.035379488,0.20639738,0.0022525918],"about_ca_topic_score_codex":0.00002359621,"about_ca_topic_score_gemma":0.000011298264,"teacher_disagreement_score":0.6963005,"about_ca_system_score_codex":0.00017194325,"about_ca_system_score_gemma":0.00018982813,"threshold_uncertainty_score":0.99988174},"labels":[],"label_agreement":null},{"id":"W2061685312","doi":"10.7551/978-0-262-31709-2-ch139","title":"A hybrid genetic/immune strategy to tackle the multiobjective quadratic assignment problem","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Knapsack problem; Mathematical optimization; Benchmark (surveying); Multi-objective optimization; Quadratic assignment problem; Genetic algorithm; Computer science; Optimization problem; Quadratic equation; Mathematics","score_opus":0.00949706403982447,"score_gpt":0.23855539712973522,"score_spread":0.22905833308991075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061685312","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":[0.0020717133,0.00005602808,0.98815155,0.0013385705,0.00015929366,0.00200069,0.0000016174927,0.0002377535,0.005982786],"genre_scores_gemma":[0.3653181,0.000006161188,0.6301916,0.0006944159,0.00003783749,0.0006495817,0.000001502998,0.000019112478,0.0030817064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980806,0.00014358886,0.00037085704,0.00057861843,0.0003794678,0.00044686018],"domain_scores_gemma":[0.9984006,0.00014645193,0.00012493048,0.0007989841,0.00036069643,0.00016831743],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016206171,0.0002434915,0.00019628574,0.000094690644,0.00026340428,0.00038942177,0.0009468856,0.00003390444,0.00026470333],"category_scores_gemma":[0.000059479604,0.00016369719,0.00006585799,0.00043900157,0.00006306367,0.0007115268,0.000378302,0.00015944264,0.0010701121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007712807,0.00041787798,0.00009946729,0.000012552205,0.000096412245,0.000016443868,0.0028140217,0.70816463,0.007478044,0.017466936,0.002451782,0.2609741],"study_design_scores_gemma":[0.0006534375,0.00038487054,0.006856698,0.000015130167,0.0000070808496,0.000032692955,0.00045990464,0.966905,0.014941459,0.008728021,0.0005669618,0.00044876357],"about_ca_topic_score_codex":0.00029503496,"about_ca_topic_score_gemma":0.000018244358,"teacher_disagreement_score":0.36324635,"about_ca_system_score_codex":0.00017123473,"about_ca_system_score_gemma":0.000107101296,"threshold_uncertainty_score":0.9997077},"labels":[],"label_agreement":null},{"id":"W2061895663","doi":"10.1145/2566669","title":"A comparative evaluation of multi-objective exploration algorithms for high-level design","year":2014,"lang":"en","type":"article","venue":"ACM Transactions on Design Automation of Electronic Systems","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Benchmark (surveying); Scalability; Algorithm; Set (abstract data type); Heuristic; Evolutionary algorithm; Transformation (genetics); Computer engineering; Machine learning; Artificial intelligence","score_opus":0.15764757757916154,"score_gpt":0.3476167720200399,"score_spread":0.18996919444087834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061895663","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":[0.00013694004,0.00012070927,0.994769,0.00008536281,0.00045915236,0.004152516,0.00002809563,0.00022555342,0.000022720686],"genre_scores_gemma":[0.5152164,0.000010896496,0.48354682,0.000008898801,0.000021192589,0.0011031687,0.000014647839,0.000019591209,0.000058368118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953926,0.0016633933,0.00094288803,0.0006439728,0.00094324327,0.00041391078],"domain_scores_gemma":[0.99384177,0.0017295236,0.000972324,0.0008973261,0.002482691,0.000076383556],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0033168863,0.00033542706,0.0006225714,0.00061613903,0.00024559035,0.00006391557,0.0007034468,0.00017090517,0.000010924048],"category_scores_gemma":[0.00045447392,0.00034427206,0.00013503371,0.0009922796,0.000083580075,0.0013725574,0.000010028027,0.00017971238,0.000014413415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007466527,0.00032879968,3.467599e-7,0.000033740256,0.0001504441,4.89088e-8,0.0015065097,0.9470512,0.003805498,0.0053806636,0.000020764075,0.041647315],"study_design_scores_gemma":[0.003019705,0.001221059,0.000066404515,0.00008173463,0.0001012015,0.0000056552335,0.00026411755,0.88027453,0.10834218,0.006342471,0.000011986107,0.0002689584],"about_ca_topic_score_codex":0.000040173025,"about_ca_topic_score_gemma":0.000009559769,"teacher_disagreement_score":0.5150795,"about_ca_system_score_codex":0.0006884316,"about_ca_system_score_gemma":0.0005378428,"threshold_uncertainty_score":0.99990094},"labels":[],"label_agreement":null},{"id":"W2062850469","doi":"10.1115/detc2014-34624","title":"Multi-Objective Selection of Cutting Conditions in Advanced Machining Processes via an Efficient Global Optimization Approach","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; National Research Council Canada; McMaster University","funders":"","keywords":"Kriging; Pareto principle; Computer science; Multi-objective optimization; Mathematical optimization; Selection (genetic algorithm); Population; Dimension (graph theory); Process (computing); Optimization problem; Machine learning; Mathematics; Algorithm","score_opus":0.01081894644514615,"score_gpt":0.280354815196197,"score_spread":0.26953586875105084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062850469","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":[0.005712799,0.0000121590265,0.9914279,0.000015311776,0.00011079067,0.0005013961,0.000005716135,0.00030627853,0.0019076285],"genre_scores_gemma":[0.4459183,0.0000016983544,0.55395234,0.0000307683,0.000012260607,0.000044217257,0.00001964733,0.000009711445,0.0000110611645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807197,0.00020321672,0.00043814143,0.00068561116,0.00028371456,0.00031735742],"domain_scores_gemma":[0.99856776,0.00011250984,0.0003121127,0.00028256213,0.0006296156,0.00009545185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033674773,0.000219913,0.000261609,0.00024142598,0.0001935348,0.000060736886,0.0003815074,0.00008519117,0.000009832205],"category_scores_gemma":[0.00040871062,0.0002251488,0.00003690194,0.0020678027,0.00006635289,0.0010964017,0.00011369406,0.00014485157,0.0000019345737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011679735,0.00043542296,0.0017173217,0.000027368134,0.000007100016,3.0188104e-7,0.0006313082,0.98938996,0.00034682534,0.0019160704,2.8420737e-7,0.0055163563],"study_design_scores_gemma":[0.0011452066,0.00015010318,0.0028034872,0.000028564089,0.0000050900594,0.00001650461,0.00026492434,0.99320024,0.0020156263,0.00012976606,0.0000013347479,0.00023913819],"about_ca_topic_score_codex":0.000057691377,"about_ca_topic_score_gemma":0.00004654487,"teacher_disagreement_score":0.4402055,"about_ca_system_score_codex":0.00024117509,"about_ca_system_score_gemma":0.00011896941,"threshold_uncertainty_score":0.91813016},"labels":[],"label_agreement":null},{"id":"W2064300800","doi":"10.1016/j.proeng.2011.12.398","title":"A MEM Electric Field Sensor Optimization by Multi-Objective Niched Pareto Genetic Algorithm","year":2011,"lang":"en","type":"article","venue":"Procedia Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Pareto principle; Pareto optimal; Genetic algorithm; Intuition; Algorithm; Microelectromechanical systems; Mathematical optimization; Set (abstract data type); Multi-objective optimization; Computer science; Optimal design; Displacement (psychology); Engineering; Mathematics; Materials science; Nanotechnology; Machine learning","score_opus":0.01063426469680353,"score_gpt":0.21106147676137968,"score_spread":0.20042721206457614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064300800","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":[0.0002370564,0.00038660696,0.99765736,0.000027909324,0.00035216374,0.00046079257,0.000003913035,0.0007233802,0.00015081624],"genre_scores_gemma":[0.022089357,0.000110467896,0.977296,0.00011568928,0.00007546404,0.00016752696,0.0000056845847,0.000056570814,0.00008324285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982247,0.000026435604,0.00032477142,0.0006499008,0.00025525893,0.00051897834],"domain_scores_gemma":[0.9989333,0.000108308675,0.00012565085,0.00037897445,0.00027261334,0.00018112338],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010099602,0.00032317737,0.00024862378,0.00025794265,0.00009454403,0.00006997216,0.00052530173,0.00013927622,0.000021814145],"category_scores_gemma":[0.00035167733,0.0003539393,0.00006691419,0.0010311591,0.000010277254,0.00063735264,0.0001383263,0.00026633334,0.000022007824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011241883,0.00021212196,0.00023504283,0.00004789476,0.000085299675,0.000035110545,0.002825046,0.9449531,0.0054752026,0.00014848962,0.00010754443,0.045863893],"study_design_scores_gemma":[0.0005365103,0.000114285176,0.0002881687,0.000015374795,0.000011698105,0.000035474022,0.00002841824,0.9524079,0.046083096,0.000024004621,0.000050959465,0.0004041144],"about_ca_topic_score_codex":0.000019125537,"about_ca_topic_score_gemma":7.323428e-7,"teacher_disagreement_score":0.045459777,"about_ca_system_score_codex":0.0001453628,"about_ca_system_score_gemma":0.00006249417,"threshold_uncertainty_score":0.9998913},"labels":[],"label_agreement":null},{"id":"W2070890570","doi":"10.1007/s11081-007-9032-0","title":"Quality assessment of coarse models and surrogates for space mapping optimization","year":2007,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Space mapping; Computer science; Convergence (economics); Mathematical optimization; Quality (philosophy); Space (punctuation); Similarity (geometry); Surrogate model; Data mining; Algorithm; Mathematics; Machine learning; Artificial intelligence","score_opus":0.031161423987999773,"score_gpt":0.29837259838241836,"score_spread":0.2672111743944186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070890570","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":[0.0003719687,0.00006814069,0.9987042,0.00008004079,0.00011558265,0.0002886357,0.0000046378555,0.00012209403,0.0002446944],"genre_scores_gemma":[0.08456199,0.0001377251,0.915193,0.000018051132,0.000015732872,0.000012582382,0.000014773271,0.000015771608,0.000030361354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990796,0.000015844986,0.00030650862,0.0002768685,0.00013404845,0.00018712603],"domain_scores_gemma":[0.99919754,0.000193519,0.00013691398,0.00016023952,0.00022445459,0.00008731549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005106785,0.00013648749,0.00018787554,0.00018884672,0.000082957486,0.000060350038,0.000098792836,0.00006233202,0.0000023770317],"category_scores_gemma":[0.00009090812,0.00015113095,0.00002558803,0.00031552068,0.000025509196,0.00071706716,0.00007528165,0.000057280373,4.148207e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025677098,0.000016755133,0.00012864514,0.000049233768,0.000010280081,2.570347e-7,0.00023680003,0.98143244,0.00024301124,0.016356245,0.0000012762397,0.0015224686],"study_design_scores_gemma":[0.0005807936,0.000022879103,0.00048047854,0.000025746715,0.0000038596986,0.0000035276062,0.00008069903,0.9982299,0.00030901338,0.00007787722,0.000023169,0.00016201852],"about_ca_topic_score_codex":0.00000470246,"about_ca_topic_score_gemma":0.0000010035355,"teacher_disagreement_score":0.08419002,"about_ca_system_score_codex":0.00004329241,"about_ca_system_score_gemma":0.000020646514,"threshold_uncertainty_score":0.61629415},"labels":[],"label_agreement":null},{"id":"W2073323853","doi":"10.1007/s00170-012-4059-6","title":"Design of a microchannel system using axiomatic design theory for size-controllable and monodispersed microspheres by enhanced perturbation","year":2012,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 Saskatchewan","funders":"","keywords":"Axiomatic design; Microchannel; Controllability; Probabilistic design; Optimal design; Systems design; Conceptual design; Computer science; Design of experiments; Design methods; Microsphere; Engineering; Engineering design process; Mathematics; Systems engineering; Nanotechnology; Mechanical engineering; Materials science; Manufacturing engineering; Applied mathematics","score_opus":0.012337246259388637,"score_gpt":0.24859735614275735,"score_spread":0.2362601098833687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073323853","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":[0.06446592,0.0011879134,0.9330094,0.0002910504,0.00049747026,0.00047674586,0.000004576293,0.000060435574,0.0000065016197],"genre_scores_gemma":[0.5044091,0.00004733156,0.4954108,0.000035541005,0.00003350435,0.000015288373,3.2003317e-7,0.000012744068,0.000035398734],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99866647,0.00010969085,0.0005285779,0.00018484703,0.00024315252,0.0002672616],"domain_scores_gemma":[0.99720854,0.0010718878,0.0009762688,0.0002301648,0.0004644627,0.00004867908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007552327,0.0001858101,0.0003196887,0.00021772023,0.00011847074,0.000047507223,0.0009998435,0.000097102835,0.000003040425],"category_scores_gemma":[0.00041089795,0.00014055954,0.0000665004,0.00012600936,0.00012575331,0.00075288204,0.00015811708,0.00015029825,6.7531244e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000413071,0.00006802028,0.0000025686227,0.000023883891,0.00018940539,0.0000019224713,0.0006799293,0.19450486,0.78650385,0.0030970518,0.000015524236,0.014499935],"study_design_scores_gemma":[0.0016342655,0.00013373035,0.0000073261394,0.000105889485,0.000029552803,0.00020559289,0.00076380774,0.10153093,0.8825861,0.01284798,0.000024393179,0.00013040609],"about_ca_topic_score_codex":0.0000016525054,"about_ca_topic_score_gemma":1.3024929e-7,"teacher_disagreement_score":0.43994313,"about_ca_system_score_codex":0.0002496873,"about_ca_system_score_gemma":0.000055624223,"threshold_uncertainty_score":0.5731852},"labels":[],"label_agreement":null},{"id":"W2074655754","doi":"10.2495/op070201","title":"Aerodynamic optimization of a biplane configuration using differential evolution","year":2007,"lang":"en","type":"article","venue":"WIT transactions on the built environment","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Airfoil; Biplane; Chord (peer-to-peer); Angle of attack; Aerodynamics; Wing; Computer science; Lift-to-drag ratio; Drag; Lift (data mining); Mathematics; Aerospace engineering; Engineering","score_opus":0.014460308286182095,"score_gpt":0.22905377170646515,"score_spread":0.21459346342028307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074655754","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":[0.010125386,0.000015170471,0.9889201,0.0001565866,0.00021593091,0.00040613854,0.000010044085,0.00006357817,0.00008708131],"genre_scores_gemma":[0.8069458,0.000024980496,0.19285251,0.00002605411,0.00001678433,0.000013393307,0.0000072012226,0.00001307248,0.00010021949],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987172,0.000079724356,0.00032830628,0.00030665257,0.00036231664,0.00020581188],"domain_scores_gemma":[0.9991367,0.00010614486,0.00019039742,0.0004779034,0.000034535715,0.00005433399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022167951,0.0001628484,0.00013291258,0.00016568675,0.00024834936,0.000026748872,0.0002918632,0.000071075105,0.00018446718],"category_scores_gemma":[0.000007672916,0.00013545083,0.000069667214,0.00026052515,0.00010577058,0.00029305773,0.000010342425,0.00014382279,0.000016966773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002435804,0.00016711114,0.000010104489,0.0000030552173,0.000022632179,8.372088e-7,0.00014616232,0.9791631,0.014767002,0.0014366168,6.5064023e-7,0.004258379],"study_design_scores_gemma":[0.00041289197,0.00008813156,0.00168902,0.00001562437,0.00002435594,0.000010662989,0.000060223632,0.9805639,0.016792808,0.00017861236,0.000017828404,0.0001459557],"about_ca_topic_score_codex":0.00004275957,"about_ca_topic_score_gemma":0.000012272178,"teacher_disagreement_score":0.7968204,"about_ca_system_score_codex":0.0003372998,"about_ca_system_score_gemma":0.000023534207,"threshold_uncertainty_score":0.5523524},"labels":[],"label_agreement":null},{"id":"W2076037038","doi":"10.1109/cec.2013.6557735","title":"A new principal curve algorithm and standard deviation clouds for non-parametric ordered data analysis","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; University of Waterloo","funders":"","keywords":"Algorithm; Convergence (economics); Vertex (graph theory); Standard deviation; Parametric statistics; Mathematics; Measure (data warehouse); Path (computing); Polygonal chain; Computer science; Curve fitting; Representation (politics); Parametric equation; Data mining; Combinatorics; Geometry; Graph; Statistics","score_opus":0.024313186612839894,"score_gpt":0.3009605480084589,"score_spread":0.276647361395619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076037038","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":[0.00017515558,0.00006727207,0.9980852,0.00030639916,0.00013766935,0.00085729413,0.000054933083,0.00014420963,0.00017187177],"genre_scores_gemma":[0.0065199835,0.00003464709,0.9920848,0.00013151836,0.000060820228,0.00005409219,0.00012539695,0.000014257466,0.00097449124],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981746,0.00004204598,0.0003293663,0.0008242088,0.00032272338,0.00030706413],"domain_scores_gemma":[0.9977851,0.00025834917,0.00017875934,0.0010799024,0.0004889568,0.00020895453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032781195,0.00019054141,0.00031501713,0.00046054664,0.00014522317,0.00037261576,0.0008591799,0.00007264378,0.00007750616],"category_scores_gemma":[0.00034758763,0.0001704627,0.000058370104,0.0028722025,0.00002722111,0.0017900153,0.0006078151,0.000082948005,0.000020641592],"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":[0.000008464174,0.00005016683,0.0010050535,0.000009549062,0.0003652689,0.0000010958067,0.00021284953,0.012746686,0.00001405728,0.0017916724,0.0014594948,0.9823356],"study_design_scores_gemma":[0.0009623191,0.00008775826,0.004377898,0.000002676903,0.000089969486,0.000002386267,0.000027669768,0.99115217,0.00014831609,0.0014987048,0.0014205885,0.00022955645],"about_ca_topic_score_codex":0.0002742385,"about_ca_topic_score_gemma":0.000059335507,"teacher_disagreement_score":0.9821061,"about_ca_system_score_codex":0.00007749692,"about_ca_system_score_gemma":0.00015758067,"threshold_uncertainty_score":0.6951268},"labels":[],"label_agreement":null},{"id":"W2076091564","doi":"10.1016/j.compstruct.2012.03.021","title":"Surrogate-based multi-objective optimization of a composite laminate with curvilinear fibers","year":2012,"lang":"en","type":"article","venue":"Composite Structures","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":134,"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":"Curvilinear coordinates; Buckling; Stiffness; Isotropy; Structural engineering; Materials science; Fiber; Composite number; Boundary value problem; Perpendicular; Composite material; Mathematics; Engineering; Geometry; Mathematical analysis","score_opus":0.012203477880992039,"score_gpt":0.2585924993968429,"score_spread":0.24638902151585088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076091564","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":[0.015958091,0.0003153053,0.9822707,0.00007105693,0.00034204163,0.00048224762,0.000031360734,0.00024810267,0.00028106308],"genre_scores_gemma":[0.43972293,0.000005070256,0.56004775,0.00008824017,0.00002788311,0.00001220265,0.00003796006,0.000026580834,0.000031391453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798447,0.00020046768,0.00037754845,0.0005056563,0.00043077345,0.00050109945],"domain_scores_gemma":[0.9980613,0.0001828872,0.00039923977,0.00064708607,0.0005384741,0.00017103285],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015923736,0.00037502474,0.0004086266,0.00030198548,0.00021896201,0.000082245875,0.0006546359,0.00011001751,0.00002234247],"category_scores_gemma":[0.00003601696,0.00032091813,0.00009680752,0.0008532876,0.00023902359,0.00094393804,0.0001834048,0.00022897075,0.000005441287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100570745,0.00016718176,0.0058278646,0.000039007846,0.0000699876,0.0000059181248,0.0010539708,0.9854384,0.0038264343,0.001083134,0.00000927306,0.0023782596],"study_design_scores_gemma":[0.002337536,0.0001552947,0.022290139,0.000055533266,0.00003704145,0.000032693464,0.00004795205,0.9006567,0.073737144,0.000083697756,0.00009242387,0.00047381723],"about_ca_topic_score_codex":0.000037649872,"about_ca_topic_score_gemma":0.00000568569,"teacher_disagreement_score":0.42376482,"about_ca_system_score_codex":0.000119139084,"about_ca_system_score_gemma":0.0000787731,"threshold_uncertainty_score":0.9999243},"labels":[],"label_agreement":null},{"id":"W2078135442","doi":"10.1115/detc2006-99560","title":"Developing Multiple Diverse Potential Designs for Heat Transfer Utilizing Graph Based Evolutionary Algorithms","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Population; Mathematical optimization; Evolutionary algorithm; Graph; Computer science; Premature convergence; Convergence (economics); Evolutionary computation; Mathematics; Genetic algorithm; Theoretical computer science","score_opus":0.04076738277535654,"score_gpt":0.2695126757341008,"score_spread":0.22874529295874427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078135442","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":[0.00034400265,0.00006395995,0.99719006,0.0003813877,0.00048871816,0.00074671494,0.000026394622,0.0005175335,0.00024120738],"genre_scores_gemma":[0.16445841,0.0000048037864,0.83483934,0.00029627586,0.00009220978,0.0001002534,0.000048487243,0.000024636376,0.00013559523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980385,0.000068108726,0.00035320834,0.0006852074,0.00033584124,0.00051914837],"domain_scores_gemma":[0.99894536,0.00021752408,0.000024902885,0.00033592407,0.00037874476,0.00009756927],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017472703,0.00026929166,0.0002176868,0.00027465724,0.0005319926,0.00010037384,0.0005049199,0.0001024495,0.000025840856],"category_scores_gemma":[0.000040637744,0.00027027135,0.00018154443,0.00062671694,0.00008218185,0.00091627333,0.00008611598,0.00010247324,0.000012714926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007087558,0.00033693315,0.00078580773,0.000039565813,0.000047345402,0.00003761406,0.00014008938,0.9275062,0.0074054743,0.04753993,0.0005965962,0.015493564],"study_design_scores_gemma":[0.0020663075,0.00005540573,0.0020053627,0.00001622728,0.000010975548,0.0000119507185,0.000058224457,0.97995025,0.012235983,0.0026307567,0.0005519199,0.00040662877],"about_ca_topic_score_codex":0.00016063113,"about_ca_topic_score_gemma":0.00004580184,"teacher_disagreement_score":0.16411442,"about_ca_system_score_codex":0.00021075008,"about_ca_system_score_gemma":0.00018816121,"threshold_uncertainty_score":0.99997497},"labels":[],"label_agreement":null},{"id":"W2078153679","doi":"10.1115/gt2010-23011","title":"Optimization of a Gas Turbine Engine Rotor Disc Using Case-Based Reasoning and the GATE Genetic Algorithm","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Initialization; Genetic algorithm; Artificial neural network; Computer science; Rotor (electric); Upgrade; Process (computing); Algorithm; Case-based reasoning; Field (mathematics); Population; Engineering design process; Local search (optimization); Mathematical optimization; Engineering; Artificial intelligence; Mechanical engineering; Machine learning; Mathematics","score_opus":0.007190403796450365,"score_gpt":0.23808501216304406,"score_spread":0.2308946083665937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078153679","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":[0.004563756,0.000053083553,0.9943648,0.00018640213,0.00019070847,0.00046703205,0.0000041584726,0.00009298317,0.000077107834],"genre_scores_gemma":[0.051962055,0.000009598049,0.94781274,0.00007697135,0.000049412276,0.000021984775,0.0000026452549,0.000017592198,0.00004697587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893326,0.00008028273,0.0002679613,0.0003319955,0.00018727484,0.0001992119],"domain_scores_gemma":[0.99887383,0.00017803453,0.00017896782,0.00044501928,0.00024249192,0.00008167939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026856535,0.00016189291,0.0001959614,0.00011089968,0.00017470754,0.000091499394,0.00025226682,0.000054850287,0.00003159188],"category_scores_gemma":[0.00016102385,0.0001126678,0.000044015116,0.0004429586,0.00019105032,0.00032838937,0.00013873998,0.00016010605,7.8814764e-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":[0.000008367085,0.000025990988,0.00003099186,0.000005660131,0.000009780856,0.000032757132,0.00017155414,0.9751738,0.0003685686,0.00076315674,0.0000013237366,0.023408098],"study_design_scores_gemma":[0.0016666645,0.000028144223,0.000053984753,0.000013667277,0.000014149489,0.00045832095,0.000024340663,0.99452376,0.0029529457,0.0001070575,0.000014031137,0.00014294524],"about_ca_topic_score_codex":0.00012841768,"about_ca_topic_score_gemma":0.000012241111,"teacher_disagreement_score":0.047398303,"about_ca_system_score_codex":0.000022435419,"about_ca_system_score_gemma":0.00006275266,"threshold_uncertainty_score":0.45944595},"labels":[],"label_agreement":null},{"id":"W2079018405","doi":"10.1115/detc2013-12663","title":"Visual HDMR Model Refinement Through Iterative Interaction","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Simon Fraser University","funders":"","keywords":"Metamodeling; Computer science; Representation (politics); Radial basis function; Surrogate model; Iterative method; Sampling (signal processing); Basis (linear algebra); Mathematical optimization; Algorithm; Artificial intelligence; Machine learning; Mathematics; Computer vision; Artificial neural network","score_opus":0.022031703522444972,"score_gpt":0.31110241241991077,"score_spread":0.2890707088974658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079018405","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":[0.0007094939,0.000007731922,0.974434,0.00086999143,0.00023580014,0.00027892485,5.9847423e-7,0.0002389377,0.023224501],"genre_scores_gemma":[0.13910115,0.000009162144,0.85515696,0.0012266375,0.000034434837,0.0000946229,0.0000044293374,0.000009617205,0.0043629995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989817,0.000034737866,0.00020823102,0.0003786172,0.000197245,0.00019943576],"domain_scores_gemma":[0.9992598,0.000042717562,0.000085773514,0.00027059895,0.0002848429,0.00005625504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004629789,0.00014057965,0.00011103108,0.00006235375,0.00010697904,0.00018698085,0.00026767995,0.000035968023,0.0002881297],"category_scores_gemma":[0.000028147948,0.000118020274,0.000037805075,0.0002444663,0.00002426819,0.0029750587,0.00020204677,0.000106382955,0.00044023548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009413845,0.00045545987,0.0000424028,0.000008150725,0.000051741234,0.000004479194,0.0056871586,0.7613331,0.003961943,0.099712946,0.0059605907,0.1227726],"study_design_scores_gemma":[0.00027543225,0.000058418107,0.0000590896,0.0000060742013,0.0000013002256,0.0000040965265,0.00012226154,0.9875062,0.0065099555,0.004810772,0.0004946877,0.00015169421],"about_ca_topic_score_codex":0.000053809443,"about_ca_topic_score_gemma":0.000005488486,"teacher_disagreement_score":0.2261731,"about_ca_system_score_codex":0.00012038566,"about_ca_system_score_gemma":0.000029716864,"threshold_uncertainty_score":0.56584835},"labels":[],"label_agreement":null},{"id":"W2080044387","doi":"10.1080/15502287.2012.682196","title":"Global Optimization Using Mixed Surrogates and Space Elimination in Computationally Intensive Engineering Designs","year":2012,"lang":"en","type":"article","venue":"International Journal for Computational Methods in Engineering Science and Mechanics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 Victoria","funders":"","keywords":"Surrogate model; Global optimization; Benchmark (surveying); Kriging; Mathematical optimization; Latin hypercube sampling; Metamodeling; Computer science; Optimization problem; Computation; Field (mathematics); Algorithm; Mathematics; Machine learning","score_opus":0.04075989357208958,"score_gpt":0.3803735774643139,"score_spread":0.3396136838922243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080044387","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":[0.0060042418,0.00017119075,0.99111444,0.00033495403,0.0021017767,0.00022439838,0.000006546967,0.00003908221,0.0000033679494],"genre_scores_gemma":[0.23112896,0.000027049304,0.76867974,0.000066342545,0.00007108103,0.000011024711,0.000004334808,0.000010189977,0.0000013034996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982692,0.00006344996,0.00041782085,0.0003231236,0.0005703281,0.0003560534],"domain_scores_gemma":[0.99723375,0.0006592953,0.0001828024,0.00007101173,0.0016913424,0.00016179457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024560415,0.00018357603,0.00018764014,0.00085832266,0.00012362155,0.000337379,0.00039922114,0.00006334526,7.372495e-7],"category_scores_gemma":[0.0026434478,0.00020410283,0.000033150263,0.00097228005,0.00004247121,0.0027383738,0.00018512322,0.00016405205,1.489818e-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":[0.000007876301,0.000027669194,0.00035312955,0.000008302335,0.000011556315,0.000003019275,0.00028952325,0.9117337,0.00066359114,0.08157619,7.807984e-7,0.005324685],"study_design_scores_gemma":[0.0006845014,0.000032421234,0.0045748935,0.00008157886,0.0000048763213,0.0003536631,0.00012756237,0.98753196,0.0002562834,0.0061346777,0.000014984127,0.00020260649],"about_ca_topic_score_codex":0.0000049781297,"about_ca_topic_score_gemma":0.0000012305038,"teacher_disagreement_score":0.22512472,"about_ca_system_score_codex":0.0007165866,"about_ca_system_score_gemma":0.00020117156,"threshold_uncertainty_score":0.83230716},"labels":[],"label_agreement":null},{"id":"W2080313552","doi":"10.1007/s00158-014-1170-3","title":"Differential geometry tools for multidisciplinary design optimization, part II: application to QSD","year":2014,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Cambridge Trust; Cambridge Commonwealth Trust; Cambridge Overseas Trust","keywords":"Multidisciplinary design optimization; Computer science; Convergence (economics); Multidisciplinary approach; Mathematical optimization; Differential (mechanical device); Decomposition; Mathematics; Engineering; Aerospace engineering","score_opus":0.017875525487434304,"score_gpt":0.27519214207137194,"score_spread":0.2573166165839376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080313552","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":[0.0036264174,0.000037702415,0.9913225,0.001104687,0.00086825684,0.0024863544,0.00005126834,0.0004419887,0.00006082502],"genre_scores_gemma":[0.09764125,0.00004457248,0.9005467,0.00011676901,0.00035092048,0.0005333521,0.00044219644,0.00006095087,0.00026324528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99687505,0.00016228842,0.0006758982,0.001307577,0.0003764161,0.000602765],"domain_scores_gemma":[0.99757934,0.0004081903,0.0003433907,0.00071598415,0.00058731914,0.0003657942],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00029523566,0.0005229782,0.00045460416,0.00037716512,0.0017836003,0.0003636667,0.0006684333,0.00021184546,0.00003506552],"category_scores_gemma":[0.00037699108,0.00048411373,0.000112979906,0.00088809367,0.00011763786,0.0021651636,0.0008933747,0.00016684455,0.000005901217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010722873,0.000058138943,0.00006240214,0.000037124253,0.000022464808,5.5406844e-7,0.0007475122,0.9663735,0.00041235847,0.0033539683,0.00006627723,0.028758451],"study_design_scores_gemma":[0.0016571737,0.00048682012,0.0009359031,0.00003165913,0.000044413206,0.000019186164,0.000095419244,0.993558,0.0009123854,0.0014394391,0.00015597425,0.00066364097],"about_ca_topic_score_codex":0.0000056021627,"about_ca_topic_score_gemma":0.0000029268026,"teacher_disagreement_score":0.09401483,"about_ca_system_score_codex":0.00012966685,"about_ca_system_score_gemma":0.00005998399,"threshold_uncertainty_score":0.99976104},"labels":[],"label_agreement":null},{"id":"W2080464723","doi":"10.1016/j.eswa.2011.11.093","title":"Design of prestressed concrete flat slab using modern heuristic optimization techniques","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Western University","funders":"","keywords":"Heuristic; Slab; Computer science; Mathematical optimization; Constraint (computer-aided design); Optimization problem; Finite element method; Boundary (topology); Genetic algorithm; Optimal design; Algorithm; Structural engineering; Mathematics; Engineering; Machine learning; Mechanical engineering","score_opus":0.0432689488108323,"score_gpt":0.2715322232163039,"score_spread":0.2282632744054716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080464723","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":[0.000014518078,0.0003494936,0.99666655,0.000010317032,0.000051196825,0.0018997636,0.000006479049,0.0003912179,0.0006104546],"genre_scores_gemma":[0.07386983,0.000031415762,0.9246516,0.00002024263,0.00004061413,0.0012878437,0.000007976689,0.000033455453,0.000056984318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856204,0.000113605034,0.00039700678,0.00046817778,0.00024903368,0.00021016331],"domain_scores_gemma":[0.99810785,0.00008740963,0.00039132356,0.00082713197,0.00049448054,0.00009178724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013012247,0.00019860726,0.00025342623,0.00015592956,0.00016817752,0.000051063736,0.0005613045,0.00008376755,0.0000058136575],"category_scores_gemma":[0.000019041197,0.00017822634,0.000026657533,0.0005543989,0.000103028484,0.00051885453,0.00008346603,0.00007565271,0.0000037739735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033734792,0.00004474929,0.00004422268,0.00005096456,0.000059798083,0.000004135335,0.0034193504,0.96423006,0.022744717,0.007172366,0.00004499841,0.00215088],"study_design_scores_gemma":[0.00023147622,0.00006317604,0.0000033455472,0.00006441743,0.000009112771,0.000027647498,0.00006813247,0.97124803,0.027799238,0.0001850534,0.0000912957,0.00020904679],"about_ca_topic_score_codex":0.000118244,"about_ca_topic_score_gemma":3.927182e-7,"teacher_disagreement_score":0.07385532,"about_ca_system_score_codex":0.000086629836,"about_ca_system_score_gemma":0.00011816153,"threshold_uncertainty_score":0.72678596},"labels":[],"label_agreement":null},{"id":"W2080927183","doi":"10.1145/2808234","title":"A Review and Taxonomy of Interactive Optimization Methods in Operations Research","year":2015,"lang":"en","type":"review","venue":"ACM Transactions on Interactive Intelligent Systems","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":130,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Deutsche Forschungsgemeinschaft","keywords":"Computer science; Optimization problem; Multi-objective optimization; Process (computing); Perspective (graphical); Test functions for optimization; Basis (linear algebra); Engineering optimization; Artificial intelligence; Machine learning; Management science; Multi-swarm optimization; Mathematics; Engineering; Algorithm","score_opus":0.2519919103655676,"score_gpt":0.502770982196378,"score_spread":0.25077907183081033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080927183","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.746781e-9,0.47621152,0.5198059,0.000038674265,0.0005468424,0.003083988,0.00005088529,0.000043883694,0.00021831079],"genre_scores_gemma":[0.0000046053765,0.68735754,0.309041,0.000032608168,0.000028949278,0.0031587367,0.000030663377,0.000056078967,0.00028981044],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9913846,0.0042235963,0.002036294,0.001331137,0.00053937815,0.0004849493],"domain_scores_gemma":[0.99215424,0.0030678012,0.0006392901,0.0017366698,0.0021676635,0.00023431156],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0028001317,0.00070157833,0.0025407125,0.0023450467,0.00018443936,0.00020926521,0.0017020458,0.0003452213,0.00006687015],"category_scores_gemma":[0.0012296312,0.0006290315,0.00035394114,0.002877982,0.0002080013,0.0014967485,0.000170171,0.001836833,0.00006090812],"study_design_candidate":"design_other","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":[0.000015332582,0.00042319464,1.0761997e-7,0.0068853428,0.00033307498,0.0000072630096,0.0006577578,0.15424614,6.365681e-7,0.00027648773,0.0000747166,0.83707994],"study_design_scores_gemma":[0.00042858845,0.00053973455,1.3994968e-7,0.081304185,0.0004906212,0.00027147747,0.0010643193,0.21809965,0.00008288979,0.00009874971,0.6966072,0.0010124728],"about_ca_topic_score_codex":0.00034864387,"about_ca_topic_score_gemma":0.000039624556,"teacher_disagreement_score":0.8360675,"about_ca_system_score_codex":0.0016761953,"about_ca_system_score_gemma":0.00066986255,"threshold_uncertainty_score":0.9996161},"labels":[],"label_agreement":null},{"id":"W2081686652","doi":"10.4236/wsn.2011.38029","title":"Modeling of Data Reduction in Wireless Sensor Networks","year":2011,"lang":"en","type":"article","venue":"Wireless Sensor Network","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Wireless sensor network; Asynchronous communication; Wireless network; Reduction (mathematics); Real-time computing; Field (mathematics); Distributed computing; Wireless; Data mining; Computer network; Telecommunications","score_opus":0.05876249012014325,"score_gpt":0.2731683338151399,"score_spread":0.21440584369499663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081686652","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":[0.022356749,0.00016501074,0.97512585,0.000043900785,0.0009668298,0.00038300426,0.000007766414,0.00022900284,0.0007218714],"genre_scores_gemma":[0.5965905,0.0002152713,0.40271744,0.000034488192,0.00030573725,0.000011224124,0.000027273445,0.000037703583,0.000060338938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969703,0.00023556537,0.00074193237,0.0010299152,0.00034450062,0.0006777631],"domain_scores_gemma":[0.9973817,0.00008960692,0.00030610868,0.0018233778,0.00026048804,0.00013872462],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00053275406,0.00032114537,0.0004967776,0.00017804778,0.0001385091,0.000045614448,0.0014926002,0.00020017235,0.000015158553],"category_scores_gemma":[0.00002726576,0.00034268247,0.00006786652,0.0013520783,0.00011675812,0.001232862,0.00070494146,0.0004038471,0.000011670694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004505545,0.000112824455,0.0004187966,0.000012336149,0.000024584531,0.000025665333,0.00050360104,0.97410035,0.00010420902,0.002493112,0.00004416666,0.022115277],"study_design_scores_gemma":[0.00054421736,0.000036741516,0.00024367757,0.00011619256,0.000011103618,0.00004180076,0.00014941751,0.9978934,0.00021758786,0.000362538,0.000023623525,0.0003597082],"about_ca_topic_score_codex":0.0002313633,"about_ca_topic_score_gemma":0.00005735061,"teacher_disagreement_score":0.5742338,"about_ca_system_score_codex":0.00009120079,"about_ca_system_score_gemma":0.00006549948,"threshold_uncertainty_score":0.99990255},"labels":[],"label_agreement":null},{"id":"W2082752737","doi":"10.1080/15598608.2011.10412055","title":"Follow-Up Experimental Designs for Computer Models and Physical Processes","year":2011,"lang":"en","type":"article","venue":"Journal of Statistical Theory and Practice","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Simon Fraser University; Acadia University","funders":"National Nuclear Security Administration; National Science Foundation","keywords":"Process (computing); Field (mathematics); Computer experiment; Physical system; Computer science; Physical science; Mathematics; Simulation","score_opus":0.08667361235577452,"score_gpt":0.35979388779304217,"score_spread":0.27312027543726763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082752737","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":[0.00049853144,0.00023581964,0.9983856,0.00007148296,0.00015847667,0.00012531623,0.000008214285,0.000011911867,0.0005046356],"genre_scores_gemma":[0.1796542,0.000049318,0.81993324,0.00023936166,0.0000825153,0.0000050412887,3.6769748e-7,0.0000073489123,0.000028629594],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99904615,0.0002939027,0.00020761232,0.00017119404,0.00014979928,0.00013135349],"domain_scores_gemma":[0.9934078,0.0057640914,0.00023633402,0.00007769908,0.00037821766,0.00013588252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000807586,0.00010609754,0.0001863415,0.00004354841,0.00011390824,0.0000821146,0.00014848626,0.000028610004,0.0000078845815],"category_scores_gemma":[0.001378851,0.00008326322,0.000023448858,0.00008129878,0.00012514519,0.002026614,0.00007127399,0.00012889899,0.0000011321246],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017408862,0.00040246974,0.0000020680673,0.000034677112,0.00007672391,0.000050671864,0.0077775763,0.00046688638,0.00022980853,0.9636153,0.00010464479,0.025498262],"study_design_scores_gemma":[0.002522673,0.0022711263,0.000043714233,0.000042708296,0.00010183698,0.001199588,0.0013444825,0.2858364,0.0033261434,0.70252866,0.0005040313,0.0002786427],"about_ca_topic_score_codex":6.2600617e-7,"about_ca_topic_score_gemma":4.5514582e-8,"teacher_disagreement_score":0.28536952,"about_ca_system_score_codex":0.000014157758,"about_ca_system_score_gemma":0.00006189783,"threshold_uncertainty_score":0.33953756},"labels":[],"label_agreement":null},{"id":"W2083130261","doi":"10.1115/detc2005-84425","title":"Modeling of Non-Linear Relations Among Different Design Evaluation Measures for Multi-Objective Design Optimization","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Weighting; Relation (database); Measure (data warehouse); Computer science; Calipers; Design of experiments; Multi-objective optimization; Evaluation methods; Design methods; Mathematical optimization; Mathematics; Reliability engineering; Data mining; Engineering; Statistics","score_opus":0.09695934750448543,"score_gpt":0.3319777351398806,"score_spread":0.23501838763539518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083130261","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":[0.00017492835,0.000049093356,0.99640363,0.00007668591,0.00014891732,0.002835644,0.000004062816,0.00018592176,0.0001211415],"genre_scores_gemma":[0.25176114,0.000013600674,0.74761087,0.00002656825,0.000046099278,0.00035912462,0.000013817789,0.00002697403,0.00014178132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99762726,0.00031557327,0.0005893038,0.00059856725,0.00057140156,0.00029790786],"domain_scores_gemma":[0.9969016,0.000345978,0.00026838508,0.00044980113,0.0019328505,0.00010143107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011153879,0.00026447186,0.0002911488,0.00032349225,0.00026870726,0.0000586208,0.00041039143,0.00013199536,0.000024899677],"category_scores_gemma":[0.00071472273,0.0002447528,0.0001112717,0.0004998165,0.000053203858,0.001449757,0.000082180806,0.000119683755,0.0000060733482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027513152,0.00018376044,0.00003674637,0.00000387366,0.00003673027,8.9300194e-8,0.00083848817,0.98680156,0.0002619998,0.00028652887,0.000013676589,0.011509004],"study_design_scores_gemma":[0.0019445764,0.00011884475,0.00018696855,0.000026662703,0.000047629022,0.0000011254497,0.00007889098,0.988372,0.008622419,0.00032910428,0.0000011466735,0.00027062366],"about_ca_topic_score_codex":0.000017669567,"about_ca_topic_score_gemma":0.000018825242,"teacher_disagreement_score":0.25158623,"about_ca_system_score_codex":0.0003637947,"about_ca_system_score_gemma":0.00019430835,"threshold_uncertainty_score":0.9980729},"labels":[],"label_agreement":null},{"id":"W2084165293","doi":"10.1162/evco.2010.18.1.18105","title":"Strength Pareto Particle Swarm Optimization and Hybrid EA-PSO for Multi-Objective Optimization","year":2010,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Particle swarm optimization; Evolutionary algorithm; Mathematical optimization; Pareto principle; Multi-swarm optimization; Convergence (economics); Imperialist competitive algorithm; Multi-objective optimization; Computer science; Metaheuristic; Mathematics; Algorithm","score_opus":0.01579169677716093,"score_gpt":0.2768926140270181,"score_spread":0.2611009172498572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084165293","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":[0.0061417567,0.00009438154,0.9909244,0.00036555884,0.00086964347,0.0010297772,0.000042182648,0.00046935808,0.00006291154],"genre_scores_gemma":[0.2819073,0.00002170283,0.71742827,0.00009642571,0.00011455129,0.00013197598,0.00020369621,0.000029223364,0.00006687657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794585,0.000112044814,0.0004447569,0.00079993956,0.00031865455,0.0003787593],"domain_scores_gemma":[0.9981132,0.00028913625,0.00029792718,0.0003171945,0.00080232014,0.00018018899],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002668636,0.00028723778,0.00023248224,0.00020479702,0.00057260884,0.0001774531,0.00028874018,0.000110488916,0.000014236832],"category_scores_gemma":[0.0003514225,0.00032268095,0.000071748815,0.0005497517,0.00013371014,0.0018779219,0.00015974087,0.00022054078,0.00000953096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023866052,0.00019200746,0.00030727507,0.000011693781,0.000021536303,0.000002152741,0.000308235,0.98732287,0.00021412938,0.004065083,0.0001156514,0.0074155224],"study_design_scores_gemma":[0.001820532,0.00015598186,0.0027714253,0.000012282281,0.000017386128,0.00004308869,0.000066850684,0.9925848,0.00089257554,0.0011726776,0.00009326395,0.000369104],"about_ca_topic_score_codex":0.000015047833,"about_ca_topic_score_gemma":0.000012252667,"teacher_disagreement_score":0.27576554,"about_ca_system_score_codex":0.00014994906,"about_ca_system_score_gemma":0.00014708981,"threshold_uncertainty_score":0.9999225},"labels":[],"label_agreement":null},{"id":"W2085781772","doi":"10.1115/detc2009-87121","title":"Hybrid and Adaptive Metamodel Based Global Optimization","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 Victoria","funders":"","keywords":"Metamodeling; Computer science; Benchmark (surveying); Global optimization; Computation; Mathematical optimization; Optimization problem; Process (computing); Identification (biology); Metaheuristic; Algorithm; Mathematics","score_opus":0.012211326574668335,"score_gpt":0.24863645431364606,"score_spread":0.23642512773897773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085781772","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":[0.000043921264,0.000051200903,0.9923141,0.000688148,0.00006566029,0.00017058206,0.000004289693,0.0002779212,0.0063841552],"genre_scores_gemma":[0.12906675,0.000007712256,0.8695176,0.0012721053,0.000012972509,0.000004382483,0.0000036697668,0.0000036856775,0.000111085865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906176,0.00004046168,0.0001420441,0.0003934237,0.00018035821,0.00018192024],"domain_scores_gemma":[0.9993707,0.000029680037,0.000061715815,0.00026578567,0.00016948993,0.00010266296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000848625,0.0001351779,0.00012680498,0.000055930534,0.00009502793,0.00009426178,0.00024191011,0.00002764877,0.000023158822],"category_scores_gemma":[0.0000363905,0.00012466496,0.000029982582,0.00029648928,0.00003070005,0.0008331601,0.000056414734,0.00004943159,0.0000073179435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059855192,0.000039989893,0.000010585069,4.508167e-7,0.000004064175,0.0000045477595,0.000013538308,0.9372159,0.0000084259045,0.033311207,0.000054407174,0.029330876],"study_design_scores_gemma":[0.00051467796,0.00009297687,0.00031441415,0.0000030786207,0.000004238729,0.000012306818,0.000005351714,0.9936808,0.000536709,0.0046420665,0.0000386183,0.00015481483],"about_ca_topic_score_codex":0.000005302445,"about_ca_topic_score_gemma":8.415388e-7,"teacher_disagreement_score":0.12902284,"about_ca_system_score_codex":0.00007651359,"about_ca_system_score_gemma":0.000052751006,"threshold_uncertainty_score":0.50836897},"labels":[],"label_agreement":null},{"id":"W2085800607","doi":"10.1115/gt2008-51181","title":"Automated Preliminary Structural Rotor Design Using Genetic Algorithms and Neural Networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Algorithm; Artificial neural network; Genetic algorithm; Computer science; Engineering design process; Aerodynamics; Sensitivity (control systems); Rotor (electric); Process (computing); Population; Optimal design; Set (abstract data type); Mathematical optimization; Control engineering; Engineering; Artificial intelligence; Machine learning; Mathematics","score_opus":0.03128943442036555,"score_gpt":0.2735420989682373,"score_spread":0.24225266454787173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085800607","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":[0.013561713,0.00025957453,0.9843613,0.00003095681,0.00026708603,0.00050604076,6.9792264e-7,0.0009851464,0.00002747584],"genre_scores_gemma":[0.21709119,0.000019079038,0.7825753,0.00013338384,0.00006501346,0.000013765711,0.0000014374964,0.000019452169,0.00008138415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984784,0.00012658632,0.00026224405,0.00053241337,0.00021292969,0.00038741468],"domain_scores_gemma":[0.999127,0.00010609865,0.00010735224,0.0003550815,0.00014134085,0.00016317249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006827155,0.00023620923,0.00019943662,0.00011074531,0.0003804583,0.00008941021,0.0003769186,0.00008620619,0.000014408228],"category_scores_gemma":[0.000029338493,0.00021310663,0.000036453202,0.0004394104,0.00013479924,0.00075107696,0.00027959922,0.00013768468,0.0000022450006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010314988,0.000011305493,0.00034683262,0.0000021239784,0.000010130078,0.00008590655,0.00018509544,0.9860141,0.000070684844,0.000037573194,0.00004801015,0.013177911],"study_design_scores_gemma":[0.0005016042,0.00014763114,0.014934164,0.0000049078194,0.000005494428,0.0010790499,0.000010180142,0.9827279,0.0002454543,0.00006654251,0.0000037921723,0.0002732813],"about_ca_topic_score_codex":0.000026521646,"about_ca_topic_score_gemma":3.7559136e-7,"teacher_disagreement_score":0.20352948,"about_ca_system_score_codex":0.00006590179,"about_ca_system_score_gemma":0.000049258637,"threshold_uncertainty_score":0.8690236},"labels":[],"label_agreement":null},{"id":"W2086136405","doi":"10.1115/detc2014-34778","title":"Problem Formulations for Simulation-Based Design Optimization Using Statistical Surrogates and Direct Search","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Computer science; Surrogate model; Simulation-based optimization; Derivative-free optimization; Multidisciplinary design optimization; Optimization problem; Algorithm; Multi-swarm optimization; Mathematics; Multidisciplinary approach","score_opus":0.04865802477201221,"score_gpt":0.3277754521897883,"score_spread":0.2791174274177761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086136405","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":[0.000028248945,0.000005119033,0.99860215,0.00009407905,0.00004086107,0.000799481,0.000009293218,0.00018932737,0.00023144679],"genre_scores_gemma":[0.20301235,7.401683e-7,0.796804,0.00005763134,0.000017802593,0.000030366804,0.000020644939,0.00001508273,0.000041386404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988467,0.00013129854,0.00022328587,0.00037924835,0.00018196914,0.00023750488],"domain_scores_gemma":[0.99616593,0.0030344222,0.00006671406,0.00020353498,0.0004312949,0.000098101555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004266021,0.00013144194,0.0001442625,0.00014396584,0.00031549804,0.0001689865,0.00014361505,0.00005040778,0.00001551346],"category_scores_gemma":[0.00048569535,0.00012451375,0.000022788103,0.00031400291,0.000050698756,0.0005951553,0.000049293427,0.00004715707,0.0000016446724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008656052,0.000029402812,0.00015845048,0.000011071591,0.000004725303,9.978801e-8,0.00006279093,0.9798901,0.000033937275,0.013251429,0.0000027172991,0.006546584],"study_design_scores_gemma":[0.00071122625,0.00008699325,0.00011004959,0.000008927958,0.0000073528213,5.6858556e-7,0.0000050470444,0.9961155,0.00053535745,0.0022252765,0.000031893574,0.00016179682],"about_ca_topic_score_codex":0.000011038056,"about_ca_topic_score_gemma":0.0000027804865,"teacher_disagreement_score":0.2029841,"about_ca_system_score_codex":0.00006385496,"about_ca_system_score_gemma":0.00008483941,"threshold_uncertainty_score":0.50775236},"labels":[],"label_agreement":null},{"id":"W2086314248","doi":"10.1007/s10732-007-9042-2","title":"Pareto memetic algorithm for multiple objective optimization with an industrial application","year":2007,"lang":"en","type":"article","venue":"Journal of Heuristics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université du Québec à Chicoutimi","funders":"","keywords":"Memetic algorithm; Mathematical optimization; Heuristics; Knapsack problem; Computer science; Pareto principle; Local search (optimization); Combinatorial optimization; Scheduling (production processes); Job shop scheduling; Continuous knapsack problem; Multi-objective optimization; Algorithm; Mathematics","score_opus":0.020248092557960003,"score_gpt":0.28023996852129207,"score_spread":0.25999187596333206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086314248","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":[0.00023072462,0.000031732998,0.99864876,0.000060868453,0.00043704512,0.00048365313,0.000013008128,0.000046154502,0.000048020796],"genre_scores_gemma":[0.036524124,0.000014474964,0.9628078,0.000080939055,0.0005035297,0.000011181899,0.000012070451,0.000026043444,0.000019856085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840605,0.000058934427,0.0005767092,0.00026417073,0.00042882047,0.00026532984],"domain_scores_gemma":[0.9964947,0.00043942203,0.0008317108,0.0002860712,0.001710892,0.00023718132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079749984,0.00017282374,0.00026296236,0.00026197848,0.00015775564,0.00008960368,0.00045660755,0.00011021077,0.000002419081],"category_scores_gemma":[0.0006714649,0.00014762416,0.000061012008,0.0005299842,0.000058166206,0.00079180725,0.000042935055,0.00024643107,9.935254e-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":[0.00021873148,0.0003380774,0.00044037492,0.000006700921,0.000061992614,0.000029406732,0.00043162965,0.7215399,0.000083553,0.0007400905,0.00007741288,0.2760321],"study_design_scores_gemma":[0.0024954337,0.0010512909,0.00024327119,0.00001897985,0.000036580266,0.0001201462,0.00017048145,0.9929512,0.0015019516,0.00054547586,0.0006682526,0.00019693414],"about_ca_topic_score_codex":0.000004419075,"about_ca_topic_score_gemma":0.000007957188,"teacher_disagreement_score":0.27583516,"about_ca_system_score_codex":0.0002119688,"about_ca_system_score_gemma":0.00022628899,"threshold_uncertainty_score":0.60199386},"labels":[],"label_agreement":null},{"id":"W2086609533","doi":"10.1007/bf03040964","title":"Procedural texture evolution using multi-objective optimization","year":2004,"lang":"en","type":"article","venue":"New Generation Computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada; Universities Space Research Association","keywords":"Computer science; Pareto principle; Multi-objective optimization; Ranking (information retrieval); Artificial intelligence; Genetic programming; Feature (linguistics); Evolutionary algorithm; Pattern recognition (psychology); Population; Abstraction; Feature vector; Data mining; Machine learning; Mathematical optimization; Mathematics","score_opus":0.032522054657071545,"score_gpt":0.2886651881681984,"score_spread":0.2561431335111269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086609533","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":[0.0021434363,0.00010658434,0.9954552,0.00028372387,0.0009223856,0.00048673293,0.0000014133379,0.00048497456,0.00011555598],"genre_scores_gemma":[0.31803823,0.0000023564057,0.6810245,0.0002666338,0.00056726736,0.0000035033363,0.00001834599,0.000022347516,0.000056842382],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805504,0.000081595856,0.00041190544,0.00072072336,0.0003562954,0.00037445244],"domain_scores_gemma":[0.9986432,0.000027522457,0.00031062603,0.00035038937,0.0005133716,0.00015487905],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017551276,0.0002740677,0.00020174206,0.00024451956,0.000612035,0.0002843705,0.00037166488,0.0001355006,0.00000739764],"category_scores_gemma":[0.00017227588,0.00029748378,0.00007301422,0.0010368205,0.00003519909,0.0013806974,0.00018241264,0.00021481511,0.000016736345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023932917,0.000055776964,0.00006758549,0.0000038015032,0.0000112964435,0.000002740107,0.00087753776,0.98044914,0.0040289154,0.0033526267,0.000021259633,0.011126908],"study_design_scores_gemma":[0.0015039385,0.000055222416,0.00036064052,0.000031197524,0.000008118969,0.000048192804,0.00005888435,0.99243176,0.004820436,0.00033575395,0.000015933847,0.00032991785],"about_ca_topic_score_codex":0.000098759534,"about_ca_topic_score_gemma":0.000026753669,"teacher_disagreement_score":0.31589478,"about_ca_system_score_codex":0.00086989155,"about_ca_system_score_gemma":0.00057862356,"threshold_uncertainty_score":0.9999477},"labels":[],"label_agreement":null},{"id":"W2087794678","doi":"10.1115/detc2009-86973","title":"On the Application of Multi-Objective Parallel Asynchronous Particle Swarm Optimization to Engineering Design Problems","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Particle swarm optimization; Benchmark (surveying); Computer science; Asynchronous communication; Multi-swarm optimization; Metaheuristic; Task (project management); Mathematical optimization; Multi-objective optimization; Parallel computing; Algorithm; Mathematics; Engineering","score_opus":0.018599729608089415,"score_gpt":0.2504233093471802,"score_spread":0.2318235797390908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087794678","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":[0.00022146164,0.000016538148,0.99737626,0.00071408367,0.000047722668,0.0012283737,8.3007734e-7,0.00023864552,0.00015610483],"genre_scores_gemma":[0.34309354,0.000004040629,0.6564369,0.000301529,0.00001029593,0.00010999384,0.0000010211467,0.000009067316,0.000033600718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879074,0.00006453336,0.00026932932,0.0003964669,0.00023194254,0.0002469975],"domain_scores_gemma":[0.99883264,0.00022377781,0.00011892445,0.00050663046,0.00023057235,0.00008744521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026983971,0.00016699673,0.00014990591,0.00009028644,0.000101862606,0.00005282497,0.0004760504,0.000046220077,0.0000069142507],"category_scores_gemma":[0.00019668472,0.0001280567,0.00003997153,0.00077650277,0.00001904761,0.00034114093,0.000059160866,0.00009400391,0.000030036224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008706649,0.00015095482,0.0000045435545,0.0000018743889,0.0000074456166,3.477161e-7,0.0005633835,0.9590613,0.0015514379,0.03037011,0.000011325271,0.008268563],"study_design_scores_gemma":[0.00036759544,0.00024516447,0.00035384425,0.000010931114,0.0000035121354,0.000001953848,0.000023252973,0.9757362,0.022524009,0.0005666216,0.000010900574,0.0001560018],"about_ca_topic_score_codex":0.000009432112,"about_ca_topic_score_gemma":0.0000014298116,"teacher_disagreement_score":0.34287208,"about_ca_system_score_codex":0.00011670521,"about_ca_system_score_gemma":0.000035959325,"threshold_uncertainty_score":0.5222},"labels":[],"label_agreement":null},{"id":"W2087951682","doi":"10.1109/wi-iat.2014.112","title":"Two-Phase Pareto Set Discovery for Team Formation in Social Networks","year":2014,"lang":"en","type":"article","venue":"2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Pareto principle; Computer science; Cover (algebra); Set (abstract data type); Population; Graph; Multi-objective optimization; Mathematical optimization; Operations research; Machine learning; Theoretical computer science; Mathematics; Engineering","score_opus":0.052602732303005405,"score_gpt":0.32979404857229716,"score_spread":0.2771913162692918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087951682","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":[0.0071246387,0.00020208064,0.98433673,0.0025517258,0.0021463002,0.0011873075,0.000101398444,0.00073490903,0.0016149207],"genre_scores_gemma":[0.96128243,0.0029849382,0.03402617,0.0005074508,0.00028429256,0.00044900764,0.00017264769,0.0000392656,0.00025378028],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99564624,0.00016962134,0.0013363406,0.0012860264,0.00074481306,0.0008169498],"domain_scores_gemma":[0.99693894,0.00067117065,0.00074786873,0.0008821943,0.000635162,0.0001246627],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008867924,0.00067865924,0.00067201874,0.0010217549,0.00038297763,0.0007094079,0.002716252,0.0003910684,0.000045340374],"category_scores_gemma":[0.0011464561,0.0006158701,0.00025803281,0.0005787317,0.00047600732,0.0018178257,0.00083348254,0.0006908475,0.00010337305],"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":[0.0002473801,0.00066581834,0.00061559316,0.00006488107,0.00014453064,0.000014701674,0.0010142758,0.036956694,0.0006176994,0.4052512,0.0043401583,0.55006707],"study_design_scores_gemma":[0.0009427961,0.00078606314,0.000099188925,0.00021015474,0.000019292542,0.000020874051,0.0025366873,0.9071362,0.03973951,0.03568991,0.01198125,0.000838092],"about_ca_topic_score_codex":0.000047591544,"about_ca_topic_score_gemma":0.00017976355,"teacher_disagreement_score":0.9541578,"about_ca_system_score_codex":0.0004789556,"about_ca_system_score_gemma":0.00013784034,"threshold_uncertainty_score":0.99962926},"labels":[],"label_agreement":null},{"id":"W2088744342","doi":"10.1007/s00158-006-0061-7","title":"An adaptive approach to constraint aggregation using adjoint sensitivity analysis","year":2006,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":201,"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":"Constraint (computer-aided design); Sensitivity (control systems); Mathematical optimization; Key (lock); Minification; Computer science; Function (biology); Mathematics; Engineering","score_opus":0.01564829907526832,"score_gpt":0.27056599761366396,"score_spread":0.25491769853839563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088744342","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":[0.08088415,0.000019903184,0.9180068,0.000056922778,0.00012388124,0.00046883748,0.000029979865,0.00018446139,0.00022508967],"genre_scores_gemma":[0.48857233,0.0000017731031,0.51123595,0.000015297608,0.000048246726,0.00000728764,0.00009827042,0.00000871578,0.000012151766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980897,0.0001840763,0.0003365646,0.0008248288,0.00026349266,0.00030136283],"domain_scores_gemma":[0.99886775,0.000044310116,0.0001964544,0.00037710753,0.00034051633,0.00017387555],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001978823,0.00027973432,0.0003032224,0.0004102505,0.00056622084,0.00017718003,0.00016516214,0.00009426112,0.0000046670616],"category_scores_gemma":[0.000022031229,0.0002588768,0.000079250996,0.0014012972,0.00011755971,0.0014559735,0.00019764427,0.00011293408,9.0811375e-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":[0.000025047586,0.000043780437,0.0005745515,0.0000054139987,0.000038872404,0.000004959535,0.0005627148,0.98939866,0.00094352773,0.0046409643,7.1255306e-7,0.0037608154],"study_design_scores_gemma":[0.00038887828,0.000090578484,0.016414056,0.00000722732,0.000105280764,0.000052787083,0.0002716566,0.9811743,0.00050252344,0.00063956936,5.0155927e-7,0.00035266991],"about_ca_topic_score_codex":0.00016447078,"about_ca_topic_score_gemma":0.000036631518,"teacher_disagreement_score":0.40768817,"about_ca_system_score_codex":0.00014457337,"about_ca_system_score_gemma":0.00004536806,"threshold_uncertainty_score":0.99998635},"labels":[],"label_agreement":null},{"id":"W2089960166","doi":"10.1007/s10999-011-9154-6","title":"Combinatorial optimization of weld sequence by using a surrogate model to mitigate a weld distortion","year":2011,"lang":"en","type":"article","venue":"International Journal of Mechanics and Materials in Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Surrogate model; Sequence (biology); Solid mechanics; Welding; Distortion (music); Set (abstract data type); Mathematical optimization; Algorithm; Girth (graph theory); Mathematics; Computer science; Engineering; Materials science; Mechanical engineering","score_opus":0.06197597549678736,"score_gpt":0.29234038666268847,"score_spread":0.23036441116590112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089960166","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":[0.013140357,0.000023031256,0.9849431,0.000064755826,0.0016302909,0.00015757236,0.00002070039,0.000009257701,0.000010968991],"genre_scores_gemma":[0.5134338,0.00006278106,0.48642275,0.000039533665,0.000025979483,0.00000306097,0.0000014050111,0.0000077725135,0.0000029161188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867505,0.00011268148,0.00057625835,0.00017231819,0.00034443292,0.000119248354],"domain_scores_gemma":[0.9986291,0.000040167335,0.00056103716,0.000104822284,0.0005913478,0.00007354722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007134015,0.0001145079,0.00021833986,0.00024271636,0.000027079644,0.00006069014,0.0004772394,0.00006164226,0.000012626512],"category_scores_gemma":[0.00014847628,0.00011317511,0.000029436487,0.00013344716,0.00001408358,0.0006621151,0.00012621107,0.00006551542,4.1121854e-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":[0.00027545608,0.0001448163,0.0000040830723,0.000008028959,0.000037289516,0.00002947289,0.0008879309,0.8081015,0.15608914,0.03321839,0.000018832194,0.001185091],"study_design_scores_gemma":[0.0007393174,0.00013857818,0.0000023588134,0.0000915306,0.0000066517196,0.000045057935,0.000016720905,0.79392934,0.17841326,0.026511522,0.000003569974,0.00010208023],"about_ca_topic_score_codex":0.000028601928,"about_ca_topic_score_gemma":7.225773e-7,"teacher_disagreement_score":0.50029343,"about_ca_system_score_codex":0.00013155324,"about_ca_system_score_gemma":0.000092398535,"threshold_uncertainty_score":0.4615147},"labels":[],"label_agreement":null},{"id":"W2090142805","doi":"10.1007/s11222-012-9357-1","title":"Estimating mixed-effects differential equation models","year":2012,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 Ottawa; Ottawa Hospital; McGill University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ode; Ordinary differential equation; Applied mathematics; Mathematics; Smoothing; Smoothing spline; Mathematical optimization; Spline (mechanical); Differential equation; Statistics; Spline interpolation; Mathematical analysis; Bilinear interpolation","score_opus":0.02088113994297148,"score_gpt":0.27275855103406865,"score_spread":0.2518774110910972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090142805","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":[0.0027119566,0.00007886311,0.9957641,0.000012635004,0.001003124,0.00013210515,0.0000060196094,0.00011255955,0.00017867576],"genre_scores_gemma":[0.46016836,0.0000015741879,0.53967863,0.000025995008,0.00010088621,0.0000020848606,0.000006323709,0.000006633115,0.000009473698],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990352,0.000061754625,0.00019007428,0.00022660596,0.00017919892,0.00030717897],"domain_scores_gemma":[0.9991573,0.00035301215,0.00013033372,0.00015322224,0.000093103255,0.00011302227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001509062,0.00012942178,0.00013432906,0.00005794099,0.0002583953,0.00013191004,0.00014734302,0.000031549756,0.0000018860515],"category_scores_gemma":[0.00011971329,0.00013068762,0.000014906665,0.00013280679,0.000029261306,0.0004458547,0.00023889622,0.00009797484,0.000005111126],"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":[0.0000011237299,0.000049047296,0.00022437108,0.00003765156,0.000012236615,0.000002574694,0.0012310903,0.1516244,0.00008547025,0.41188204,0.00004059085,0.4348094],"study_design_scores_gemma":[0.00024367154,0.000023252123,0.0016261712,0.000023008983,0.000006484926,0.000008003226,0.000014360571,0.9771222,0.00013028472,0.020644745,0.0000055577602,0.00015226223],"about_ca_topic_score_codex":0.000007243745,"about_ca_topic_score_gemma":3.546316e-7,"teacher_disagreement_score":0.8254978,"about_ca_system_score_codex":0.00003121117,"about_ca_system_score_gemma":0.000013536629,"threshold_uncertainty_score":0.53292865},"labels":[],"label_agreement":null},{"id":"W2092838665","doi":"10.1109/mwsym.2006.249380","title":"Space-Mapping-Based Modeling Utilizing Parameter Extraction with Variable Weight Coefficients and a Data Base","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Weighting; Robustness (evolution); Space mapping; Base (topology); Surrogate model; Computer science; Algorithm; Set (abstract data type); Data mining; Mathematical optimization; Mathematics","score_opus":0.03384643139179515,"score_gpt":0.27462707551552623,"score_spread":0.24078064412373107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092838665","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":[0.001290916,0.000042257194,0.99661213,0.00022374115,0.00010761075,0.00025393075,0.000007900809,0.00025891853,0.001202621],"genre_scores_gemma":[0.24807014,0.0000021742344,0.75157726,0.00012530136,0.000023613835,0.000009984238,0.000033230197,0.000014045098,0.00014424966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983718,0.000049324513,0.00021140596,0.0007918253,0.0002822995,0.0002933569],"domain_scores_gemma":[0.9986032,0.00015265163,0.000099257064,0.00089423754,0.00016891293,0.000081729995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025658772,0.00018364364,0.0001513893,0.00016515107,0.00020522464,0.0002259071,0.00044393959,0.000052901116,0.000017672808],"category_scores_gemma":[0.000061986095,0.00015402453,0.000013111235,0.00059109146,0.000042413627,0.0014024456,0.00023190444,0.00012723892,0.000005264046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000116000865,0.00012359595,0.00024476525,0.000009106726,0.0000075959224,0.00000966225,0.00002861925,0.9926788,0.0004403331,0.0041065556,0.000055039254,0.0022842875],"study_design_scores_gemma":[0.00085551105,0.000029298424,0.00004763806,0.000027864116,0.000007809388,0.000018075205,0.000029952665,0.99689496,0.00079693086,0.0003225874,0.00074423,0.00022515185],"about_ca_topic_score_codex":0.00015989738,"about_ca_topic_score_gemma":0.000022038876,"teacher_disagreement_score":0.24677922,"about_ca_system_score_codex":0.000057054316,"about_ca_system_score_gemma":0.00008856954,"threshold_uncertainty_score":0.6280938},"labels":[],"label_agreement":null},{"id":"W2093077824","doi":"10.1080/08982110802247744","title":"Evaluating Three DOE Methodologies: Optimization of a Composite Laminate under Fabrication Error","year":2008,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy; National Science Foundation","keywords":"Design of experiments; Response surface methodology; Computer science; Composite number; Bayesian probability; Reliability engineering; Engineering drawing; Engineering; Mathematics; Algorithm; Machine learning; Artificial intelligence; Statistics","score_opus":0.20182623593448912,"score_gpt":0.41174375457916007,"score_spread":0.20991751864467095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093077824","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":[0.009792821,0.000060665137,0.9891605,0.00010412553,0.0002137364,0.00022240986,0.0000028462662,0.00034402942,0.00009889337],"genre_scores_gemma":[0.13320431,0.000013708776,0.8666552,0.000029981295,0.000024085151,0.000022131813,0.000007989685,0.00001535193,0.000027284555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847364,0.0001752345,0.0004569185,0.00034730713,0.0003367699,0.00021012576],"domain_scores_gemma":[0.9982802,0.0005535499,0.00025814257,0.00046978644,0.00038687515,0.000051441453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090498186,0.00015864232,0.00025969718,0.00016089377,0.000100324534,0.000021675922,0.00039546864,0.000067550136,0.000007600528],"category_scores_gemma":[0.00087065325,0.00016744668,0.00006286746,0.000640471,0.00004351713,0.0005653509,0.00015394227,0.0001255701,0.0000033581541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003701603,0.000021806765,0.000105688654,0.000027197128,0.000016685673,8.691795e-7,0.00055097,0.98340964,0.00797886,0.00389281,0.0000013168676,0.003990428],"study_design_scores_gemma":[0.00033092854,0.000036978687,0.008292526,0.000022129223,0.0000055659475,0.000008518971,0.000035355646,0.9840591,0.006527861,0.00049102894,0.000004064318,0.0001859744],"about_ca_topic_score_codex":0.000021814682,"about_ca_topic_score_gemma":9.851623e-7,"teacher_disagreement_score":0.12341149,"about_ca_system_score_codex":0.00012053091,"about_ca_system_score_gemma":0.000042413176,"threshold_uncertainty_score":0.6828277},"labels":[],"label_agreement":null},{"id":"W2096649264","doi":"10.1007/s10514-009-9130-2","title":"A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot","year":2009,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":222,"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":"Computer science; Mobile robot; Reinforcement learning; Motion planning; Bayesian optimization; Partially observable Markov decision process; Artificial intelligence; Robot; Differentiable function; Bayesian probability; Robotics; Machine learning; Mathematical optimization; Markov chain","score_opus":0.03127060285093802,"score_gpt":0.306921433026712,"score_spread":0.27565083017577396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096649264","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":[0.0013606962,0.00007052509,0.9968054,0.000236535,0.0000639824,0.000960784,0.0000040361797,0.00035573408,0.00014226127],"genre_scores_gemma":[0.1486699,0.0000051383613,0.850703,0.00029820402,0.00006698646,0.000060387018,0.000072619194,0.000025016876,0.00009877173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984436,0.000044430042,0.00032328322,0.00065352255,0.00019191552,0.00034321536],"domain_scores_gemma":[0.9989457,0.000086107575,0.0002080827,0.00031899163,0.00031717255,0.00012395046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015312969,0.000252504,0.00026287275,0.00018559124,0.00027870448,0.00021584077,0.00021698108,0.00007415325,8.327045e-7],"category_scores_gemma":[0.000058747133,0.00024287746,0.000039099854,0.0003801139,0.000042664884,0.0014623464,0.00005498022,0.0001121749,9.748112e-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":[0.000021823786,0.00011570391,0.000011418133,0.0000075390567,0.000014917046,0.000007856088,0.002767296,0.94179964,0.00042800515,0.0003818332,0.000022143699,0.05442185],"study_design_scores_gemma":[0.0011187217,0.0005409755,0.0002862147,0.000028776712,0.000012681438,0.00006411085,0.00063100987,0.99600476,0.0006071895,0.00034464878,0.00003920106,0.00032169794],"about_ca_topic_score_codex":0.000007191429,"about_ca_topic_score_gemma":0.0000031225063,"teacher_disagreement_score":0.1473092,"about_ca_system_score_codex":0.00010542132,"about_ca_system_score_gemma":0.00011795876,"threshold_uncertainty_score":0.9904255},"labels":[],"label_agreement":null},{"id":"W2097746659","doi":"10.1007/s00158-005-0557-6","title":"Adaptive weighted sum method for multiobjective optimization: a new method for Pareto front generation","year":2005,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":488,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Multi-objective optimization; Mathematical optimization; Pareto principle; Piecewise; Mathematics; Pareto analysis; Simple (philosophy); Computer science","score_opus":0.025438543972311874,"score_gpt":0.33198768003632445,"score_spread":0.3065491360640126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097746659","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":[0.000046933983,0.0002834783,0.993817,0.0015880433,0.0007249114,0.0030338764,0.00011877063,0.00031780044,0.000069202964],"genre_scores_gemma":[0.0015358864,0.00007371042,0.99530214,0.0001832559,0.00090706814,0.0004869443,0.00061151263,0.00006776803,0.00083171536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99699235,0.0002054399,0.0006605454,0.0013052804,0.00029356254,0.0005428419],"domain_scores_gemma":[0.9974624,0.00040491731,0.00045757976,0.0004334181,0.0009525468,0.00028915855],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037107567,0.0005336479,0.0005070178,0.000285385,0.0010836244,0.00023302926,0.0003971247,0.0002490765,0.00003403179],"category_scores_gemma":[0.00016799185,0.00049024355,0.00017613074,0.00044022314,0.00006939617,0.0022955476,0.00025009087,0.00016417493,0.0000018705971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018737059,0.000035086137,0.000009897087,0.000019727866,0.000064425876,6.2064044e-7,0.0023799767,0.9145596,0.00024180952,0.0033105644,0.00017802413,0.07901291],"study_design_scores_gemma":[0.003913345,0.00037770008,0.00008605814,0.00002083986,0.00010118303,0.000032707845,0.00026503197,0.99028784,0.0021287529,0.0019144089,0.00021816093,0.0006539901],"about_ca_topic_score_codex":0.000038895367,"about_ca_topic_score_gemma":0.000070625865,"teacher_disagreement_score":0.07835892,"about_ca_system_score_codex":0.0003041717,"about_ca_system_score_gemma":0.00019440116,"threshold_uncertainty_score":0.9997549},"labels":[],"label_agreement":null},{"id":"W2097998348","doi":"","title":"Random search for hyper-parameter optimization","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":7937,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Hyperparameter optimization; Random search; Computer science; Grid; Set (abstract data type); Artificial neural network; Fraction (chemistry); Search algorithm; Data mining; Artificial intelligence; Machine learning; Algorithm; Mathematics","score_opus":0.030044046383621726,"score_gpt":0.29760255476646075,"score_spread":0.267558508382839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097998348","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":[0.00010444512,0.000051749943,0.9956669,0.00026003172,0.0003932623,0.00050875486,0.0000018788969,0.00020736542,0.002805608],"genre_scores_gemma":[0.024003072,0.000010605296,0.9739851,0.00036751418,0.00010467673,0.000081822785,0.000007782495,0.0000148583185,0.0014245902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902576,0.0000493094,0.00015682288,0.0002387661,0.0001725776,0.00035678083],"domain_scores_gemma":[0.9989792,0.00033245704,0.000039551498,0.0003119384,0.00021268814,0.00012417353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030571767,0.00010824223,0.00012564688,0.00009889371,0.00012092078,0.00007939302,0.00031061564,0.000048699036,0.000072243376],"category_scores_gemma":[0.00012610087,0.00009305241,0.000058976748,0.000290362,0.000027469148,0.0012613065,0.00010800382,0.000057479643,0.000047248006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029006495,0.00012331594,0.00038522197,0.0000068061395,0.000015138741,2.0624654e-7,0.00045584142,0.94894975,0.00012959332,0.014639986,0.00031099262,0.034954157],"study_design_scores_gemma":[0.0014998325,0.000030087256,0.0000699839,0.000002033191,0.0000032368323,0.000006310906,0.000021232821,0.99112034,0.0054851463,0.000099811965,0.0015208579,0.00014110738],"about_ca_topic_score_codex":0.0000030599722,"about_ca_topic_score_gemma":2.409461e-7,"teacher_disagreement_score":0.042170625,"about_ca_system_score_codex":0.00004999562,"about_ca_system_score_gemma":0.000025668305,"threshold_uncertainty_score":0.37945673},"labels":[],"label_agreement":null},{"id":"W2098678467","doi":"10.1109/tmtt.2006.884648","title":"Theoretical Justification of Space-Mapping-Based Modeling Utilizing a Database and On-Demand Parameter Extraction","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Weighting; Space mapping; Benchmark (surveying); Computer science; Set (abstract data type); Data mining; Point (geometry); Base (topology); Parameter space; Space (punctuation); Data modeling; Surrogate model; Algorithm; Mathematical optimization; Database; Mathematics; Machine learning; Statistics","score_opus":0.017395911040698712,"score_gpt":0.2718570943285353,"score_spread":0.25446118328783657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098678467","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":[0.014599126,0.00006120395,0.9841904,0.00011867483,0.000057534417,0.00033050726,0.000018688095,0.00024230176,0.0003815454],"genre_scores_gemma":[0.697274,0.000045909503,0.30254436,0.000057974703,0.000009817967,0.000034496,0.000003458573,0.000012167032,0.000017814913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99877816,0.0002155814,0.00027159994,0.00043842,0.00013224642,0.00016397375],"domain_scores_gemma":[0.9988476,0.0005296558,0.00010776359,0.00035883972,0.000105335916,0.00005079427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056266534,0.00018809756,0.00017769428,0.0003021154,0.00022735259,0.00005701819,0.00012837508,0.00010083021,0.000010994268],"category_scores_gemma":[0.000022720857,0.00017846734,0.00005083555,0.0002279676,0.00029983025,0.00036388164,0.000004045087,0.0002314817,9.676556e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022441417,0.0003539301,0.0000019219951,0.00005330159,0.000019023042,0.0000043869436,0.00020520666,0.028290499,0.44652334,0.4765452,0.000004668181,0.047774076],"study_design_scores_gemma":[0.00020754908,0.0000901169,0.0000020325256,0.00007412121,0.000016707798,0.000014100382,0.000045138484,0.30106002,0.6456805,0.052655786,0.000013546896,0.0001403881],"about_ca_topic_score_codex":0.000006565584,"about_ca_topic_score_gemma":0.0000019187078,"teacher_disagreement_score":0.6826749,"about_ca_system_score_codex":0.000036485108,"about_ca_system_score_gemma":0.000023054206,"threshold_uncertainty_score":0.72776866},"labels":[],"label_agreement":null},{"id":"W2100358079","doi":"10.1109/tmag.2009.2022492","title":"Improved Sequential Optimization Method for High Dimensional Electromagnetic Device Optimization","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Benchmark (surveying); Dimension (graph theory); Optimization problem; Mathematical optimization; Reduction (mathematics); Engineering optimization; Algorithm; Mathematics","score_opus":0.012824749163893524,"score_gpt":0.2773621062419217,"score_spread":0.26453735707802817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100358079","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":[0.00003287298,0.000034171266,0.99659836,0.0009912002,0.00081016676,0.0009970615,0.00003275638,0.000407385,0.000096029275],"genre_scores_gemma":[0.025333373,0.0000510873,0.9728056,0.0009459713,0.00007901954,0.00011370548,0.000035083074,0.000041004416,0.0005951586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99777824,0.00014389835,0.00047912405,0.00076040503,0.00035906382,0.00047928948],"domain_scores_gemma":[0.9982832,0.00021805674,0.00019446065,0.0005556209,0.0005825959,0.00016605135],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021637524,0.00035078506,0.0002807383,0.00032703613,0.00040904444,0.0001700876,0.00046386063,0.00018797629,0.00014042847],"category_scores_gemma":[0.000029896788,0.0003853551,0.00013428263,0.0008401825,0.000044094355,0.00060218084,0.0000040803466,0.00025530267,0.0000081579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005604125,0.00025417487,5.209749e-8,0.0000059262534,0.000016698124,0.0000017179281,0.000058859958,0.9306285,0.005656048,0.0005855516,0.000028226677,0.06270822],"study_design_scores_gemma":[0.001679533,0.0016035653,0.000007951992,0.0000115295625,0.000064122934,0.000023659817,0.00000615927,0.9609707,0.03464973,0.0005452442,0.000038432798,0.0003993507],"about_ca_topic_score_codex":0.000010328468,"about_ca_topic_score_gemma":0.0000052610635,"teacher_disagreement_score":0.062308867,"about_ca_system_score_codex":0.00017878106,"about_ca_system_score_gemma":0.00013991666,"threshold_uncertainty_score":0.9998598},"labels":[],"label_agreement":null},{"id":"W2101083806","doi":"10.1287/ijoc.1110.0476","title":"A Generic Branch-and-Cut Algorithm for Multiobjective Optimization Problems: Application to the Multilabel Traveling Salesman Problem","year":2011,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche","keywords":"Travelling salesman problem; Branch and cut; 2-opt; Bottleneck traveling salesman problem; Branch and bound; Mathematical optimization; Mathematics; Maximum cut; Algorithm; Traveling purchaser problem; Constraint (computer-aided design); Hamiltonian path; Computer science; Integer programming; Combinatorics; Graph","score_opus":0.025452194892687716,"score_gpt":0.26221907165372105,"score_spread":0.23676687676103333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101083806","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":[0.0003816835,0.000042180218,0.99680275,0.00021298426,0.0003380296,0.0016808123,0.000004748383,0.00017164015,0.0003651967],"genre_scores_gemma":[0.060492404,0.000028008135,0.9382533,0.00078645756,0.00025185442,0.00011029608,0.000005418818,0.00003566276,0.000036585803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979615,0.000055251232,0.0006918731,0.0004640875,0.00034891057,0.0004783631],"domain_scores_gemma":[0.997919,0.0001924039,0.00061526353,0.00033294014,0.0007318134,0.00020854163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008616303,0.00029643922,0.0002588412,0.00025170762,0.00094903726,0.00031408836,0.0007150038,0.00009159956,0.00000197072],"category_scores_gemma":[0.000118086995,0.00021626198,0.00009838046,0.0006741162,0.00004949949,0.0009702294,0.00021068176,0.0003759138,0.000012960638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009338959,0.00004108917,0.000016590835,0.0000067061637,0.000021054,7.515159e-7,0.004278943,0.553655,0.000026377798,0.00052862254,0.0000076413635,0.4414079],"study_design_scores_gemma":[0.0011432301,0.00028959734,0.00038540593,0.00008480637,0.000011305166,0.00015412457,0.00016299321,0.99560183,0.0006167229,0.00078329706,0.00046440467,0.00030230742],"about_ca_topic_score_codex":0.000013395229,"about_ca_topic_score_gemma":0.0000057175744,"teacher_disagreement_score":0.4419468,"about_ca_system_score_codex":0.00020352729,"about_ca_system_score_gemma":0.00009582853,"threshold_uncertainty_score":0.8818908},"labels":[],"label_agreement":null},{"id":"W2102314356","doi":"10.1115/omae2014-23489","title":"Response Surface Models for Analyzing Planing Hull Motions in a Vertical Plane","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Memorial University of Newfoundland","funders":"","keywords":"Hull; Marine engineering; Trim; Head (geology); Center of gravity; Vertical plane; Engineering; Structural engineering; Geology","score_opus":0.024210763723180963,"score_gpt":0.27856523520608484,"score_spread":0.25435447148290385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102314356","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":[0.013082124,0.000008501577,0.98535925,0.0006273372,0.00008539581,0.00020075546,0.0000032272674,0.00016169083,0.00047170656],"genre_scores_gemma":[0.4139581,0.0000012866036,0.5856919,0.00013451095,0.000009253394,0.000010396832,0.0000029320995,0.000007763584,0.00018382461],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988652,0.00014128695,0.00021691181,0.00036291976,0.00013112619,0.000282562],"domain_scores_gemma":[0.9987989,0.0006916306,0.000029115721,0.00030630745,0.00008791881,0.00008613961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000595957,0.00011022377,0.00015121794,0.0001347705,0.000099904784,0.00006707727,0.00031863587,0.000052418396,0.0000061183123],"category_scores_gemma":[0.00039692337,0.00010785646,0.000035495545,0.00037514252,0.000023976667,0.0005672095,0.000103352955,0.000095886026,0.000011965548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050399132,0.000041771673,0.00013617383,0.0000023099958,0.00000418101,0.000001949786,0.00031377733,0.95859337,0.0010418821,0.03879104,0.00003648607,0.0009866337],"study_design_scores_gemma":[0.0007255832,0.00006724068,0.0006448062,0.000011786872,0.000001865439,0.0000051396937,0.000025097052,0.99286634,0.0011154546,0.0042749075,0.00012252633,0.00013926093],"about_ca_topic_score_codex":0.000015872949,"about_ca_topic_score_gemma":0.000025415353,"teacher_disagreement_score":0.400876,"about_ca_system_score_codex":0.00008023691,"about_ca_system_score_gemma":0.000040452993,"threshold_uncertainty_score":0.4398259},"labels":[],"label_agreement":null},{"id":"W2105084210","doi":"10.1109/icsmc.1998.726710","title":"An extension to design of experiment for design optimization with implicit parametric models and virtual prototypes","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Virtual prototyping; Parametric statistics; Computation; Extension (predicate logic); Parametric design; Mathematical optimization; Nonlinear system; Simulation; Algorithm; Mathematics","score_opus":0.062214232993946156,"score_gpt":0.29235219520489003,"score_spread":0.23013796221094387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105084210","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":[0.00019768157,0.000040220835,0.9964575,0.000045316818,0.000021631815,0.0031001235,0.0000011790843,0.00010984727,0.000026508867],"genre_scores_gemma":[0.11361714,0.000016495547,0.8857027,0.000098128,0.0000075711346,0.00048713273,9.747466e-7,0.000017525577,0.000052332976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988355,0.000075035525,0.00020630502,0.00048969674,0.00019874281,0.00019473229],"domain_scores_gemma":[0.9989065,0.00015888522,0.00009333737,0.00036120685,0.0003536309,0.00012642365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018044765,0.00015424186,0.00018089202,0.0002444119,0.00009364326,0.0000656579,0.0002301272,0.00004476024,0.000009366607],"category_scores_gemma":[0.00004763062,0.00012296299,0.00001505254,0.00062454457,0.000029713747,0.00089180487,0.000050483213,0.00003124405,0.0000010992194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006729664,0.00013964584,0.0000017793262,0.00000293279,0.0000067864144,5.5816616e-7,0.00050708343,0.982282,0.0022803058,0.002330684,0.00001792382,0.012363018],"study_design_scores_gemma":[0.00067875173,0.0023442023,0.000009837486,0.000010782222,0.000004570213,0.0000066538537,0.00005982685,0.9702805,0.026109762,0.00031623675,0.000002115641,0.00017675018],"about_ca_topic_score_codex":0.000004660472,"about_ca_topic_score_gemma":1.9306704e-7,"teacher_disagreement_score":0.11341945,"about_ca_system_score_codex":0.000040926003,"about_ca_system_score_gemma":0.000020658726,"threshold_uncertainty_score":0.5014285},"labels":[],"label_agreement":null},{"id":"W2105756282","doi":"10.1115/detc2013-12664","title":"Development of a Common Platform for Testing Metamodel Based Design Optimization Methods","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Simon Fraser University","funders":"","keywords":"Testbed; Computer science; Metamodeling; Benchmark (surveying); Pareto principle; Set (abstract data type); Optimization problem; Test functions for optimization; Engineering optimization; Mathematical optimization; Algorithm; Multi-swarm optimization; Software engineering","score_opus":0.12831463682150115,"score_gpt":0.35454135550807325,"score_spread":0.2262267186865721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105756282","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":[0.00002116439,0.000009048996,0.99816287,0.000046653528,0.00006671074,0.00097458635,6.4173275e-7,0.00018204437,0.00053627684],"genre_scores_gemma":[0.0013976435,3.4447018e-7,0.9980265,0.00014273146,0.0000063845205,0.00031071188,0.000005596727,0.000016895843,0.00009319211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880254,0.00006459635,0.00043413928,0.00030869996,0.00016689464,0.00022311792],"domain_scores_gemma":[0.99781936,0.00093951164,0.00023209887,0.0003005582,0.00063441513,0.000074030155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006705446,0.00015096663,0.00022449746,0.00015649716,0.00014678715,0.00006555497,0.00041399046,0.000052512678,0.000026552601],"category_scores_gemma":[0.00034976387,0.00013216643,0.00004027475,0.0005164498,0.000024329865,0.00088481494,0.00010505961,0.000049660284,0.0000041157664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002481113,0.000052761814,0.0000041467124,0.000009472021,0.000010267012,5.8325565e-8,0.00016855642,0.83529353,0.001495505,0.00096455007,0.000011861506,0.1619868],"study_design_scores_gemma":[0.0004948064,0.000041551113,0.000030488533,0.00001118307,0.000004779121,9.335564e-7,0.000027584429,0.9261996,0.07186938,0.0011245079,0.00003757132,0.00015759599],"about_ca_topic_score_codex":0.000009374819,"about_ca_topic_score_gemma":0.0000010337967,"teacher_disagreement_score":0.1618292,"about_ca_system_score_codex":0.00008320496,"about_ca_system_score_gemma":0.00019534994,"threshold_uncertainty_score":0.5389591},"labels":[],"label_agreement":null},{"id":"W2106831600","doi":"10.1109/tmag.2006.871573","title":"Multiobjective approaches for robust electromagnetic design","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Compromise; Multi-objective optimization; Mathematical optimization; Optimal design; Robustness (evolution); Decision maker; Operations research; Mathematics; Machine learning","score_opus":0.04258719587666003,"score_gpt":0.23518942404153992,"score_spread":0.19260222816487987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106831600","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":[0.0000610391,0.00007348333,0.997031,0.00021809981,0.00042265098,0.0011505154,0.000020896883,0.0003780204,0.00064429163],"genre_scores_gemma":[0.08752286,0.000020877947,0.90953,0.00009710528,0.00006576985,0.0004098768,0.000003951951,0.00004324197,0.0023063372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821335,0.00010597816,0.0003101971,0.000641234,0.00026449835,0.0004647326],"domain_scores_gemma":[0.99870926,0.00037444188,0.0001039109,0.0005052578,0.00021884171,0.000088269655],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015459536,0.00029059453,0.00021543883,0.00025604217,0.00033765315,0.00012723316,0.0004577821,0.00012560037,0.00002516496],"category_scores_gemma":[0.000012533295,0.00030982212,0.00013292013,0.0006118429,0.00009947372,0.0003360101,0.0000027521662,0.00022410604,0.00003188112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035532343,0.0003658892,5.583251e-7,0.000007628034,0.000013463564,0.0000023363293,0.00011299345,0.92817044,0.0009850176,0.0010823318,0.000107483764,0.069116324],"study_design_scores_gemma":[0.0012443392,0.00094042154,0.000088078035,0.000007116556,0.000030963878,0.000017915281,0.000026900241,0.9528549,0.041798014,0.0024422493,0.00018928054,0.00035982102],"about_ca_topic_score_codex":0.000017058273,"about_ca_topic_score_gemma":0.000020074267,"teacher_disagreement_score":0.08750103,"about_ca_system_score_codex":0.00015312595,"about_ca_system_score_gemma":0.00008997575,"threshold_uncertainty_score":0.9999354},"labels":[],"label_agreement":null},{"id":"W2107571131","doi":"10.1016/j.applthermaleng.2009.12.010","title":"A grid based multi-objective evolutionary algorithm for the optimization of power plants","year":2010,"lang":"en","type":"article","venue":"Applied Thermal Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Header; Mathematical optimization; Evolutionary algorithm; Process (computing); Set (abstract data type); Genetic algorithm; Computer science; Cogeneration; Power station; Population; Grid; Task (project management); Power (physics); Engineering; Electricity generation; Mathematics; Systems engineering","score_opus":0.006514385156439571,"score_gpt":0.2141665179242579,"score_spread":0.20765213276781833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107571131","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":[0.00022860283,0.000037155594,0.99794227,0.000035997786,0.0007326693,0.00066788046,0.0000370909,0.00019973106,0.000118607524],"genre_scores_gemma":[0.13945855,0.000002567336,0.8601302,0.000049931696,0.00008585124,0.00021516859,0.00001358988,0.000031397554,0.000012733782],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989917,0.000009180128,0.00022511419,0.00031336275,0.00019537329,0.0002652183],"domain_scores_gemma":[0.99890345,0.00035975897,0.00012068503,0.00040883129,0.00014796303,0.000059332997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001711332,0.00019195853,0.0001659695,0.00011651227,0.00012654281,0.00002887628,0.0005209651,0.000089186826,0.00001550264],"category_scores_gemma":[0.000054633652,0.00016042408,0.000071405186,0.00027590003,0.00004864988,0.00023488323,0.00009605693,0.00021950337,0.0000035045532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008591806,0.000053182983,0.0000030644628,0.000005065202,0.000027608676,6.704001e-7,0.00016399211,0.9716847,0.012740981,0.002113545,0.000005365631,0.013193214],"study_design_scores_gemma":[0.00092029094,0.00002399929,0.000838497,0.0000067962546,0.0000065794125,0.000004294817,0.000023906454,0.98336715,0.014422512,0.000016559105,0.00018367625,0.00018574043],"about_ca_topic_score_codex":0.0000048827073,"about_ca_topic_score_gemma":5.926474e-7,"teacher_disagreement_score":0.13922995,"about_ca_system_score_codex":0.00004068924,"about_ca_system_score_gemma":0.000058269,"threshold_uncertainty_score":0.6541904},"labels":[],"label_agreement":null},{"id":"W2109424752","doi":"10.1115/imece2013-64792","title":"Decomposition of High-Dimensional Shape Optimization Problems Through Quantifying Design Variable Correlation","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Airfoil; Aerodynamics; Turbomachinery; Mathematical optimization; Computer science; Multidisciplinary design optimization; Metamodeling; Engineering design process; Shape optimization; Variable (mathematics); Decomposition; Optimization problem; Scale (ratio); Gas compressor; Mathematics; Engineering; Aerospace engineering; Mechanical engineering; Finite element method; Physics","score_opus":0.03358741631807934,"score_gpt":0.273719910749234,"score_spread":0.24013249443115467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109424752","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":[0.00028839562,0.000031026157,0.997571,0.00016640784,0.0003209308,0.0007596624,0.0000013967594,0.00023529767,0.00062588934],"genre_scores_gemma":[0.11215176,0.000010242926,0.8874375,0.0001460793,0.000018860568,0.00006216489,0.00003472301,0.00001633166,0.00012232306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984558,0.00012796637,0.00043203554,0.0004258624,0.00033027434,0.00022803449],"domain_scores_gemma":[0.9984309,0.0002150517,0.00029315864,0.00032134255,0.00068235176,0.000057172256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020870191,0.00017010288,0.00019929913,0.00012424841,0.00016917287,0.00010090049,0.00029193988,0.00009648775,0.00063084293],"category_scores_gemma":[0.000066175686,0.00016080776,0.000035790647,0.0007005236,0.000043043954,0.0029842723,0.0001253156,0.00009925794,0.000072528266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042036118,0.000090602065,0.00003101654,0.000008943032,0.000012458465,3.1358815e-7,0.0001217561,0.96495265,0.002149671,0.029483154,0.000111857626,0.0030333756],"study_design_scores_gemma":[0.00054144004,0.00008741179,0.00025830575,0.000038390524,0.0000066070074,0.000010605342,0.000010329927,0.9865539,0.0040886113,0.008210203,0.0000073959327,0.00018677379],"about_ca_topic_score_codex":0.00017756589,"about_ca_topic_score_gemma":9.3103483e-7,"teacher_disagreement_score":0.11186336,"about_ca_system_score_codex":0.000085153435,"about_ca_system_score_gemma":0.00007284238,"threshold_uncertainty_score":0.6907288},"labels":[],"label_agreement":null},{"id":"W2111188993","doi":"10.1109/tmtt.2006.890524","title":"Space-Mapping Optimization With Adaptive Surrogate Model","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Space mapping; Extrapolation; Surrogate model; Mathematical optimization; Space (punctuation); Computer science; Point (geometry); Algorithm; Optimization problem; Mathematics; Statistics","score_opus":0.013081143403960575,"score_gpt":0.24249825300624736,"score_spread":0.22941710960228678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111188993","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":[0.0005991961,0.000026832964,0.996173,0.00006022346,0.000053036274,0.000392843,0.000007175717,0.00069139607,0.0019962573],"genre_scores_gemma":[0.36199272,0.000060960756,0.6374923,0.0001273971,0.000008630013,0.000023026447,8.969153e-7,0.000019005975,0.0002750602],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988208,0.00010332069,0.00020456847,0.00044838927,0.00014739834,0.00027554831],"domain_scores_gemma":[0.9990799,0.00020779592,0.000106747706,0.00032156552,0.0001865715,0.00009742801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068032433,0.00023123132,0.0001749595,0.00033422912,0.00030391768,0.00006442919,0.00021353082,0.000103181716,0.000007260547],"category_scores_gemma":[0.0000053192266,0.00020879794,0.00004684059,0.00046710472,0.00016723198,0.0006717391,0.000005058101,0.00025547054,0.0000026029609],"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":[0.00038958614,0.00020296469,0.0000011720145,0.000012158998,0.00006427621,0.000022668424,0.0015764867,0.7689534,0.051629014,0.056401536,0.0000063326247,0.12074041],"study_design_scores_gemma":[0.00033253233,0.00019500234,9.899632e-7,0.0000581634,0.000013154724,0.000050963215,0.00015194684,0.24327078,0.7467603,0.008854457,0.00002733164,0.00028434544],"about_ca_topic_score_codex":0.0000033873848,"about_ca_topic_score_gemma":0.0000083579125,"teacher_disagreement_score":0.6951313,"about_ca_system_score_codex":0.0000868488,"about_ca_system_score_gemma":0.00004192031,"threshold_uncertainty_score":0.85145324},"labels":[],"label_agreement":null},{"id":"W2111515077","doi":"10.1007/s00158-009-0366-4","title":"Pareto frontier for simultaneous structural and manufacturing optimization of a composite part","year":2009,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematical optimization; Sorting; Pareto principle; Multi-objective optimization; Engineering design process; Constraint (computer-aided design); Vector optimization; Convergence (economics); Computer science; Optimization problem; Mathematics; Multi-swarm optimization; Algorithm; Engineering","score_opus":0.009233764074291486,"score_gpt":0.26013590647274615,"score_spread":0.25090214239845465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111515077","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":[0.04691798,0.0002495166,0.95116895,0.00029676664,0.0003483445,0.0007871515,0.00004916708,0.0001376238,0.000044480843],"genre_scores_gemma":[0.40535071,0.000085070395,0.594285,0.000034452525,0.000056801673,0.000011144201,0.00010864017,0.000013788311,0.00005435813],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829406,0.000054271695,0.00046788313,0.0006458989,0.00021042673,0.00032746285],"domain_scores_gemma":[0.9988665,0.00013764076,0.00031674097,0.00028704316,0.00024563834,0.00014645196],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000086978,0.00032855172,0.00036446916,0.00016517461,0.00051738333,0.00012330385,0.00024412875,0.00012393727,0.000008080159],"category_scores_gemma":[0.000057846944,0.00029132704,0.00006288149,0.0001831013,0.00012221691,0.0011302055,0.0001754718,0.000111060486,1.7352816e-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":[0.00009489825,0.000011639573,0.00016857829,0.00003959397,0.00001828896,0.000003075151,0.0007007509,0.97717196,0.00022022973,0.00057889527,0.0000043862124,0.020987732],"study_design_scores_gemma":[0.0014224859,0.00026244146,0.0027866184,0.000037369377,0.000037803435,0.000051054656,0.000103002996,0.99145067,0.0017298376,0.0017476871,0.000010642228,0.0003604009],"about_ca_topic_score_codex":0.0000053022095,"about_ca_topic_score_gemma":0.0000022060326,"teacher_disagreement_score":0.35843274,"about_ca_system_score_codex":0.000052019583,"about_ca_system_score_gemma":0.000024443783,"threshold_uncertainty_score":0.99995387},"labels":[],"label_agreement":null},{"id":"W2113159894","doi":"10.1002/cjce.22384","title":"Adaptive Sampling for Surrogate Modelling with Artificial Neural Network and its Application in an Industrial Cracking Furnace","year":2015,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Shanghai Municipal Education Commission; National Natural Science Foundation of China; Science and Technology Commission of Shanghai Municipality; Beijing Municipal Science and Technology Commission; Shanghai Education Development Foundation; Natural Science Foundation of Shanghai","keywords":"Surrogate model; Adaptive sampling; Computer science; Artificial neural network; Surrogate data; Sampling (signal processing); Artificial intelligence; Process (computing); Selection (genetic algorithm); Machine learning; Data mining; Nonlinear system; Statistics; Mathematics","score_opus":0.07640862325215528,"score_gpt":0.2548289818497013,"score_spread":0.17842035859754604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113159894","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":[0.14425966,0.00010295052,0.8551815,0.00018658467,0.000111798065,0.00014493531,0.0000011609454,0.0000095983105,0.0000018169866],"genre_scores_gemma":[0.8453298,4.1108132e-7,0.15432125,0.000020281994,0.00030890622,0.0000058871638,7.9329436e-7,0.000012094916,5.196435e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992601,0.000017267119,0.00022238024,0.00012829965,0.00011973974,0.00025221487],"domain_scores_gemma":[0.99910647,0.00010894971,0.00012444427,0.000090016656,0.00024094155,0.00032918883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004201222,0.00009800191,0.00014144092,0.00009173179,0.00006027146,0.000087006854,0.00026335276,0.000054639007,1.3748776e-7],"category_scores_gemma":[0.000082193015,0.00008100278,0.000018298744,0.00027802848,0.000020588725,0.00048180672,0.0000169562,0.00029874788,9.90409e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025422187,0.0000024139194,0.000033996675,0.0000013664041,0.0000051035945,0.0000038694147,0.000399724,0.9947987,0.00039900112,0.0018055376,7.440081e-7,0.0025241151],"study_design_scores_gemma":[0.00041909018,0.000049511513,0.000006034469,0.000036583875,0.0000052366177,0.00004405192,0.000029338884,0.9971717,0.0014018525,0.0007096154,0.000029049475,0.00009793797],"about_ca_topic_score_codex":0.00011347858,"about_ca_topic_score_gemma":0.00021175876,"teacher_disagreement_score":0.7010702,"about_ca_system_score_codex":0.00019524715,"about_ca_system_score_gemma":0.00025686438,"threshold_uncertainty_score":0.33031973},"labels":[],"label_agreement":null},{"id":"W2113955139","doi":"10.1145/1569901.1569940","title":"An experimental investigation of model-based parameter optimisation","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Gaussian process; Process (computing); Key (lock); Model parameter; Gaussian network model; Machine learning; Gaussian; Artificial intelligence; Mathematical optimization; Algorithm; Mathematics","score_opus":0.028739908156011285,"score_gpt":0.3002304423346162,"score_spread":0.2714905341786049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113955139","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":[0.034825854,0.000007775265,0.9642434,0.00016108908,0.000037080372,0.00014657615,6.39186e-7,0.00014555074,0.0004320243],"genre_scores_gemma":[0.49478137,2.4107104e-7,0.5048022,0.0003833325,0.0000046423947,0.000004276732,0.0000047294525,0.0000025132201,0.000016632292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923307,0.000039646227,0.00018106695,0.00025543862,0.00018372294,0.00010705765],"domain_scores_gemma":[0.99934375,0.000025889507,0.00009292911,0.00034354258,0.000117680895,0.000076201824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008268357,0.00009182771,0.00009051424,0.00009806873,0.000042978707,0.000037389822,0.00025883302,0.000039430637,0.000011290656],"category_scores_gemma":[0.000018244464,0.00008874831,0.000028255172,0.0002217545,0.00003796167,0.0010109838,0.000014061698,0.000039204922,0.0000030827314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044162302,0.00007909071,0.000041477615,7.5842166e-7,0.0000010551298,2.8597324e-7,0.00039356135,0.93486255,0.05164261,0.0074643036,0.000009496406,0.0055004125],"study_design_scores_gemma":[0.00023457935,0.00012512552,0.000257578,0.0000022275938,7.5682556e-7,4.4610647e-7,0.000013734724,0.6600654,0.3371249,0.002103918,7.082907e-7,0.000070575654],"about_ca_topic_score_codex":0.000002629748,"about_ca_topic_score_gemma":2.566231e-7,"teacher_disagreement_score":0.45995554,"about_ca_system_score_codex":0.000050793547,"about_ca_system_score_gemma":0.000052484935,"threshold_uncertainty_score":0.36190513},"labels":[],"label_agreement":null},{"id":"W2114452512","doi":"10.1002/cjs.11156","title":"Sequential design for computer experiments with a flexible Bayesian additive model","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Bayesian probability; Focus (optics); Feature (linguistics); Representation (politics); Surrogate model; Artificial intelligence; Bayesian inference; Machine learning; Computer experiment; Simulation","score_opus":0.04889409630846797,"score_gpt":0.28230011981881054,"score_spread":0.23340602351034256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114452512","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":[0.000016249947,0.00006088047,0.99887013,0.000050422273,0.0004389849,0.0002210529,0.00024719213,0.000010863742,0.000084245235],"genre_scores_gemma":[0.034923054,0.0000035409648,0.9644827,0.00024088046,0.00019070834,0.0000100891775,0.0000090469475,0.000021307296,0.000118640455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895793,0.00004691996,0.00025604962,0.00013189911,0.00018275813,0.0004244279],"domain_scores_gemma":[0.99811935,0.00012257665,0.00025184706,0.00014496646,0.0006205625,0.0007406974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018493047,0.00014014536,0.00018261261,0.00020624125,0.00016883294,0.000104093444,0.00033105386,0.00003988469,0.000017797169],"category_scores_gemma":[0.000051922707,0.00012760465,0.000031734595,0.00015658354,0.00007890928,0.0005847619,0.000016312264,0.00011548127,0.0000028253633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057398338,0.000073627496,0.00018457869,0.000012616523,0.0001423655,0.000106444175,0.0049747066,0.8991029,0.000047865542,0.050213885,0.010397288,0.03468629],"study_design_scores_gemma":[0.00084815314,0.0002908828,0.00009339311,0.00002769361,0.000018525041,0.00013625772,0.00005725012,0.99445933,0.0010166563,0.0019255189,0.0009364116,0.00018993973],"about_ca_topic_score_codex":0.000060943967,"about_ca_topic_score_gemma":0.00016036654,"teacher_disagreement_score":0.09535638,"about_ca_system_score_codex":0.00028563174,"about_ca_system_score_gemma":0.0013078031,"threshold_uncertainty_score":0.52035666},"labels":[],"label_agreement":null},{"id":"W2115173649","doi":"10.1504/ijpmb.2014.065518","title":"Municipal waste management optimisation using a firefly algorithm-driven simulation-optimisation approach","year":2014,"lang":"en","type":"article","venue":"International Journal of Process Management and Benchmarking","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Firefly algorithm; Computer science; Firefly protocol; Mathematical optimization; Variety (cybernetics); Stochastic simulation; Municipal solid waste; Stochastic modelling; Algorithm; Engineering; Mathematics; Artificial intelligence; Waste management","score_opus":0.02096694470863541,"score_gpt":0.300349919083832,"score_spread":0.2793829743751966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115173649","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":[0.005401438,0.00005313962,0.99085385,0.00009308193,0.0007088338,0.00024223537,0.0000011509593,0.000038079463,0.0026081959],"genre_scores_gemma":[0.40053874,0.000077806726,0.59891963,0.0000848719,0.00027712874,0.0000069676344,0.00000864409,0.000011543671,0.00007463051],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785024,0.00007871272,0.00059806835,0.0003629907,0.00089474925,0.00021522911],"domain_scores_gemma":[0.9983007,0.000078234014,0.00075220934,0.00018979415,0.00059111015,0.000087951215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000499627,0.00021130865,0.0002097979,0.00061172125,0.00017191599,0.00035437578,0.00083578774,0.00005271257,0.0000115040675],"category_scores_gemma":[0.000028219969,0.00020544321,0.00008093003,0.00033556236,0.00004498078,0.001688108,0.00032557608,0.00015298529,0.0000012090369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017194277,0.00008230773,0.00017416317,0.00003931631,0.00017031874,0.000016110169,0.00084128184,0.7776021,0.000007772087,0.002240653,0.0000050844487,0.21880375],"study_design_scores_gemma":[0.0011840906,0.00005737472,0.00044171352,0.00014631076,0.0000535573,0.00003616621,0.0004775031,0.99436796,0.000046645404,0.0024363794,0.0005386053,0.00021370129],"about_ca_topic_score_codex":0.0000039300994,"about_ca_topic_score_gemma":4.2066378e-7,"teacher_disagreement_score":0.3951373,"about_ca_system_score_codex":0.00017619513,"about_ca_system_score_gemma":0.000015249065,"threshold_uncertainty_score":0.83777314},"labels":[],"label_agreement":null},{"id":"W2115276375","doi":"10.1002/qre.1591","title":"Using Genetic Algorithms to Design Experiments: A Review","year":2014,"lang":"en","type":"review","venue":"Quality and Reliability Engineering International","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Queen's University","funders":"","keywords":"Computer science; Implementation; Set (abstract data type); Optimal design; Range (aeronautics); Genetic algorithm; Mathematical optimization; Machine learning; Engineering; Mathematics; Software engineering","score_opus":0.11815220724140803,"score_gpt":0.41623084700981183,"score_spread":0.2980786397684038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115276375","genre_codex":"methods","genre_gemma":"review","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":[6.987796e-8,0.45652398,0.54206586,0.00007554174,0.00062386814,0.00057908066,0.000010739315,0.00010260504,0.0000182404],"genre_scores_gemma":[1.8131398e-7,0.5068109,0.49275565,0.00015852341,0.0000993008,0.00010666061,0.000009791893,0.000022033963,0.000036927926],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99685645,0.00033885374,0.0010716782,0.0009520215,0.00048595967,0.00029502067],"domain_scores_gemma":[0.9979508,0.0004698167,0.00031704296,0.0007653002,0.00027775136,0.00021930883],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011695191,0.00046586734,0.0011980224,0.00023328043,0.000074861426,0.00013227016,0.0010070086,0.00018348635,0.000021219974],"category_scores_gemma":[0.0011576775,0.00042733506,0.00025367422,0.00042453883,0.00003333052,0.00027090596,0.0004893041,0.0003217106,0.000028207814],"study_design_candidate":"design_other","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":[0.0000023775413,0.00012343492,0.0000011500226,0.018307462,0.0001633517,0.000008579585,0.00012462198,0.1298298,0.0000021769765,0.002229371,0.00012939192,0.8490783],"study_design_scores_gemma":[0.000134977,0.000033817243,0.00000812688,0.012597709,0.000061614504,0.00006340212,0.0000012730721,0.34857193,0.000003167876,0.00008439079,0.6378406,0.00059898075],"about_ca_topic_score_codex":0.000014141201,"about_ca_topic_score_gemma":9.466318e-8,"teacher_disagreement_score":0.84847933,"about_ca_system_score_codex":0.00043648577,"about_ca_system_score_gemma":0.00013028979,"threshold_uncertainty_score":0.99981785},"labels":[],"label_agreement":null},{"id":"W2116193148","doi":"10.1109/tmag.2009.2012694","title":"Multi-Objective Optimization Applied to the Matching of a Specified Torque-Speed Curve for an Internal Permanent Magnet Motor","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Torque; Computer science; Magnet; Control theory (sociology); Direct torque control; Voltage; Induction motor; Physics; Electrical engineering; Engineering","score_opus":0.02438668565967948,"score_gpt":0.28029162879351527,"score_spread":0.25590494313383577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116193148","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":[0.0009051521,0.00001373174,0.9959932,0.0003935295,0.00059566594,0.0015778058,0.00008043455,0.00015299191,0.00028748493],"genre_scores_gemma":[0.330416,0.000020730717,0.6686277,0.0003960997,0.00006435986,0.00007062697,0.0000058523883,0.000024012104,0.0003746481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828714,0.000084309766,0.00042822975,0.0005490616,0.00033568512,0.00031555654],"domain_scores_gemma":[0.99854594,0.00014566928,0.00017036124,0.0006394031,0.0003472917,0.00015130841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021167874,0.00027144104,0.0002524223,0.0002525255,0.00024616875,0.00011849394,0.0007358576,0.00008999673,0.000036165413],"category_scores_gemma":[0.000013958755,0.00024105185,0.00011688777,0.00046648603,0.000056383476,0.00033990943,0.0000068935597,0.00023008963,0.00001192311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000162836,0.00041994898,3.478344e-7,0.0000059386666,0.000014561827,8.763762e-7,0.0030873104,0.95108527,0.007352782,0.00034625575,0.000024002857,0.03749986],"study_design_scores_gemma":[0.001374939,0.0013943573,0.0003818479,0.00001970533,0.000027723605,0.0000087086655,0.0004343198,0.97696054,0.01874001,0.00022866153,0.00012436455,0.0003048502],"about_ca_topic_score_codex":0.000023700411,"about_ca_topic_score_gemma":0.0000369988,"teacher_disagreement_score":0.32951084,"about_ca_system_score_codex":0.00014121438,"about_ca_system_score_gemma":0.00005362096,"threshold_uncertainty_score":0.9829809},"labels":[],"label_agreement":null},{"id":"W2117561764","doi":"10.2514/6.2000-4938","title":"Aircraft conceptual design using genetic algorithms","year":2000,"lang":"en","type":"article","venue":"8th Symposium on Multidisciplinary Analysis and Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Conceptual design; Genetic algorithm; Algorithm design; Algorithm; Machine learning; Human–computer interaction","score_opus":0.021407334126306625,"score_gpt":0.2741287671290095,"score_spread":0.2527214330027029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117561764","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":[0.0035527493,0.00012730944,0.995072,0.00022763638,0.00010925641,0.00043030013,0.000011176846,0.00021557014,0.00025397565],"genre_scores_gemma":[0.035300512,0.0005393251,0.9632961,0.0001203776,0.000082664694,0.00003305884,0.00004904624,0.000034008877,0.0005448745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99716634,0.00032670627,0.00055554445,0.001087167,0.00043469205,0.00042954177],"domain_scores_gemma":[0.9984867,0.00016782309,0.00022358102,0.00067295553,0.00021216476,0.00023680391],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028055388,0.0003958042,0.00047508872,0.0005764617,0.00068172946,0.00022790537,0.00045378305,0.00015334335,0.00022532884],"category_scores_gemma":[0.000018764984,0.00038016058,0.0001847501,0.002317721,0.0001869105,0.00092087424,0.00015964621,0.00017389306,0.000023084232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035085715,0.00015932407,0.00038234866,0.0000034474258,0.00018366473,0.000015956763,0.000872527,0.99042034,0.00019554458,0.00013414642,0.000003957843,0.0075936643],"study_design_scores_gemma":[0.0007798699,0.00021091464,0.0016459936,0.000015999278,0.00028016733,0.000017851733,0.000089728426,0.99598104,0.00042757308,0.00006164619,0.00002503417,0.00046417947],"about_ca_topic_score_codex":0.000021424528,"about_ca_topic_score_gemma":0.000002059177,"teacher_disagreement_score":0.031775896,"about_ca_system_score_codex":0.00012355945,"about_ca_system_score_gemma":0.00006503104,"threshold_uncertainty_score":0.99986506},"labels":[],"label_agreement":null},{"id":"W2118097554","doi":"10.2307/3315868","title":"Design and analysis of computer experiments when the output is highly correlated over the input space","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Realization (probability); Limiting; Gaussian process; Process (computing); Code (set theory); Function (biology); Gaussian; Mathematical optimization; Mathematics; Stochastic process; Applied mathematics; Computer science; Source code; Algorithm; Correlation; Space (punctuation); Statistics; Engineering","score_opus":0.029100535860775448,"score_gpt":0.23941308834084127,"score_spread":0.2103125524800658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118097554","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":[0.00061678205,0.00042300695,0.9977175,0.000757366,0.00027831498,0.00010668219,0.000056128578,0.000003712919,0.000040511153],"genre_scores_gemma":[0.12922587,0.00008054295,0.8693828,0.0009490437,0.000035840025,0.0000012903885,0.0000018130357,0.000011771869,0.00031102874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895865,0.0001374647,0.00033749305,0.00013411918,0.00024018403,0.0001921199],"domain_scores_gemma":[0.9983211,0.00037143327,0.00039786324,0.00027238813,0.0004043722,0.0002327992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022510828,0.00011604722,0.00022495107,0.00028809826,0.00018064218,0.00012587462,0.0005115454,0.000038446477,0.00008941285],"category_scores_gemma":[0.00007464667,0.00007545517,0.00004646723,0.0005730391,0.0001848284,0.00019674144,0.000038199814,0.00017623235,0.000002873931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019160785,0.00009434682,0.0059900116,0.000012601251,0.002942712,0.00042366743,0.074070364,0.6904868,0.00004320704,0.0183078,0.11515373,0.092455596],"study_design_scores_gemma":[0.00030985143,0.00008304098,0.006390146,0.000011528718,0.00014472744,0.000028769015,0.000068992114,0.9907577,0.00005134731,0.00049185904,0.0015704955,0.000091542504],"about_ca_topic_score_codex":0.0004146729,"about_ca_topic_score_gemma":0.00022325612,"teacher_disagreement_score":0.3002709,"about_ca_system_score_codex":0.000099978584,"about_ca_system_score_gemma":0.00015317005,"threshold_uncertainty_score":0.30769727},"labels":[],"label_agreement":null},{"id":"W2118101551","doi":"10.1109/fitme.2009.156","title":"Casting Design through Multi-objective Optimization","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer 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 Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flexibility (engineering); Sensitivity (control systems); Computer science; Mathematical optimization; Casting; Multi-objective optimization; Process (computing); Evolutionary algorithm; Engineering design process; Optimization problem; Engineering optimization; Engineering; Algorithm; Mathematics; Artificial intelligence; Machine learning; Mechanical engineering; Materials science","score_opus":0.04437748358033325,"score_gpt":0.2972923484010716,"score_spread":0.2529148648207383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118101551","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":[0.00000412658,0.000046094556,0.9924039,0.00034544716,0.00021492025,0.00042301076,7.3838135e-7,0.00070563913,0.005856104],"genre_scores_gemma":[0.013358826,0.000020535555,0.9848512,0.000987107,0.000047526446,0.000015081695,0.0000029395258,0.0000147126,0.00070207834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840826,0.000120571436,0.00027285726,0.0005912628,0.00024826726,0.00035880445],"domain_scores_gemma":[0.99885297,0.00014289626,0.00014338395,0.0004336202,0.0003422763,0.00008484447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001888219,0.000219695,0.00018493093,0.00010983487,0.00025500733,0.00015208061,0.00050643506,0.00007852624,0.00003758745],"category_scores_gemma":[0.00025050517,0.0002087084,0.000053762265,0.00084021816,0.000035139336,0.002003277,0.00009503907,0.00014287264,0.000048654594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045115085,0.00009442172,0.0000077285795,8.50483e-7,0.000006764951,0.000011251956,0.000830061,0.9747207,0.00015611215,0.005907023,0.000049047943,0.018211495],"study_design_scores_gemma":[0.00069182477,0.000120415,0.00018671446,0.000009655972,0.0000036170773,0.0000308391,0.000075595475,0.9927997,0.0044264016,0.001334712,0.000049042068,0.00027146004],"about_ca_topic_score_codex":0.00001247125,"about_ca_topic_score_gemma":0.0000012768713,"teacher_disagreement_score":0.018078992,"about_ca_system_score_codex":0.00014510773,"about_ca_system_score_gemma":0.00007507235,"threshold_uncertainty_score":0.85108817},"labels":[],"label_agreement":null},{"id":"W2118311688","doi":"10.1017/s0890060401151024","title":"Kriging as a surrogate fitness landscape in evolutionary optimization","year":2001,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Kriging; Fitness landscape; Fitness approximation; Mathematical optimization; Surrogate model; Interpolation (computer graphics); Evolutionary algorithm; Fitness function; Computer science; Process (computing); Function (biology); Black box; Engineering design process; Mathematics; Genetic algorithm; Machine learning; Artificial intelligence; Engineering","score_opus":0.02283994912642328,"score_gpt":0.26412463775253625,"score_spread":0.24128468862611296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118311688","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":[0.003957447,0.00008063394,0.9953195,0.00009042834,0.00011189266,0.0002638158,0.0000013107128,0.00014236993,0.000032625077],"genre_scores_gemma":[0.5640389,0.00010773608,0.43571258,0.000017992328,0.000028909142,0.000046843594,0.000008856444,0.000012029266,0.000026149224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867517,0.000029835019,0.00036264004,0.00045850957,0.00014834982,0.00032548542],"domain_scores_gemma":[0.99929154,0.00024154012,0.00008771371,0.00022247655,0.00007075759,0.00008599687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032507523,0.00018655151,0.0002482425,0.0007605444,0.00012495794,0.00015172159,0.00024290747,0.00006108774,0.0000348884],"category_scores_gemma":[0.00010463844,0.00019756625,0.000093315786,0.0009433417,0.00001920705,0.0005991183,0.0000669506,0.000098537465,0.0000045612874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001404219,0.000026210897,0.00009405747,0.00000660285,0.000059265964,0.000010351238,0.00018926748,0.9740255,0.0000954788,0.0027698937,8.160657e-7,0.022708531],"study_design_scores_gemma":[0.000055695167,0.00003090029,0.00022011057,0.000013185938,0.00004056461,0.000008331632,0.00007320486,0.9834166,0.013966683,0.0018919004,0.00005479871,0.00022802847],"about_ca_topic_score_codex":0.000048334816,"about_ca_topic_score_gemma":0.00001717968,"teacher_disagreement_score":0.5600814,"about_ca_system_score_codex":0.000055982928,"about_ca_system_score_gemma":0.000020652644,"threshold_uncertainty_score":0.8056518},"labels":[],"label_agreement":null},{"id":"W2121400278","doi":"10.1109/cisda.2011.5945946","title":"Multi-objective evolutionary optimization of a military air transportation fleet mix with the flexibility objective","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Flexibility (engineering); Multi-objective optimization; Sorting; Task (project management); Computer science; Genetic algorithm; Mathematical optimization; Pareto principle; Monte Carlo method; Operations research; Fleet management; Stochastic programming; Engineering; Mathematics; Systems engineering; Algorithm","score_opus":0.020820380523508236,"score_gpt":0.24738306747208538,"score_spread":0.22656268694857715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121400278","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":[0.003291012,0.00016518019,0.9937066,0.00014229688,0.00013350694,0.0009930938,0.000038469374,0.000274291,0.0012555636],"genre_scores_gemma":[0.34618673,0.000019583487,0.6533802,0.00012365855,0.000013450846,0.00008774716,0.00002275465,0.00001781332,0.00014808388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978421,0.00022685954,0.00042704196,0.00076122367,0.00043087473,0.0003119207],"domain_scores_gemma":[0.9976651,0.00016903333,0.00024593464,0.0007728505,0.0010499108,0.000097153985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029781947,0.00029734502,0.00028809303,0.00017705068,0.0002502534,0.0000064070023,0.00064822956,0.000104382874,0.0000707771],"category_scores_gemma":[0.00007102085,0.00020626241,0.00011377076,0.001128922,0.00037930804,0.0014449751,0.00006077018,0.00020649224,0.000007592887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024499858,0.00065636873,0.001953999,0.00002096545,0.00011493232,0.00000780689,0.015106195,0.9778311,0.0001507824,0.0023388956,0.00006955971,0.0015043603],"study_design_scores_gemma":[0.0012564235,0.00036928424,0.13152137,0.000022400052,0.000032403274,0.000010820229,0.0018808692,0.85965383,0.004364944,0.00054918363,0.000014306647,0.00032414048],"about_ca_topic_score_codex":0.0005972096,"about_ca_topic_score_gemma":0.0004460789,"teacher_disagreement_score":0.34289572,"about_ca_system_score_codex":0.00018142388,"about_ca_system_score_gemma":0.00021969738,"threshold_uncertainty_score":0.8411137},"labels":[],"label_agreement":null},{"id":"W2121410096","doi":"10.4271/2004-01-0240","title":"Design Space Reduction for Multi-Objective Optimization and Robust Design Optimization Problems","year":2004,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mathematical optimization; Linear subspace; Computer science; Multi-objective optimization; Set (abstract data type); Pareto principle; Optimization problem; Reduction (mathematics); Engineering optimization; Point (geometry); Computation; Pareto optimal; Optimal design; Engineering design process; Mathematics; Algorithm; Engineering; Machine learning","score_opus":0.03414118991731742,"score_gpt":0.2673352140016915,"score_spread":0.23319402408437412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121410096","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":[0.00011992132,0.00023532829,0.9886597,0.0025351166,0.00033489443,0.0047082608,0.00001837981,0.0028627962,0.00052564027],"genre_scores_gemma":[0.16406117,0.00054575433,0.8332419,0.00043995376,0.00009575454,0.0012529694,0.00004478452,0.00014288047,0.00017485989],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9947376,0.00034691702,0.0010389495,0.0021288595,0.00076201913,0.0009856394],"domain_scores_gemma":[0.996657,0.0005109125,0.0005438265,0.0012394837,0.00063746225,0.00041128905],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010271905,0.00092058565,0.0008288543,0.0004575423,0.0009033305,0.00036474882,0.0010797186,0.0007275021,0.000034688648],"category_scores_gemma":[0.001242059,0.00088614767,0.00023378133,0.0016506229,0.0007852429,0.0025329501,0.00043093212,0.00077193294,0.000013044758],"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":[0.0001938096,0.00040711055,0.0000017196594,0.000027314973,0.00003457119,0.000006943673,0.00013522546,0.7630771,0.22829458,0.006069504,0.00008555404,0.0016666083],"study_design_scores_gemma":[0.08328577,0.056156665,0.37302774,0.0056855185,0.0018957696,0.005374644,0.0028443183,0.33435085,0.025505655,0.07833289,0.005993572,0.027546607],"about_ca_topic_score_codex":0.000020144036,"about_ca_topic_score_gemma":0.0008626715,"teacher_disagreement_score":0.4287262,"about_ca_system_score_codex":0.0009186185,"about_ca_system_score_gemma":0.00028874626,"threshold_uncertainty_score":0.9993589},"labels":[],"label_agreement":null},{"id":"W2124035050","doi":"10.1109/cec.2008.4982907","title":"Optimizing the stochastic fleet estimation model using evolutionary computation","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Defence Research and Development Canada","funders":"","keywords":"Genetic algorithm; Computation; Computer science; Evolutionary computation; Monte Carlo method; Set (abstract data type); Mathematical optimization; Stochastic modelling; Function (biology); Evolutionary algorithm; Algorithm; Machine learning; Mathematics","score_opus":0.04387516469604175,"score_gpt":0.2919703577495393,"score_spread":0.24809519305349753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124035050","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":[0.0009980965,0.000047603568,0.9975787,0.00023294562,0.00016014895,0.00026609265,0.0000013942473,0.00028848273,0.0004265439],"genre_scores_gemma":[0.297641,0.0000024539786,0.7020529,0.00016189065,0.000020153204,0.000009736973,0.000004895455,0.000009242298,0.00009775071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988806,0.000053378477,0.00022593254,0.00031700253,0.00031500577,0.00020806193],"domain_scores_gemma":[0.9991412,0.00012538371,0.00016393252,0.00027671744,0.00023683964,0.00005592828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011159525,0.00013806332,0.00010845334,0.000111773814,0.0006529433,0.000051971143,0.00034537262,0.000041368647,0.0000048529632],"category_scores_gemma":[0.00008728522,0.00011042383,0.000043036587,0.00047432125,0.00009431306,0.0011454091,0.00016807941,0.00011163298,0.00002028931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020408927,0.000024287609,0.0000032354296,0.0000012034343,0.000005080848,0.000001930381,0.00066131103,0.9934805,0.00006979935,0.0036339997,0.000054860084,0.0020617743],"study_design_scores_gemma":[0.00025391282,0.00001300364,0.00010802644,0.0000073750057,0.0000041909802,0.000108733424,0.000031062584,0.99475455,0.000054677064,0.004519557,0.0000014825029,0.00014342401],"about_ca_topic_score_codex":0.000012453143,"about_ca_topic_score_gemma":7.348816e-7,"teacher_disagreement_score":0.2966429,"about_ca_system_score_codex":0.00017551138,"about_ca_system_score_gemma":0.00013986361,"threshold_uncertainty_score":0.50219774},"labels":[],"label_agreement":null},{"id":"W2126489459","doi":"10.1016/j.inffus.2008.11.003","title":"Overfitting cautious selection of classifier ensembles with genetic algorithms","year":2008,"lang":"en","type":"article","venue":"Information Fusion","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Overfitting; Computer science; Artificial intelligence; Machine learning; Classifier (UML); Ensemble learning; Selection (genetic algorithm); Random subspace method; Data mining; Pattern recognition (psychology); Artificial neural network","score_opus":0.011274993820370434,"score_gpt":0.21826553205423144,"score_spread":0.20699053823386102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126489459","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":[0.03508334,0.000013634004,0.9623412,0.000052966614,0.00016199582,0.00022243788,0.000002647756,0.00017719402,0.0019446089],"genre_scores_gemma":[0.4019483,0.000056612313,0.5976739,0.00013907936,0.000030442647,0.000015428921,0.000011985507,0.0000074388963,0.00011684266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883664,0.00003402755,0.00039016106,0.00015277887,0.0004050377,0.00018134166],"domain_scores_gemma":[0.9987955,0.00004527117,0.00036295832,0.00022188824,0.00051493954,0.000059405123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009606965,0.00013154424,0.00013684802,0.00024587274,0.000260111,0.000037979287,0.00020476463,0.00006927561,0.00002021028],"category_scores_gemma":[0.00005240028,0.00011471514,0.00003262744,0.00070575956,0.000055790668,0.0021868472,0.000083917184,0.00010300506,0.000034516015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091899296,0.00019152944,0.007625027,0.000089250054,0.00005407768,0.000017459013,0.012921606,0.48871994,0.004655548,0.0036859415,0.0008046618,0.48114306],"study_design_scores_gemma":[0.00087920175,0.00017036508,0.021451052,0.00003086053,0.0000043385976,0.00025546664,0.00009941643,0.96278894,0.011837034,0.00007870654,0.002201473,0.00020314351],"about_ca_topic_score_codex":0.000032526943,"about_ca_topic_score_gemma":0.0000039083693,"teacher_disagreement_score":0.48093992,"about_ca_system_score_codex":0.00008870278,"about_ca_system_score_gemma":0.00011618387,"threshold_uncertainty_score":0.46779478},"labels":[],"label_agreement":null},{"id":"W2126901434","doi":"10.1504/ijise.2010.033997","title":"A computational intelligent approach to estimate the Weibull parameters","year":2010,"lang":"en","type":"article","venue":"International Journal of Industrial and Systems Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Weibull distribution; Censoring (clinical trials); Sample size determination; Statistics; Shape parameter; Scale parameter; Warranty; Mathematics; Computer science","score_opus":0.02893584695091346,"score_gpt":0.28629667725962704,"score_spread":0.2573608303087136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126901434","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":[0.020285541,0.000032569886,0.9743817,0.0005512096,0.0044852234,0.00014310867,0.0000030367971,0.000022772887,0.000094860115],"genre_scores_gemma":[0.7103637,0.000003014671,0.28896177,0.00005136284,0.00057446264,0.000008968977,0.0000013431227,0.000009086216,0.000026297494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998887,0.000026405365,0.00040573758,0.00013145372,0.00043163577,0.000117748015],"domain_scores_gemma":[0.99897134,0.00019185539,0.00019704511,0.00010006663,0.00042193406,0.00011773637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004846463,0.000108230895,0.00014695674,0.00020252753,0.00004480156,0.00029136174,0.0006562256,0.00005660908,0.0000010738052],"category_scores_gemma":[0.00034954448,0.00007610963,0.00004928498,0.00016279574,0.000020222833,0.00030270804,0.000115025534,0.00037797232,0.0000023190294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009043167,0.000022255137,0.00009521612,0.000001564531,0.00006171409,0.000008959728,0.00027434327,0.97848886,0.00020918944,0.013061864,0.000083425264,0.0076835784],"study_design_scores_gemma":[0.0004129813,0.000038221155,0.000141795,0.00004479054,0.000004279329,0.00069578685,0.000047323338,0.9955619,0.000176331,0.00011842444,0.0026612703,0.00009691883],"about_ca_topic_score_codex":0.0000109279445,"about_ca_topic_score_gemma":3.351219e-7,"teacher_disagreement_score":0.69007814,"about_ca_system_score_codex":0.00005232351,"about_ca_system_score_gemma":0.000055048073,"threshold_uncertainty_score":0.31036606},"labels":[],"label_agreement":null},{"id":"W2128628754","doi":"10.1007/s10479-011-1045-6","title":"Multiobjective scatter search for a commercial territory design problem","year":2012,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; HEC Montréal","funders":"Instituto Tecnológico y de Estudios Superiores de Monterrey; Universidad de León; Ministerio de Ciencia e Innovación; Universidad Autónoma de Nuevo León; Consejo Nacional de Ciencia y Tecnología","keywords":"Tabu search; Heuristics; Mathematical optimization; Metaheuristic; Sorting; Computer science; Local search (optimization); Theory of computation; Genetic algorithm; Multi-objective optimization; Heuristic; Mathematics; Algorithm","score_opus":0.3634122278103288,"score_gpt":0.4893909142891731,"score_spread":0.1259786864788443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128628754","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":[0.0017762565,0.00014347398,0.9925055,0.0029308652,0.00012104125,0.0017038016,0.000024929554,0.000049813665,0.0007443282],"genre_scores_gemma":[0.3377055,0.000035387908,0.6604506,0.00023350374,0.0002100608,0.00063788303,0.000012993767,0.000021824886,0.0006922966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99726915,0.0006932766,0.00031303646,0.0003539177,0.00063335197,0.00073727267],"domain_scores_gemma":[0.995887,0.0006539039,0.000029151604,0.00051129266,0.0027190812,0.0001995781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002913869,0.00013426767,0.00018889691,0.00051179883,0.0006430519,0.00015441763,0.00073128147,0.00008472333,0.00003054716],"category_scores_gemma":[0.00030688217,0.00012781247,0.00007865719,0.0007619591,0.00024149292,0.0017518576,0.00030398986,0.0002984208,0.000057983198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003472897,0.0048950333,0.0028542865,0.00019822204,0.0003416878,0.0000056159133,0.045128748,0.55586576,0.025331173,0.106331095,0.023032323,0.23566873],"study_design_scores_gemma":[0.0014091375,0.000763484,0.0067473426,0.000062197614,0.000005271166,0.000012225444,0.00060265773,0.751836,0.23254159,0.0007132791,0.0048433226,0.000463477],"about_ca_topic_score_codex":0.00012799814,"about_ca_topic_score_gemma":0.000021159989,"teacher_disagreement_score":0.33592924,"about_ca_system_score_codex":0.000073037576,"about_ca_system_score_gemma":0.00032010832,"threshold_uncertainty_score":0.5212041},"labels":[],"label_agreement":null},{"id":"W2131420534","doi":"10.1108/03321640710751217","title":"Hybrid genetic algorithms using quadratic local search operators","year":2007,"lang":"en","type":"article","venue":"COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematical optimization; Local search (optimization); Benchmark (surveying); Algorithm; Computer science; Quadratic equation; Convergence (economics); Operator (biology); Optimization problem; Function (biology); Genetic algorithm; Mathematics","score_opus":0.01582700624974915,"score_gpt":0.29988942850420613,"score_spread":0.284062422254457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131420534","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":[0.041438323,0.0005640097,0.9572812,0.00021610499,0.00026101875,0.00019590759,6.241218e-7,0.00003274322,0.000010124625],"genre_scores_gemma":[0.5648922,0.00014407675,0.43475014,0.00008838488,0.00009227625,0.0000045109414,0.0000012521422,0.000014215951,0.000012990114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987087,0.000021203103,0.00039117094,0.00019453486,0.00030690216,0.00037747802],"domain_scores_gemma":[0.99903506,0.00049262564,0.00008716969,0.00007279633,0.00021932073,0.00009300257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007050631,0.00014484704,0.00016148329,0.00027448166,0.00015761591,0.00022957355,0.00037139785,0.000034305835,0.0000010026611],"category_scores_gemma":[0.000099158424,0.00011645205,0.000042180352,0.00023467717,0.00003589821,0.00021923383,0.00009791835,0.00035473588,5.7020486e-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":[0.00001352362,0.000076748656,0.000025056235,0.000017203507,0.0000614126,0.000023579258,0.00049684144,0.8571435,0.0007041026,0.047483772,0.0000063996417,0.09394782],"study_design_scores_gemma":[0.00060424895,0.000079031735,0.00013407324,0.000030336589,0.000006193527,0.0011033199,0.000049263046,0.9809926,0.0005837661,0.016189907,0.00009132256,0.00013589476],"about_ca_topic_score_codex":0.000003855657,"about_ca_topic_score_gemma":0.0000024724632,"teacher_disagreement_score":0.52345383,"about_ca_system_score_codex":0.0003014732,"about_ca_system_score_gemma":0.00009173157,"threshold_uncertainty_score":0.4748777},"labels":[],"label_agreement":null},{"id":"W2131534747","doi":"10.1177/1063293x06063842","title":"Modeling of Non-linear Relations among Different Design and Manufacturing Evaluation Measures for Multiobjective Optimal Concurrent Design","year":2006,"lang":"en","type":"article","venue":"Concurrent Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Measure (data warehouse); Weighting; Calipers; Relation (database); Multi-objective optimization; Concurrent engineering; Computer science; Engineering; Industrial engineering; Mathematical optimization; Mathematics; Data mining; Scheduling (production processes)","score_opus":0.03447491840577782,"score_gpt":0.2751872620252006,"score_spread":0.2407123436194228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131534747","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":[0.013653904,0.00046186856,0.9834641,0.000009230154,0.00052818115,0.0017221889,0.000007714951,0.00014633502,0.000006426816],"genre_scores_gemma":[0.618949,0.00001231293,0.3807158,0.0000011179019,0.00004943435,0.0002333353,0.000012136788,0.000020540336,0.000006334863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982721,0.000082706196,0.00046857586,0.0004934528,0.0003707347,0.00031241833],"domain_scores_gemma":[0.99874735,0.00038002137,0.00016445604,0.00022552184,0.00039678602,0.00008588074],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005085339,0.0002899779,0.0003037344,0.00024725214,0.00013792513,0.00006129641,0.00023382479,0.000081578735,0.0000021249577],"category_scores_gemma":[0.00018000371,0.00029551334,0.000082634695,0.00012177444,0.000040482064,0.0005918956,0.0000950316,0.0001645524,8.278305e-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":[0.000005329599,0.00004666814,0.000041607924,0.000027876398,0.000028897419,3.574083e-7,0.0003026091,0.9838506,0.0010248228,0.00030776483,0.0000040862847,0.014359394],"study_design_scores_gemma":[0.001413156,0.00007469953,0.0010239466,0.0001106206,0.00004733495,0.0000022542406,0.000024355226,0.9738687,0.023060648,0.00007398095,0.000006513581,0.000293764],"about_ca_topic_score_codex":0.0000084491485,"about_ca_topic_score_gemma":0.0000013031264,"teacher_disagreement_score":0.60529506,"about_ca_system_score_codex":0.00024892986,"about_ca_system_score_gemma":0.00006169103,"threshold_uncertainty_score":0.9999497},"labels":[],"label_agreement":null},{"id":"W2135491697","doi":"10.2514/6.2006-7048","title":"Comparison of Three Surrogate Modeling Techniques: Datascape, Kriging, and Second Order Regression","year":2006,"lang":"en","type":"article","venue":"11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Kriging; Surrogate model; Robustness (evolution); Computer science; Data mining; Mathematical optimization; Algorithm; Machine learning; Mathematics","score_opus":0.02758020689390097,"score_gpt":0.31733893492933113,"score_spread":0.2897587280354302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135491697","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":[0.01145481,0.00040082063,0.9869041,0.00012987899,0.0000518776,0.00033646435,0.00004448975,0.0001696807,0.00050786365],"genre_scores_gemma":[0.46778,0.00015446407,0.53171366,0.000009826011,0.000017240738,0.000018382028,0.00019215606,0.000013738773,0.00010051558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973378,0.00012962935,0.0008481875,0.000969132,0.00038095994,0.00033424396],"domain_scores_gemma":[0.99754274,0.00013294212,0.00059375056,0.0007052386,0.000880346,0.00014500857],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038187933,0.00038156367,0.0007448687,0.0006686863,0.0004594571,0.000241121,0.0004908387,0.00015986255,0.00010197098],"category_scores_gemma":[0.0000622342,0.0003419912,0.00009796484,0.0019447011,0.00018770482,0.0014374327,0.0006247094,0.0002212277,0.0000011304414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020076179,0.00015982606,0.019448236,0.000037144382,0.00009620377,0.000003590626,0.0003648755,0.97199684,0.00023986513,0.002174759,0.000014585769,0.0054440224],"study_design_scores_gemma":[0.0004977391,0.0000709353,0.0022189417,0.00006992851,0.00017169329,0.0000052441424,0.00018355086,0.99433154,0.0013745831,0.000659869,0.000025911775,0.00039009153],"about_ca_topic_score_codex":0.00013033314,"about_ca_topic_score_gemma":0.00036843342,"teacher_disagreement_score":0.45632517,"about_ca_system_score_codex":0.000031441145,"about_ca_system_score_gemma":0.000089087785,"threshold_uncertainty_score":0.9999032},"labels":[],"label_agreement":null},{"id":"W2138002307","doi":"10.1007/s00158-009-0420-2","title":"Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions","year":2009,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":588,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Manitoba","funders":"","keywords":"Computer science; Black box; Curse of dimensionality; Mathematical optimization; Engineering optimization; Optimization problem; Industrial engineering; Management science; Data science; Machine learning; Artificial intelligence; Algorithm; Engineering; Mathematics","score_opus":0.021485025260407235,"score_gpt":0.26375841630971775,"score_spread":0.2422733910493105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138002307","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":[0.020431101,0.000074886666,0.97804946,0.00034803312,0.000111161295,0.0008218999,0.000031733576,0.00011387756,0.000017858847],"genre_scores_gemma":[0.4079709,0.000019603916,0.5917443,0.00003388555,0.000015232064,0.000010370917,0.00018090905,0.00001210214,0.000012721886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819344,0.0001105464,0.0004254567,0.0006722097,0.00033524877,0.00026310707],"domain_scores_gemma":[0.9980581,0.00014060225,0.0002315088,0.00022420053,0.0011811125,0.0001644674],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019480509,0.0003112298,0.00031680733,0.00025454556,0.0004675647,0.00015976925,0.00017518799,0.00009127403,0.000007268524],"category_scores_gemma":[0.000054835673,0.00026154553,0.0000236159,0.0006886152,0.00013055402,0.0015496382,0.00015621641,0.0001227042,7.1910836e-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":[0.00011703317,0.000032848468,0.00007272045,0.000014379856,0.00002408725,0.0000020744562,0.0012477452,0.9957951,0.00013797126,0.0011105931,0.0000042526663,0.0014412237],"study_design_scores_gemma":[0.0010444225,0.00058955606,0.009208691,0.000050150233,0.000024113324,0.000028138418,0.0002392116,0.9873067,0.00006739855,0.0011040416,8.379229e-8,0.0003374491],"about_ca_topic_score_codex":0.000064105545,"about_ca_topic_score_gemma":0.000023635977,"teacher_disagreement_score":0.3875398,"about_ca_system_score_codex":0.000052133466,"about_ca_system_score_gemma":0.00014097829,"threshold_uncertainty_score":0.99998367},"labels":[],"label_agreement":null},{"id":"W2139642264","doi":"10.1109/iwfhr.2004.105","title":"Unsupervised Feature Selection for Ensemble of Classifiers","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Random subspace method; Computer science; Artificial intelligence; Hidden Markov model; Feature selection; Pattern recognition (psychology); Feature (linguistics); Machine learning; Context (archaeology); Selection (genetic algorithm); Set (abstract data type); Cascading classifiers; Ensemble learning; Support vector machine","score_opus":0.015412840965242924,"score_gpt":0.2631135485831697,"score_spread":0.24770070761792679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139642264","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":[0.0004859906,0.000015539346,0.9964529,0.00071074883,0.00015788038,0.0002782348,0.0000015002245,0.00014618957,0.0017509727],"genre_scores_gemma":[0.084595144,0.00000552055,0.9140049,0.00018239129,0.00002449012,0.000023220657,0.0000027697686,0.0000083687855,0.0011531597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935704,0.000011480411,0.00011269671,0.00024388381,0.000119325974,0.00015553905],"domain_scores_gemma":[0.99941504,0.000040716284,0.000060631734,0.00017785109,0.0002580477,0.000047684774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006666225,0.000086158725,0.00010835224,0.000086663225,0.00007333952,0.000023190734,0.00022338488,0.000061864775,0.000005662682],"category_scores_gemma":[0.000050954142,0.00007835935,0.000054099295,0.00044607418,0.000023323904,0.0003839533,0.00003407724,0.000058377045,0.000004252062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058229,0.0003210009,0.00030251112,0.000054474665,0.000065787855,0.0000015695839,0.0013045318,0.4768015,0.06384686,0.381589,0.0013325595,0.07432197],"study_design_scores_gemma":[0.0029620924,0.00029078018,0.0006085051,0.000017416576,0.000007904119,0.000015424148,0.000114309434,0.69466454,0.28088808,0.01790589,0.0022469843,0.00027809702],"about_ca_topic_score_codex":0.000011071229,"about_ca_topic_score_gemma":0.000016001057,"teacher_disagreement_score":0.3636831,"about_ca_system_score_codex":0.000085513144,"about_ca_system_score_gemma":0.00008838912,"threshold_uncertainty_score":0.31954014},"labels":[],"label_agreement":null},{"id":"W2143083947","doi":"10.5267/j.ijiec.2013.09.007","title":"A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems","year":2013,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Optimization algorithm; Computer science; Algorithm; Mathematics","score_opus":0.016273509657610593,"score_gpt":0.2571568847819251,"score_spread":0.2408833751243145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143083947","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":[0.00010760265,0.00002384248,0.99685436,0.00088034663,0.0011861529,0.0007656698,0.000020529522,0.0001456234,0.000015847785],"genre_scores_gemma":[0.045059424,0.000006178419,0.95436245,0.00008423054,0.0003168904,0.00006665496,0.00004964452,0.00003253722,0.000021959013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823874,0.000097944394,0.0007633625,0.00030861437,0.00034849282,0.00024287145],"domain_scores_gemma":[0.9962386,0.00066932,0.0006882396,0.00010967384,0.002122954,0.00017122531],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046392652,0.00025375572,0.0002959562,0.00067056774,0.00017443394,0.00043802988,0.00048415182,0.00014828941,0.000015982914],"category_scores_gemma":[0.0014784121,0.00026167693,0.00012325593,0.00030763503,0.000062790685,0.0013827898,0.00008258328,0.0005172911,8.873732e-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":[0.000008025976,0.00009604024,0.000017504577,0.000004011218,0.00013164198,0.000003164435,0.00022406859,0.95015985,0.00026308047,0.00023262708,0.00001868026,0.048841313],"study_design_scores_gemma":[0.00486374,0.00023964356,0.000029176415,0.00011775455,0.00002248813,0.00009008733,0.00008474,0.9940206,0.00014032607,0.00005917231,0.00008451166,0.00024778207],"about_ca_topic_score_codex":0.000024110821,"about_ca_topic_score_gemma":5.1025614e-7,"teacher_disagreement_score":0.048593532,"about_ca_system_score_codex":0.00024244306,"about_ca_system_score_gemma":0.0003046695,"threshold_uncertainty_score":0.99998355},"labels":[],"label_agreement":null},{"id":"W2146220287","doi":"10.1177/0021998314568168","title":"Curved fiber paths optimization of a composite cylindrical shell via Kriging-based approach","year":2015,"lang":"en","type":"article","venue":"Journal of Composite Materials","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Materials science; Composite laminates; Sequential quadratic programming; Finite element method; Curvilinear coordinates; Fiber; Shell (structure); Buckling; Flexibility (engineering); Maximization; Minification; Displacement (psychology); Structural engineering; Kriging; Optimal design; Quadratic programming; Composite number; Computer science; Composite material; Mathematical optimization; Mathematics; Geometry; Engineering","score_opus":0.022358493526088357,"score_gpt":0.26567186096578954,"score_spread":0.2433133674397012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146220287","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":[0.014208972,0.000107687454,0.98393404,0.00015100138,0.0007362337,0.00027113885,0.000013533736,0.00005677815,0.00052060286],"genre_scores_gemma":[0.2500892,0.000008350396,0.74956006,0.00011859691,0.00014195588,0.000004457546,0.000017322529,0.000026491463,0.000033598877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969947,0.0004824117,0.0012035648,0.00029813216,0.0007348444,0.00028635687],"domain_scores_gemma":[0.9963386,0.00013866178,0.001491611,0.00042215287,0.0013385521,0.00027044272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097534386,0.0002751947,0.00074017135,0.00037070384,0.000077310906,0.00021143537,0.00086186477,0.000120508725,0.000045661753],"category_scores_gemma":[0.000079672776,0.00023898925,0.00014638095,0.0005092594,0.000087530185,0.00087178446,0.00018121563,0.00016974602,0.000015008578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020826457,0.0004019605,0.00012408271,0.00002849016,0.00004932436,0.00002466661,0.0002682884,0.946298,0.05162167,0.000117425436,0.0000769085,0.0007809106],"study_design_scores_gemma":[0.0029761172,0.00038996368,0.00025320117,0.000093363014,0.000045397173,0.00017103815,0.000014385341,0.8364088,0.15880336,0.00024055409,0.00029283215,0.0003109703],"about_ca_topic_score_codex":0.0000081809485,"about_ca_topic_score_gemma":1.0744143e-7,"teacher_disagreement_score":0.23588021,"about_ca_system_score_codex":0.00013696893,"about_ca_system_score_gemma":0.0001950717,"threshold_uncertainty_score":0.97456986},"labels":[],"label_agreement":null},{"id":"W2147614493","doi":"10.1109/tmag.2010.2043512","title":"Reducing the Design Space of Standard Electromagnetic Devices Using Bayesian Response Surfaces","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"McGill University","funders":"","keywords":"Computer science; Set (abstract data type); Space (punctuation); Bayesian probability; Bayesian optimization; Design of experiments; Parameter space; Artificial intelligence; Mathematics; Statistics; Programming language","score_opus":0.017158156437938898,"score_gpt":0.26733003916599435,"score_spread":0.25017188272805546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147614493","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":[0.03990912,0.0000590438,0.95828044,0.0005892397,0.00059285434,0.00038741066,0.000010586241,0.00011269101,0.000058630398],"genre_scores_gemma":[0.48242548,0.000022115224,0.51732767,0.000031631123,0.000012233797,0.000007817353,1.06503684e-7,0.000016822083,0.00015609711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818593,0.00037205376,0.00032213607,0.00039195642,0.0004018682,0.00032604698],"domain_scores_gemma":[0.9978876,0.000807854,0.00017625323,0.00075028936,0.00028337745,0.000094620824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066809583,0.00021675062,0.00020394992,0.00020217826,0.00036384183,0.00011516041,0.00064754125,0.000098642275,0.000070300295],"category_scores_gemma":[0.00005780336,0.00018217703,0.00007726247,0.00083091814,0.00021788677,0.00030333726,0.0000054627194,0.00045830684,0.0000055446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020719322,0.00008310465,0.0000030615395,0.000007832771,0.000016381107,0.000004591793,0.0007961949,0.7085525,0.27138802,0.00012757446,0.000010086568,0.018803468],"study_design_scores_gemma":[0.00037693605,0.00067618175,0.00013759464,0.000019180723,0.000029819877,0.000035302084,0.00007717892,0.67211396,0.3259827,0.0002126206,0.00014386576,0.00019469688],"about_ca_topic_score_codex":0.00003089308,"about_ca_topic_score_gemma":0.000046906825,"teacher_disagreement_score":0.44251636,"about_ca_system_score_codex":0.000060049148,"about_ca_system_score_gemma":0.00025718418,"threshold_uncertainty_score":0.7428964},"labels":[],"label_agreement":null},{"id":"W2148259137","doi":"10.1109/cisda.2009.5356525","title":"Minimizing risk on a fleet mix problem with a multiobjective evolutionary algorithm","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; University of Waterloo","funders":"","keywords":"Sorting; Mathematical optimization; Genetic algorithm; Computer science; Monte Carlo method; Multi-objective optimization; Set (abstract data type); Task (project management); Evolutionary algorithm; Pareto principle; Pareto optimal; Duration (music); Algorithm; Engineering; Mathematics","score_opus":0.006984395624691636,"score_gpt":0.2354641947237092,"score_spread":0.22847979909901756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148259137","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":[0.00018147899,0.000052361218,0.9862951,0.0004935985,0.00010284142,0.0006470682,0.000012377636,0.00065971713,0.011555473],"genre_scores_gemma":[0.019804502,0.000023829713,0.9783041,0.00070591626,0.00007307241,0.000061007027,0.000007183303,0.000020379131,0.0010000352],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774206,0.00013031153,0.00028016116,0.0008824014,0.00049307704,0.00047198104],"domain_scores_gemma":[0.99846804,0.00016762246,0.00021380256,0.0005966199,0.00038144394,0.00017244673],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016551929,0.00032516418,0.00025447007,0.00027477503,0.0003487614,0.00010818547,0.00053192314,0.00008722643,0.000021547801],"category_scores_gemma":[0.00006662005,0.00026013103,0.000076027376,0.0009447777,0.000073381896,0.0010221854,0.00009419679,0.00030605172,0.00009100103],"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":[0.00011719221,0.0010593231,0.00025989374,0.0000056326426,0.00009601735,0.00010217991,0.0022671642,0.18192652,0.00012746181,0.014891649,0.000846534,0.79830045],"study_design_scores_gemma":[0.0019360011,0.0011101196,0.0071242414,0.000049645365,0.000012655225,0.00007642494,0.0001563576,0.98284584,0.0010364393,0.0045197196,0.0006147521,0.00051782886],"about_ca_topic_score_codex":0.000039469258,"about_ca_topic_score_gemma":0.0000078017065,"teacher_disagreement_score":0.8009193,"about_ca_system_score_codex":0.00027087145,"about_ca_system_score_gemma":0.00012896131,"threshold_uncertainty_score":0.9999851},"labels":[],"label_agreement":null},{"id":"W2149775970","doi":"10.1098/rspa.2014.0697","title":"Reduced dimensional Gaussian process emulators of parametrized partial differential equations based on Isomap","year":2014,"lang":"en","type":"article","venue":"Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Isomap; Dimensionality reduction; Emulation; Curse of dimensionality; Subspace topology; Gaussian process; Nonlinear dimensionality reduction; Mathematics; Kernel (algebra); Nonlinear system; Algorithm; Mathematical optimization; Computer science; Gaussian; Applied mathematics; Artificial intelligence","score_opus":0.009284712542864781,"score_gpt":0.23819348932994813,"score_spread":0.22890877678708335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149775970","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":[0.42455462,0.0000022917843,0.5748651,0.00021316063,0.000052079184,0.0001361795,0.0000012446465,0.000050627426,0.00012467922],"genre_scores_gemma":[0.93533915,1.3182483e-7,0.06456869,0.000021068034,0.000037260797,0.000015645797,1.4696683e-7,0.00000607324,0.000011853694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987393,0.000006923068,0.00022033331,0.00028662255,0.0005424838,0.00020430978],"domain_scores_gemma":[0.9991814,0.00038949383,0.00014637243,0.00009264789,0.00010877865,0.00008134397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020907134,0.00014478061,0.00026244516,0.000035360805,0.00016196606,0.000057013902,0.0004762706,0.00004034828,0.0000022555855],"category_scores_gemma":[0.00055707636,0.000090958056,0.0001494142,0.00057358603,0.00032118213,0.00018697597,0.00013051621,0.00012220621,7.3188073e-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":[0.000009688059,0.0006112148,0.00006674948,0.00032672935,0.000030495929,4.6368246e-8,0.0015383936,0.6407228,0.012595012,0.34358618,0.000015855992,0.00049679994],"study_design_scores_gemma":[0.00024967123,0.00010674997,0.00050941564,0.000102838625,0.000011934651,3.492518e-7,0.000026819836,0.9706504,0.018651348,0.009580847,6.813135e-7,0.00010895295],"about_ca_topic_score_codex":7.8211656e-7,"about_ca_topic_score_gemma":5.6305005e-9,"teacher_disagreement_score":0.5107845,"about_ca_system_score_codex":0.000014216474,"about_ca_system_score_gemma":0.000022341066,"threshold_uncertainty_score":0.3709162},"labels":[],"label_agreement":null},{"id":"W2151238122","doi":"10.1023/a:1012771025575","title":"A Taxonomy of Global Optimization Methods Based on Response Surfaces","year":2001,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2137,"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":"University of Waterloo; RAND Corporation","keywords":"Mathematics; Simple (philosophy); Mathematical optimization; Theme (computing); Management science; Algorithm; Computer science; Epistemology; Engineering","score_opus":0.02457143740247184,"score_gpt":0.3308629553278328,"score_spread":0.3062915179253609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151238122","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":[0.00063177064,0.00013101898,0.9960013,0.00088605506,0.0006548419,0.00035571074,0.000018672745,0.00007067596,0.001249946],"genre_scores_gemma":[0.04555365,0.00007953651,0.95396715,0.00028865278,0.000059857717,0.0000074405243,0.0000051655115,0.000014667119,0.000023849754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966089,0.00093764317,0.0010405692,0.0003772885,0.00071997073,0.00031562647],"domain_scores_gemma":[0.9957034,0.0003464802,0.0016194197,0.00047777465,0.001639762,0.00021316604],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015710375,0.00029729074,0.0004971764,0.00018103518,0.00012874439,0.0001368847,0.000799797,0.00016421718,0.000058674435],"category_scores_gemma":[0.0012712596,0.000279374,0.00020774324,0.0024048998,0.00009403945,0.00144994,0.00010396445,0.00016637548,0.0000033008673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010033245,0.00027235833,0.0017793087,0.0000059958834,0.000036289006,0.00002719645,0.000021548525,0.988131,0.000028613458,0.0007740325,0.000105692685,0.007814653],"study_design_scores_gemma":[0.0019355369,0.00066085655,0.0018635779,0.00007772101,0.000032704807,0.00013388103,0.000035304838,0.99417436,0.00019487602,0.00016635857,0.00048533763,0.00023950038],"about_ca_topic_score_codex":0.000008947962,"about_ca_topic_score_gemma":0.0000018877187,"teacher_disagreement_score":0.04492188,"about_ca_system_score_codex":0.00095687225,"about_ca_system_score_gemma":0.0005603045,"threshold_uncertainty_score":0.99996585},"labels":[],"label_agreement":null},{"id":"W2152762978","doi":"10.1109/cec.2007.4425018","title":"Virtual Reality High Dimensional Objective Spaces for Multi-objective Optimization: An Improved Representation","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Multi-objective optimization; Mathematical optimization; Representation (politics); Mathematics; Knapsack problem; Pareto principle; Similarity (geometry); Space (punctuation); Genetic programming; Optimization problem; Computer science; Theoretical computer science; Algorithm; Artificial intelligence; Image (mathematics)","score_opus":0.03380054065247252,"score_gpt":0.33189549783470673,"score_spread":0.2980949571822342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152762978","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":[0.00089278695,0.000013205562,0.9955209,0.00025962695,0.0008352339,0.001433485,0.00003337202,0.0005813634,0.00043001812],"genre_scores_gemma":[0.11377782,0.0000048362485,0.884488,0.00033386226,0.00019269667,0.000113496935,0.00012641624,0.0000385783,0.00092432695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969886,0.00016566813,0.00055876654,0.0012832463,0.0004419548,0.0005617957],"domain_scores_gemma":[0.9965764,0.0005240815,0.00037198598,0.00076877186,0.0014804647,0.00027826862],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008388682,0.0003570397,0.0003465226,0.0002962578,0.00048724623,0.0002064512,0.00055682944,0.00018035917,0.000038281843],"category_scores_gemma":[0.00056509057,0.0003445002,0.000121331585,0.0009437413,0.00015006718,0.0026929628,0.00024592777,0.00019192397,0.0000084226685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002553204,0.0005093258,0.00014575274,0.0000051042725,0.000071290284,0.0000074276654,0.0016012568,0.95410687,0.0012023905,0.021388182,0.00006957535,0.02063749],"study_design_scores_gemma":[0.0025100058,0.00048146464,0.00344265,0.0000073095734,0.000014254652,0.000014378184,0.00072749803,0.9763167,0.015043378,0.0009664369,0.000027051681,0.00044889687],"about_ca_topic_score_codex":0.00033071716,"about_ca_topic_score_gemma":0.00025209747,"teacher_disagreement_score":0.11288503,"about_ca_system_score_codex":0.00034869454,"about_ca_system_score_gemma":0.00017242676,"threshold_uncertainty_score":0.9999007},"labels":[],"label_agreement":null},{"id":"W2153365440","doi":"10.1155/2011/695087","title":"A Novel Ranking Method Based on Subjective Probability Theory for Evolutionary Multiobjective Optimization","year":2011,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Ranking (information retrieval); Multi-objective optimization; Pareto principle; Mathematical optimization; Set (abstract data type); Preference; Computer science; Transitive relation; Pareto optimal; Decision maker; Mathematics; Artificial intelligence; Operations research; Statistics","score_opus":0.03152620258173917,"score_gpt":0.26019325520533504,"score_spread":0.22866705262359588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153365440","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":[0.000018394234,0.00001255325,0.9966614,0.000023600689,0.00012683771,0.0018113046,0.0000073676724,0.0003483077,0.0009902264],"genre_scores_gemma":[0.07239058,5.881723e-7,0.9266212,0.000038735743,0.000019842746,0.0008689446,0.0000041951894,0.00004060912,0.000015329435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981457,0.00009957449,0.0004620218,0.0006333051,0.00025932863,0.0004000927],"domain_scores_gemma":[0.99776715,0.0013458299,0.00012633362,0.00044939882,0.00022909342,0.000082170176],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012805564,0.00028183754,0.0003226674,0.0003281429,0.000089252244,0.00003562648,0.00040709376,0.00012432263,0.000019974012],"category_scores_gemma":[0.0016163725,0.000271649,0.00010405859,0.0006403863,0.000043582684,0.000516625,0.00010367625,0.00025263132,0.000003909078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002743258,0.0004324669,0.000013811152,0.00015615525,0.00001416745,9.090323e-7,0.0014717804,0.8983091,0.00015251341,0.09841773,4.499236e-7,0.0010034582],"study_design_scores_gemma":[0.001111395,0.00010765031,0.00014545338,0.00018749732,0.0000074150407,0.000006745875,0.00002486932,0.9106779,0.00075788243,0.086693525,0.0000019306876,0.0002776966],"about_ca_topic_score_codex":0.0000040883847,"about_ca_topic_score_gemma":7.456579e-7,"teacher_disagreement_score":0.07237218,"about_ca_system_score_codex":0.0004075343,"about_ca_system_score_gemma":0.00006118866,"threshold_uncertainty_score":0.9999736},"labels":[],"label_agreement":null},{"id":"W2154275070","doi":"10.1109/tmtt.2007.902618","title":"Interpolated Coarse Models for Microwave Design Optimization With Space Mapping","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Space mapping; Interpolation (computer graphics); Surrogate model; Overhead (engineering); Computer science; Mathematical optimization; Reduction (mathematics); Parameter space; Space (punctuation); Microwave; Process (computing); Algorithm; Mathematics","score_opus":0.019762351674073093,"score_gpt":0.2545360727206209,"score_spread":0.2347737210465478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154275070","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":[0.0001467047,0.00004297613,0.9974427,0.000071237475,0.00009358862,0.0011099473,0.000010539561,0.0006824046,0.00039989044],"genre_scores_gemma":[0.19832507,0.000066485365,0.8010241,0.00020243418,0.000013506287,0.00008192449,0.0000028412246,0.000036084093,0.0002475649],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99858934,0.00015134948,0.00028390542,0.0005251926,0.00011498824,0.0003352062],"domain_scores_gemma":[0.9986359,0.00050071406,0.00014493546,0.00034934047,0.0002671017,0.00010203167],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000985045,0.00028118424,0.00022399295,0.00040502747,0.0003175109,0.00010120134,0.0002656973,0.00014540718,0.0000050477815],"category_scores_gemma":[0.000009382285,0.00025969464,0.000066290355,0.00044864754,0.00015977901,0.0007686043,0.0000054991274,0.00022560066,0.0000013360863],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011984839,0.0002897888,2.835347e-7,0.000036907724,0.00012133243,0.00001722736,0.002259082,0.44822046,0.31404796,0.030177878,0.000026859678,0.20360373],"study_design_scores_gemma":[0.00044880077,0.00030207032,1.1181834e-7,0.000080073514,0.000018529292,0.000066184766,0.00012856793,0.25059777,0.7321379,0.015904909,0.000042944605,0.00027212396],"about_ca_topic_score_codex":0.0000032438147,"about_ca_topic_score_gemma":0.0000053604035,"teacher_disagreement_score":0.41808993,"about_ca_system_score_codex":0.0001076071,"about_ca_system_score_gemma":0.00004567125,"threshold_uncertainty_score":0.9999855},"labels":[],"label_agreement":null},{"id":"W2154884452","doi":"10.1139/tcsme-2000-0024","title":"DESIGN OPTIMIZATION OF A COMPLEX MECHANICAL SYSTEM USING ADAPTIVE RESPONSE SURFACE METHOD","year":2000,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Response surface methodology; Computer science; Multi-objective optimization; Engineering optimization; Systems design; Scheme (mathematics); Optimization problem; Constraint (computer-aided design); Complex system; Mathematical optimization; Control engineering; Engineering; Mechanical engineering; Mathematics","score_opus":0.036057275624062336,"score_gpt":0.2602254478170133,"score_spread":0.22416817219295096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154884452","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":[0.00012548183,0.000016547028,0.9987313,0.00014885998,0.00015723325,0.0006541733,0.00006808182,0.00009210668,0.000006215964],"genre_scores_gemma":[0.15976627,0.0000030555068,0.8401104,0.00002971046,0.000008260442,0.000017849768,0.0000011635209,0.000026836442,0.000036439556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986461,0.00013650347,0.00038048904,0.00029402232,0.00022997611,0.00031292083],"domain_scores_gemma":[0.9986169,0.00043291113,0.000111662375,0.000427531,0.00022237634,0.00018863089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076970295,0.0001745458,0.00029457168,0.00006206047,0.00026541532,0.000022546928,0.00057775964,0.00015659293,0.000029126626],"category_scores_gemma":[0.0000473164,0.00017050805,0.0004002718,0.0007587377,0.000033233475,0.0002432481,0.000012857578,0.00017765694,4.3665472e-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":[0.000044899927,0.000015648935,1.41019845e-8,0.000025910082,0.00007907499,2.7407506e-7,0.00020126752,0.98434085,0.01122284,0.003195612,0.000003250335,0.00087037997],"study_design_scores_gemma":[0.00046259936,0.00007678324,0.0000010979375,0.000057982732,0.000052788335,0.000017817703,0.000089126486,0.9742934,0.024697624,0.000056759738,0.000038145132,0.00015588921],"about_ca_topic_score_codex":0.0015071498,"about_ca_topic_score_gemma":0.00018372406,"teacher_disagreement_score":0.15964079,"about_ca_system_score_codex":0.0007304656,"about_ca_system_score_gemma":0.00039378766,"threshold_uncertainty_score":0.69531167},"labels":[],"label_agreement":null},{"id":"W2155752967","doi":"10.1109/tmtt.2003.820904","title":"Space Mapping: The State of the Art","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1038,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Space mapping; Uniqueness; Surrogate model; Computer science; Context (archaeology); Mathematical optimization; Optimization problem; Algorithm; Wedge (geometry); State space; Mathematics; Machine learning","score_opus":0.011819821801210958,"score_gpt":0.23438356695429435,"score_spread":0.2225637451530834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155752967","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":[0.002729401,0.000042961772,0.99497443,0.0007442136,0.00011890121,0.00034119523,0.00000766113,0.00018085184,0.0008604046],"genre_scores_gemma":[0.80568606,0.00014702455,0.1924806,0.00041539824,0.000008929933,0.000040505507,1.9075223e-7,0.000013907865,0.0012073725],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921083,0.00016005796,0.00015832087,0.00021733191,0.00011489456,0.00013855251],"domain_scores_gemma":[0.99917716,0.00014952908,0.00009417915,0.00047185368,0.00007792748,0.000029378984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039249889,0.00012855849,0.00010864548,0.00008371971,0.0002550848,0.000035655445,0.00041419014,0.00003798047,0.0000050896533],"category_scores_gemma":[0.000007997265,0.00007878709,0.00007443731,0.00037382403,0.00029484284,0.00020129797,0.000008334868,0.00022485053,0.000004849957],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000780445,0.00031509012,0.0000015285337,0.000024494191,0.000083224855,0.000004729164,0.0050575244,0.020079033,0.5901547,0.123980425,0.000060220456,0.26016098],"study_design_scores_gemma":[0.0001393303,0.000049317114,0.0000083220975,0.000037485876,0.00000593059,0.00002630642,0.000049430542,0.00014901608,0.8813737,0.11745966,0.000612016,0.0000894623],"about_ca_topic_score_codex":0.0000047003955,"about_ca_topic_score_gemma":0.000007012004,"teacher_disagreement_score":0.8029567,"about_ca_system_score_codex":0.00003986942,"about_ca_system_score_gemma":0.00003934563,"threshold_uncertainty_score":0.32128444},"labels":[],"label_agreement":null},{"id":"W2156410580","doi":"10.20381/ruor-4435","title":"New Multi-Objective Optimization Techniques and Their Application to Complex Chemical Engineering Problems","year":2011,"lang":"en","type":"dissertation","venue":"Library and Archives Canada (Government of Canada)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sorting; Pareto principle; Multi-objective optimization; Mathematical optimization; Genetic algorithm; Computation; Computer science; Domain (mathematical analysis); Algorithm; Evolutionary algorithm; Test functions for optimization; Population; Optimization problem; Mathematics; Multi-swarm optimization","score_opus":0.004694590886637776,"score_gpt":0.16741519583060122,"score_spread":0.16272060494396345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156410580","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":[0.00017361526,0.00013564155,0.9894418,0.00019031609,0.00010838389,0.00068556186,0.000068921385,0.00006879145,0.009126927],"genre_scores_gemma":[0.03373153,0.00012112051,0.9623554,0.00021054932,0.000060982024,0.00009930876,0.00011895113,0.000055482797,0.0032466757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984091,0.000022792903,0.00029709705,0.00049819273,0.000543211,0.00022960821],"domain_scores_gemma":[0.99913967,0.00008707689,0.0002331617,0.00025526865,0.0000040614636,0.00028074603],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000089701525,0.0003082745,0.00029948002,0.00006819847,0.00009515029,0.000040713338,0.00037848155,0.00006632511,0.000004844775],"category_scores_gemma":[0.000005169815,0.00031106162,0.000021937709,0.0001798955,0.000017616116,0.000501271,0.0001638929,0.00016377639,1.907841e-9],"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":[0.0005774452,0.00017991987,0.0005250053,0.0011214573,0.0003668703,0.0000355815,0.0051462227,0.11217769,0.19400476,0.09224952,0.00035272638,0.5932628],"study_design_scores_gemma":[0.00036597002,0.00006627957,0.0016100721,0.00020946933,0.00001276746,0.000010031127,0.00051333127,0.63908,0.35459384,0.00041798205,0.0025290342,0.000591207],"about_ca_topic_score_codex":0.0013119454,"about_ca_topic_score_gemma":0.002658599,"teacher_disagreement_score":0.5926716,"about_ca_system_score_codex":0.000018632067,"about_ca_system_score_gemma":0.00073617534,"threshold_uncertainty_score":0.99993414},"labels":[],"label_agreement":null},{"id":"W2157862026","doi":"10.1109/cefc-06.2006.1633022","title":"Dealing with Model Errors in Approximation Model-Based Optimization","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Benchmark (surveying); Computer science; Electromagnetics; Process (computing); Code (set theory); Data modeling; Bayesian optimization; Robust optimization; Mathematical optimization; Machine learning; Mathematics; Engineering","score_opus":0.011605774514861222,"score_gpt":0.23568275551314316,"score_spread":0.22407698099828194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157862026","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":[0.00040658697,0.0000065322774,0.99438995,0.0001985861,0.000019045758,0.00029046938,0.0000010456625,0.0002756516,0.0044121165],"genre_scores_gemma":[0.24640456,9.75212e-7,0.7532451,0.00012010367,0.000006034971,0.000030282594,0.000014725522,0.000014322276,0.00016387117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988762,0.00002201042,0.00024360736,0.00039557097,0.00024790334,0.00021467023],"domain_scores_gemma":[0.99935615,0.00002339107,0.00010827882,0.0002965574,0.00017538836,0.000040227806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012506063,0.00015039979,0.00012460821,0.000243334,0.00007995975,0.00006964623,0.00024888327,0.0000553878,0.000004515489],"category_scores_gemma":[0.00001255291,0.00013606068,0.000021944392,0.0005557436,0.000028513312,0.0009436786,0.000038629303,0.00008350771,0.0000027945935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000673794,0.00005679525,0.00006534399,0.0000036052654,9.704552e-7,0.000001313927,0.000052311283,0.96846294,0.000045032022,0.030600514,0.0000030428043,0.00070137944],"study_design_scores_gemma":[0.0007791857,0.000016637088,0.000022268856,0.0000123650125,0.0000018720671,0.0000014575481,0.000008627867,0.9948122,0.00113451,0.0030175794,8.1414754e-7,0.00019247497],"about_ca_topic_score_codex":0.000034849083,"about_ca_topic_score_gemma":0.000052901,"teacher_disagreement_score":0.24599797,"about_ca_system_score_codex":0.00012818468,"about_ca_system_score_gemma":0.00009550033,"threshold_uncertainty_score":0.5548393},"labels":[],"label_agreement":null},{"id":"W2158942047","doi":"10.1109/icit.2004.1490788","title":"Multiobjective genetic estimation of DC motor parameters and load torque","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Robustness (evolution); Control theory (sociology); Genetic algorithm; Sorting; Torque; DC motor; Computer science; Multi-objective optimization; Engineering; Algorithm; Artificial intelligence; Machine learning; Physics","score_opus":0.008715653474568326,"score_gpt":0.24905201107606506,"score_spread":0.24033635760149674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158942047","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":[0.025405966,0.00007040168,0.97334194,0.00012995639,0.000069100046,0.00026880144,0.0000021515473,0.000096261436,0.000615409],"genre_scores_gemma":[0.346691,0.000013158363,0.6530066,0.00008357731,0.000009472505,0.000013297434,4.522681e-7,0.0000050208387,0.00017738708],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991315,0.000036426693,0.00020222651,0.0003008976,0.00018997546,0.00013894861],"domain_scores_gemma":[0.9992833,0.0001057103,0.00010431852,0.00025354588,0.00018641523,0.00006670244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008019959,0.00011038992,0.00012872343,0.00008546379,0.00004846141,0.000034655197,0.00018351655,0.000042347045,0.000011777545],"category_scores_gemma":[0.0001295393,0.00010423615,0.000028007411,0.0002002195,0.00007148477,0.0005984046,0.000092523616,0.000053901556,0.000014416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009846441,0.00008575322,0.00019633227,0.000008966122,0.00001832503,0.0000015045788,0.0012343607,0.6362906,0.001866092,0.0019798847,0.000019846937,0.3582885],"study_design_scores_gemma":[0.0004518084,0.000071126786,0.0069895126,0.0000068384516,0.0000035512508,0.000008931093,0.000029923141,0.97280407,0.019031022,0.000440626,0.00004288689,0.00011969205],"about_ca_topic_score_codex":0.00004655192,"about_ca_topic_score_gemma":0.000014318184,"teacher_disagreement_score":0.3581688,"about_ca_system_score_codex":0.00011987493,"about_ca_system_score_gemma":0.000052500927,"threshold_uncertainty_score":0.42506266},"labels":[],"label_agreement":null},{"id":"W2160331134","doi":"10.3390/s150717572","title":"Wireless Sensor Network Optimization: Multi-Objective Paradigm","year":2015,"lang":"en","type":"review","venue":"Sensors","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":143,"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":"Wireless sensor network; Optimization problem; Computer science; Software deployment; Key distribution in wireless sensor networks; Wireless network; Wireless; Distributed computing; Mathematical optimization; Computer network; Telecommunications; Mathematics","score_opus":0.05381203925768556,"score_gpt":0.33002241792906734,"score_spread":0.27621037867138176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160331134","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.2407492e-8,0.47267583,0.5239341,0.00002538432,0.0011549639,0.0010369444,0.000029868886,0.00048775546,0.00065509323],"genre_scores_gemma":[1.15750325e-7,0.5690269,0.42870763,0.000053794865,0.00045527637,0.00010348814,0.00008149396,0.000120716955,0.0014506009],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99443775,0.00088597555,0.0011138112,0.0017905936,0.0007346869,0.00103715],"domain_scores_gemma":[0.9955641,0.00043781975,0.0010946025,0.0017730439,0.0006297685,0.00050066016],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005633479,0.001153493,0.0025327934,0.00041009655,0.00034812075,0.0003096892,0.0016406814,0.00066988316,0.000030745923],"category_scores_gemma":[0.00018979285,0.00105553,0.0005880664,0.0025875487,0.00019630782,0.0005900934,0.0006132393,0.00087782415,0.00044853866],"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":[0.0000027627327,0.000073163166,4.113003e-7,0.00045487488,0.00015461014,0.00010565546,0.00020483378,0.62576777,6.752546e-9,0.000934087,0.00036818322,0.3719336],"study_design_scores_gemma":[0.0004321142,0.00004216462,3.3229625e-7,0.0012502933,0.00014159839,0.00020264006,0.000022212394,0.47745565,4.2280678e-7,0.00007855416,0.5194396,0.00093441986],"about_ca_topic_score_codex":0.0000115385865,"about_ca_topic_score_gemma":0.0000044385197,"teacher_disagreement_score":0.5190714,"about_ca_system_score_codex":0.0007632491,"about_ca_system_score_gemma":0.00080678205,"threshold_uncertainty_score":0.9991895},"labels":[],"label_agreement":null},{"id":"W2161762394","doi":"10.1109/cec.2006.1688478","title":"Virtual Reality Spaces for Visual Data Mining with Multiobjective Evolutionary Optimization: Implicit and Explicit Function Representations Mixing Unsupervised and Supervised Properties","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Mixing (physics); Computer science; Function (biology); Virtual reality; Artificial intelligence; Evolutionary algorithm; Machine learning; Theoretical computer science","score_opus":0.0383133552738841,"score_gpt":0.284566148260437,"score_spread":0.24625279298655292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161762394","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":[0.012220515,0.00018297686,0.98534966,0.00054402614,0.00008397708,0.0010365135,0.00003836302,0.0002974243,0.00024657752],"genre_scores_gemma":[0.3359277,0.000027216067,0.6631056,0.000096149946,0.000103493396,0.0001852713,0.00024309578,0.00002791341,0.00028354098],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978537,0.00010488432,0.0003511094,0.001099568,0.00028042504,0.00031031345],"domain_scores_gemma":[0.9983716,0.00028210218,0.00014468584,0.00059855933,0.00050286256,0.000100172656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022563522,0.00027152043,0.00025277975,0.0001919065,0.0006735793,0.0003256453,0.00031463307,0.00008077248,0.000007960301],"category_scores_gemma":[0.00012749904,0.00023245552,0.000024277711,0.00050512777,0.00015221162,0.003091496,0.00043537508,0.00008712317,6.28438e-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":[0.00039817675,0.00030049987,0.005041255,0.000058083948,0.00011957092,0.000004796199,0.0019494042,0.96970284,0.0021812574,0.008245421,0.00035998962,0.011638684],"study_design_scores_gemma":[0.0019428476,0.00028995852,0.0074261,0.000032490323,0.000033885943,0.000036869933,0.0024819784,0.98677593,0.0004310947,0.00017043436,0.000045067252,0.000333359],"about_ca_topic_score_codex":0.00037231177,"about_ca_topic_score_gemma":0.00012910849,"teacher_disagreement_score":0.32370716,"about_ca_system_score_codex":0.00007442681,"about_ca_system_score_gemma":0.00010470602,"threshold_uncertainty_score":0.9479261},"labels":[],"label_agreement":null},{"id":"W2162219390","doi":"10.1109/cca.2009.5280821","title":"Multi-objective optimal gating and riser design for metal-casting","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Flexibility (engineering); Casting; Computer science; Multi-objective optimization; Gating; Process (computing); Key (lock); Evolutionary algorithm; Mathematical optimization; Optimal design; Engineering design process; Engineering; Mechanical engineering; Materials science; Mathematics; Machine learning","score_opus":0.0425162078338612,"score_gpt":0.30099854005851506,"score_spread":0.2584823322246539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162219390","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":[0.0003842484,0.00007351602,0.9978718,0.00017071229,0.000097306474,0.00065931195,0.0000017501624,0.00029160434,0.00044971908],"genre_scores_gemma":[0.0849805,0.0000051734273,0.9143445,0.00029539524,0.00004031532,0.000039708735,0.0000011622924,0.00001341589,0.00027984503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861634,0.00006847107,0.00024164781,0.0005747762,0.0001476191,0.00035112465],"domain_scores_gemma":[0.99892944,0.00038468634,0.00013198289,0.00024189928,0.00020101653,0.000110989575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003078838,0.00020110923,0.00020851589,0.00011495462,0.0002932688,0.00014619017,0.00027554305,0.00005687314,0.000004848779],"category_scores_gemma":[0.0004360651,0.00018450558,0.000055100525,0.00031856674,0.00003971278,0.0010070199,0.000105263236,0.00010663911,0.0000043769446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044200213,0.00024211236,0.00007199348,0.000012611498,0.00006725523,0.000014040244,0.0029804616,0.7168834,0.028146122,0.007762772,0.000053024396,0.24372199],"study_design_scores_gemma":[0.0010239213,0.00019436842,0.00065372186,0.00001045293,0.000008581741,0.000021783055,0.00013804443,0.9736296,0.023487475,0.0005650504,0.000023342145,0.00024364662],"about_ca_topic_score_codex":0.000003924585,"about_ca_topic_score_gemma":5.8533044e-7,"teacher_disagreement_score":0.2567462,"about_ca_system_score_codex":0.000042189607,"about_ca_system_score_gemma":0.000040826428,"threshold_uncertainty_score":0.75239193},"labels":[],"label_agreement":null},{"id":"W2164040928","doi":"10.11575/prism/24742","title":"A Genetic Algorithm Optimizer with Applications to the SAGD Process","year":2013,"lang":"en","type":"dissertation","venue":"PRISM (University of Calgary)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Calgary","keywords":"Genetic algorithm; Process (computing); Computer science; Algorithm; Machine learning; Programming language","score_opus":0.006435286983608043,"score_gpt":0.21937165554143143,"score_spread":0.21293636855782339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164040928","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":[0.00018919405,0.00014302657,0.992968,0.00027258234,0.000101512676,0.001636988,0.0000015942071,0.00012489529,0.004562158],"genre_scores_gemma":[0.00010524649,0.00007274961,0.98119056,0.000082322884,0.00002041801,0.000049575498,0.000058547485,0.000028993443,0.018391585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842083,0.000049500413,0.00016471921,0.00063072785,0.000466994,0.000267203],"domain_scores_gemma":[0.9980599,0.000052235973,0.00033318356,0.00077424495,0.000616161,0.00016427056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006830411,0.00026708556,0.00030472188,0.00024723465,0.00034850568,0.00005899046,0.0016797966,0.00016250898,0.00006560724],"category_scores_gemma":[0.00001107777,0.00024140369,0.00008489173,0.00075587555,0.000080596714,0.0003994722,0.00014753976,0.00028680405,0.00012578754],"study_design_candidate":"design_other","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":[0.000031910593,0.00013524185,0.000009550382,0.00007213867,0.00011446467,0.000016115999,0.006541889,0.0039335773,0.000016679638,0.0011593759,0.0005566651,0.9874124],"study_design_scores_gemma":[0.0008273538,0.00014702999,0.0029973343,0.00009454187,0.000106227024,0.0000134577895,0.00075038907,0.9827885,0.00017318298,0.000990809,0.010470795,0.00064038625],"about_ca_topic_score_codex":0.00016445201,"about_ca_topic_score_gemma":0.000021870426,"teacher_disagreement_score":0.986772,"about_ca_system_score_codex":0.00008540132,"about_ca_system_score_gemma":0.0002656523,"threshold_uncertainty_score":0.98441565},"labels":[],"label_agreement":null},{"id":"W2164616507","doi":"10.1109/tmag.2008.2002779","title":"Sequential Optimization Method for the Design of Electromagnetic Device","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bayesian optimization; Computer science; Test functions for optimization; Kriging; Mathematical optimization; Sequential analysis; Process (computing); Optimization problem; Continuous optimization; Finite element method; Design of experiments; Algorithm; Optimal design; Multi-swarm optimization; Mathematics; Artificial intelligence; Machine learning","score_opus":0.04263268396322485,"score_gpt":0.295755138234833,"score_spread":0.2531224542716082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164616507","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":[0.00001052508,0.00008179855,0.99821424,0.00027587547,0.00041036,0.0008261528,0.0000113766655,0.000111096415,0.00005857849],"genre_scores_gemma":[0.015026852,0.00030214599,0.98374444,0.00017126142,0.00002776046,0.00014280694,0.000001288231,0.000024627108,0.00055879186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986938,0.0001627476,0.00030465968,0.00032740427,0.00026475856,0.0002466218],"domain_scores_gemma":[0.9981326,0.00084024586,0.00013287851,0.0004770868,0.00035994328,0.000057235124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022499361,0.00017271086,0.0001704788,0.00014329265,0.00036280925,0.000029495497,0.0005093415,0.00007743669,0.000048671107],"category_scores_gemma":[0.000025883419,0.00014803866,0.000100398756,0.0006316726,0.00010102286,0.00021437809,0.0000029746657,0.00015310146,0.000005333965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002808092,0.00010389075,1.7524818e-7,0.000006032241,0.000020876612,0.0000012633371,0.00024522629,0.9692278,0.0029631988,0.00018700976,0.000032103697,0.02718436],"study_design_scores_gemma":[0.0006641551,0.0006425808,0.00000940748,0.0000049612668,0.000044940054,0.000043712833,0.000013705457,0.94047993,0.05767422,0.00013746078,0.00013729616,0.00014763247],"about_ca_topic_score_codex":0.000009463707,"about_ca_topic_score_gemma":0.000002952824,"teacher_disagreement_score":0.05471102,"about_ca_system_score_codex":0.000053481075,"about_ca_system_score_gemma":0.00012984913,"threshold_uncertainty_score":0.60368407},"labels":[],"label_agreement":null},{"id":"W2164634985","doi":"10.1504/ijmmno.2012.044711","title":"GATE: a genetic algorithm designed for expensive cost functions","year":2012,"lang":"en","type":"article","venue":"International Journal of Mathematical Modelling and Numerical Optimisation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Polytechnique Montréal","funders":"","keywords":"Convergence (economics); Computer science; Algorithm; Heuristic; Genetic algorithm; Local search (optimization); Mathematical optimization; Gaussian; Function (biology); Mathematics","score_opus":0.03710974873704519,"score_gpt":0.2977458013577234,"score_spread":0.2606360526206782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164634985","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":[0.00048570608,0.00016507789,0.99791,0.00057974877,0.0005117307,0.00023519542,0.0000049866594,0.000030915988,0.0000766103],"genre_scores_gemma":[0.10468886,0.000053471478,0.89471835,0.0001402201,0.0002915977,0.000027717759,0.0000037024097,0.000014415441,0.00006166589],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985886,0.00005710277,0.0005383814,0.0001657629,0.00043276304,0.0002173842],"domain_scores_gemma":[0.99783397,0.0005530322,0.00037028984,0.000115808965,0.00091632275,0.0002105801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035830858,0.00014357512,0.0002301211,0.00016231838,0.0000920816,0.00011375977,0.00030188254,0.000066134446,0.000019989922],"category_scores_gemma":[0.00025762815,0.00012297371,0.00011080006,0.00011945717,0.000046779627,0.00084017386,0.00005862972,0.00013049338,0.0000109153925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006045552,0.00034941928,0.000015232505,0.000010118221,0.00011441278,0.000005458366,0.0011554308,0.86950916,0.00012383926,0.007635442,0.00008334741,0.12093769],"study_design_scores_gemma":[0.000706884,0.00010950174,0.000022933385,0.000036685287,0.000025256222,0.00026059954,0.000092699156,0.9859768,0.0004565857,0.011815056,0.00036171504,0.00013526475],"about_ca_topic_score_codex":0.0000011457281,"about_ca_topic_score_gemma":1.11070495e-8,"teacher_disagreement_score":0.120802425,"about_ca_system_score_codex":0.0001090341,"about_ca_system_score_gemma":0.00004340904,"threshold_uncertainty_score":0.50147223},"labels":[],"label_agreement":null},{"id":"W2165622730","doi":"10.1287/opre.1080.0685","title":"Percentile Optimization for Markov Decision Processes with Parameter Uncertainty","year":2009,"lang":"en","type":"article","venue":"Operations Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":228,"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; HEC Montréal","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Ambiguity; Computer science; Markov decision process; Mathematical optimization; Parametric statistics; Set (abstract data type); Percentile; Markov chain; Markov process; Process (computing); Decision process; Operations research; Machine learning; Mathematics; Management science; Statistics; Economics","score_opus":0.04191846451408229,"score_gpt":0.3783378437581784,"score_spread":0.33641937924409615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165622730","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":[0.0011680854,0.000047722086,0.99586487,0.0010694065,0.000050200302,0.0011235186,0.00000992358,0.00010248052,0.00056376564],"genre_scores_gemma":[0.08008291,0.000085255226,0.9183091,0.00014344086,0.000046717323,0.0002713251,0.000046595553,0.000014017323,0.0010006466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813014,0.000108311,0.00021124812,0.00055817154,0.0005900671,0.00040208298],"domain_scores_gemma":[0.9961469,0.0005941756,0.000022053406,0.00051838165,0.0026018845,0.00011661696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050800084,0.00013433452,0.00013150171,0.0003115366,0.0008150827,0.000637746,0.00057920255,0.00006263731,0.000069740294],"category_scores_gemma":[0.0015251641,0.00010683286,0.000028090733,0.00156458,0.000081479826,0.0012893765,0.00008182591,0.00017005316,0.00001844263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043447573,0.00013422982,0.000009844848,0.000008037128,0.00000492838,0.0000018860471,0.00027604753,0.94743514,0.00006982444,0.0016828624,0.00024653054,0.05008722],"study_design_scores_gemma":[0.0006610031,0.00039389185,0.00011236461,0.000037988648,0.0000022507954,0.000008856593,0.00008518197,0.996733,0.00057171803,0.00051726744,0.0007244789,0.00015196243],"about_ca_topic_score_codex":0.000013811141,"about_ca_topic_score_gemma":0.00009712534,"teacher_disagreement_score":0.07891482,"about_ca_system_score_codex":0.00015130555,"about_ca_system_score_gemma":0.00050571666,"threshold_uncertainty_score":0.62690395},"labels":[],"label_agreement":null},{"id":"W2166062557","doi":"10.1109/tmag.2006.871954","title":"Selection of approximation models for electromagnetic device optimization","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Selection (genetic algorithm); Mathematical optimization; High frequency approximation; Approximation algorithm; Model selection; Approximation theory; Linear approximation; Algorithm; Artificial intelligence; Mathematics; Physics","score_opus":0.013548640877156808,"score_gpt":0.23927083241886593,"score_spread":0.2257221915417091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166062557","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":[0.00026360352,0.000024161849,0.99787486,0.00010581713,0.00023397393,0.0007145204,0.000015542617,0.00021504544,0.00055249024],"genre_scores_gemma":[0.22868033,0.000028587718,0.77061117,0.00003961424,0.000028133207,0.00012700963,0.000009394763,0.000023808636,0.0004519467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864894,0.000054524106,0.00039542175,0.00038422312,0.00026402058,0.00025286482],"domain_scores_gemma":[0.9988564,0.00012657346,0.0001852147,0.0002837395,0.0005026298,0.000045446523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000114246606,0.00018705666,0.0001795647,0.0002915375,0.0001756337,0.00005500046,0.0002539383,0.00010745542,0.000023489325],"category_scores_gemma":[0.000007128306,0.00021288419,0.000090368136,0.000810855,0.000043406282,0.00058073155,0.0000017018189,0.00011862838,0.0000034493835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019626787,0.00022860397,7.105782e-7,0.000021779208,0.000007160337,1.4365982e-7,0.00005169371,0.97631276,0.0044055716,0.0027240957,0.000020039564,0.016207825],"study_design_scores_gemma":[0.0007302474,0.000524585,0.000015272115,0.00001030287,0.000027547787,0.000007072559,0.000007509567,0.91011715,0.08531556,0.0030268875,0.00003342504,0.00018444915],"about_ca_topic_score_codex":0.00001639847,"about_ca_topic_score_gemma":0.000019845482,"teacher_disagreement_score":0.22841673,"about_ca_system_score_codex":0.00010399312,"about_ca_system_score_gemma":0.000067404595,"threshold_uncertainty_score":0.86811656},"labels":[],"label_agreement":null},{"id":"W2166674691","doi":"10.1109/tsmcc.2007.900651","title":"Optimal Advertising Campaign Generation for Multiple Brands Using MOGA","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Greedy algorithm; Mathematical optimization; Heuristic; Genetic algorithm; Computer science; Variety (cybernetics); Encoding (memory); Pareto optimal; Set (abstract data type); Pareto principle; Optimization problem; Multi-objective optimization; Key (lock); Mathematics; Artificial intelligence","score_opus":0.03964088752379955,"score_gpt":0.29618822251481386,"score_spread":0.2565473349910143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166674691","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":[0.0014820013,0.0029704887,0.99319726,0.000035747806,0.00031676303,0.0018219675,0.000023202592,0.000076228156,0.000076315104],"genre_scores_gemma":[0.4895667,0.0057685636,0.50225973,0.00016799566,0.0003044168,0.00089723116,0.000016939677,0.000046172507,0.00097225513],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850714,0.00006065236,0.0005311278,0.000510823,0.00013911353,0.00025117074],"domain_scores_gemma":[0.99897367,0.00012769479,0.00019458831,0.0003871888,0.0001577509,0.00015912483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000505291,0.00021205208,0.00029377674,0.00014429125,0.00059028744,0.00015945591,0.0001464556,0.00009705738,0.000002454445],"category_scores_gemma":[0.0000064498067,0.00019585981,0.00007566232,0.00031298533,0.000071270675,0.00025200433,0.000004506417,0.000112001006,0.00000538085],"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":[0.00003829784,0.0004906736,0.0000776511,0.0004680369,0.00011386228,0.0000026183975,0.0013883772,0.37767318,0.012976073,0.007624625,0.000247323,0.5988993],"study_design_scores_gemma":[0.00073671056,0.00007457729,0.000028913564,0.00010752371,0.000050448005,0.000055450568,0.00008412928,0.9379947,0.0023877285,0.000024807643,0.058150053,0.0003049426],"about_ca_topic_score_codex":0.000016907166,"about_ca_topic_score_gemma":0.000022301852,"teacher_disagreement_score":0.59859437,"about_ca_system_score_codex":0.00006212857,"about_ca_system_score_gemma":0.000024100102,"threshold_uncertainty_score":0.7986931},"labels":[],"label_agreement":null},{"id":"W2168016899","doi":"10.1109/ijcnn.2007.4371305","title":"Multi-objective Evolutionary Optimization of Neural Networks for Virtual Reality Visual Data Mining: Application to Hydrochemistry","year":2007,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Artificial neural network; Computer science; Visualization; Genetic algorithm; Data mining; Virtual reality; Set (abstract data type); Artificial intelligence; Domain (mathematical analysis); Multi-objective optimization; Data set; Similarity (geometry); Evolutionary computation; Pattern recognition (psychology); Machine learning; Image (mathematics); Mathematics","score_opus":0.07781558349810247,"score_gpt":0.3645663362506648,"score_spread":0.28675075275256234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168016899","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":[0.0041440073,0.000041219315,0.98078424,0.0023311311,0.00895157,0.0018705367,0.0003045566,0.0004441669,0.0011285559],"genre_scores_gemma":[0.9516833,0.00014529,0.0409699,0.0017840094,0.0024861298,0.0003264316,0.0020610616,0.00012878062,0.00041512193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99125457,0.0003340501,0.0021684503,0.0029999348,0.001866211,0.0013767843],"domain_scores_gemma":[0.99095976,0.0012872027,0.0017388947,0.0017643744,0.0035647869,0.000684958],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0011540987,0.0012311329,0.0009862441,0.0007537467,0.00050181465,0.000577666,0.0058132103,0.00066777907,0.000117484415],"category_scores_gemma":[0.0005105819,0.0013147304,0.0003893308,0.0011886319,0.00043419664,0.0021249636,0.0009668347,0.0013710202,0.000010659591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001617772,0.000659633,0.00025656636,0.000011111172,0.00021502396,0.000030005076,0.00011834381,0.9586247,0.00079373736,0.0068698074,0.0010777392,0.029725546],"study_design_scores_gemma":[0.0023181543,0.0008521526,0.0007331664,0.00018209839,0.000045292774,0.0000665397,0.00017066477,0.99370563,0.00047275325,0.00019406194,0.00009418612,0.0011653175],"about_ca_topic_score_codex":0.00007789848,"about_ca_topic_score_gemma":0.00012969064,"teacher_disagreement_score":0.94753927,"about_ca_system_score_codex":0.0007343847,"about_ca_system_score_gemma":0.00020884015,"threshold_uncertainty_score":0.99956584},"labels":[],"label_agreement":null},{"id":"W2168984786","doi":"10.1109/cec.2007.4425019","title":"Visualizing High Dimensional Objective Spaces for Multi-objective Optimization: A Virtual Reality Approach","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV","keywords":"Embedding; Multi-objective optimization; Mathematical optimization; Virtual reality; Knapsack problem; Computer science; Pareto principle; Optimization problem; Mathematics; High dimensional; Theoretical computer science; Artificial intelligence","score_opus":0.03784962194572387,"score_gpt":0.3238311413234767,"score_spread":0.2859815193777528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168984786","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":[0.00030039725,0.000038406783,0.9950408,0.00013088669,0.0006176601,0.0013547114,0.000027830416,0.00065154146,0.0018378212],"genre_scores_gemma":[0.08415325,0.0000054165507,0.91398245,0.000408662,0.00018111784,0.000134884,0.00006652692,0.0000486032,0.0010190854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99660134,0.00015252782,0.0005869493,0.0013269522,0.00059078634,0.0007414591],"domain_scores_gemma":[0.9969713,0.0006320124,0.00034716981,0.0006273216,0.0011485846,0.0002736219],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011269061,0.00044636874,0.00045000322,0.0003685139,0.00058465084,0.00021051039,0.00066097366,0.00020641566,0.000024997897],"category_scores_gemma":[0.00039151235,0.00041961425,0.00017035096,0.0011874805,0.00018166186,0.0015118823,0.00040458495,0.0002472764,0.000011744525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000109862354,0.00054413365,0.00010612511,0.000011756583,0.00009128732,0.0000074365853,0.0020216675,0.9075124,0.0001380978,0.08393808,0.000108779765,0.0054103467],"study_design_scores_gemma":[0.0022651616,0.00024742063,0.0011439475,0.000014283911,0.00001661687,0.00002869189,0.0009625134,0.99099404,0.0031155548,0.00056574645,0.00008056263,0.00056547305],"about_ca_topic_score_codex":0.00013352932,"about_ca_topic_score_gemma":0.00005022367,"teacher_disagreement_score":0.08385285,"about_ca_system_score_codex":0.0004267336,"about_ca_system_score_gemma":0.00019118865,"threshold_uncertainty_score":0.9998256},"labels":[],"label_agreement":null},{"id":"W2169003314","doi":"10.5555/1921427.1921443","title":"A Bayesian interactive optimization approach to procedural animation design","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":133,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Animation; Bayesian optimization; Variety (cybernetics); Artificial intelligence; Human–computer interaction; Task (project management); Domain (mathematical analysis); Domain knowledge; Machine learning; Bayesian probability; Graphics; Computer graphics; Computer graphics (images)","score_opus":0.014259424015521412,"score_gpt":0.26299789057719214,"score_spread":0.24873846656167073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169003314","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":[0.000021646747,9.5349543e-7,0.9855209,0.00045268316,0.00025730566,0.0007630255,7.3625074e-7,0.0003932295,0.012589517],"genre_scores_gemma":[0.05781023,5.893736e-7,0.9409344,0.0005627746,0.00004889131,0.00012599047,0.0000065866925,0.000016981243,0.0004935138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878275,0.000056941753,0.00020776794,0.00050102756,0.00021988331,0.0002316468],"domain_scores_gemma":[0.9989695,0.00006829749,0.00009858661,0.0003639856,0.00035086958,0.00014877343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018223505,0.00016405372,0.00012557844,0.00021204709,0.00013114675,0.00021157903,0.00049516384,0.000067389876,0.00003804089],"category_scores_gemma":[0.00026315713,0.00015077392,0.00003108285,0.0007594773,0.000023353918,0.0017495438,0.00014262402,0.00018696609,0.00003416033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011540923,0.00011695913,0.000007613809,0.000003249692,0.0000064535793,6.402847e-7,0.0010931077,0.97956127,0.0014146309,0.010027352,0.00018964102,0.0075675375],"study_design_scores_gemma":[0.0002574463,0.000059133425,0.000071623006,0.000003769976,0.0000021866692,0.000025383693,0.000073847936,0.99401003,0.00490064,0.00032687988,0.000066727174,0.00020233491],"about_ca_topic_score_codex":0.0000058610217,"about_ca_topic_score_gemma":0.0000028777001,"teacher_disagreement_score":0.057788584,"about_ca_system_score_codex":0.00006790742,"about_ca_system_score_gemma":0.000075330114,"threshold_uncertainty_score":0.61483824},"labels":[],"label_agreement":null},{"id":"W2169196235","doi":"10.1109/ccece.2005.1557046","title":"Multi-objective optimization for process control of the in-situ bioremediation system","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Normalization (sociology); Computer science; Bioremediation; Process (computing); Set (abstract data type); Controller (irrigation); Control system; Optimal control; Process control; Multi-objective optimization; Decision maker; Mathematical optimization; Control engineering; Engineering; Machine learning; Mathematics; Operations research","score_opus":0.007996387483837028,"score_gpt":0.24373814782665326,"score_spread":0.23574176034281624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169196235","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":[0.0011568374,0.000022734479,0.99645793,0.00011354843,0.00027253063,0.0013476047,0.0000074575987,0.0001125374,0.0005087855],"genre_scores_gemma":[0.6415944,6.383397e-7,0.3581749,0.000028365308,0.000025408832,0.00012087937,0.000003654413,0.000008002645,0.0000437852],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988664,0.00006774658,0.00035594316,0.00031509117,0.00021314611,0.00018172074],"domain_scores_gemma":[0.99870145,0.00012892342,0.00029292304,0.00027691823,0.0005777803,0.000022021277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020853146,0.00012510848,0.00017795523,0.00013972595,0.00008487018,0.00003168666,0.00040179913,0.00006592388,0.0000013985124],"category_scores_gemma":[0.00011937505,0.00009238718,0.000059257225,0.00071733946,0.000043771026,0.0005110159,0.00004087086,0.000057162713,0.0000010780125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010888363,0.00009391618,0.0011498897,0.0000340602,0.000005449396,2.2858983e-7,0.00019370922,0.99043113,0.0010235793,0.00606744,0.000004352806,0.0009853794],"study_design_scores_gemma":[0.002137284,0.000025390675,0.005832779,0.000026748612,0.000005455663,0.0000017585858,0.00014081641,0.9756123,0.015989052,0.00011677645,0.0000035257824,0.00010814776],"about_ca_topic_score_codex":0.00003378648,"about_ca_topic_score_gemma":0.00008026522,"teacher_disagreement_score":0.64043754,"about_ca_system_score_codex":0.00017114011,"about_ca_system_score_gemma":0.000088335844,"threshold_uncertainty_score":0.37674397},"labels":[],"label_agreement":null},{"id":"W2170491249","doi":"10.1115/detc2013-12352","title":"Accounting for Test Variability Through Sizing Local Domains in Sequential Design Optimization With Concurrent Calibration-Based Model Validation","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Sizing; Domain (mathematical analysis); Calibration; Sequential analysis; Design of experiments; Mathematical optimization; Mathematics; Statistics","score_opus":0.02942634739263835,"score_gpt":0.2735618950461124,"score_spread":0.24413554765347403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170491249","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":[0.00013104506,0.000002532962,0.9973439,0.00028674808,0.00009924635,0.0017791222,0.0000058689275,0.00022934492,0.00012221302],"genre_scores_gemma":[0.37984005,8.1078315e-7,0.6196179,0.00016657427,0.0000191322,0.00029399648,0.000033469933,0.000015285164,0.0000128394495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982213,0.00013152683,0.0004253593,0.00061294687,0.0002865313,0.00032238243],"domain_scores_gemma":[0.99798596,0.0008512664,0.00021446004,0.00033285,0.000554357,0.00006107866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046259927,0.00021617321,0.00019613565,0.00010337009,0.00018973085,0.0003093343,0.0003122679,0.000090288355,0.00002672782],"category_scores_gemma":[0.00034155042,0.00019293214,0.00003771036,0.000571393,0.000082255276,0.002709013,0.00006449056,0.000108690365,0.0000027664369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007376482,0.00013508991,0.00036637156,0.000013194052,0.0000042696142,3.17569e-7,0.0001868572,0.9916757,0.00017269528,0.004707187,0.000008732934,0.0027222114],"study_design_scores_gemma":[0.0015297038,0.000078185105,0.000039527094,0.000021542566,0.0000065019062,0.0000011967452,0.00003900172,0.9897554,0.005932251,0.0023357966,0.0000020917846,0.00025882514],"about_ca_topic_score_codex":0.00008109163,"about_ca_topic_score_gemma":0.000023829594,"teacher_disagreement_score":0.379709,"about_ca_system_score_codex":0.00032116365,"about_ca_system_score_gemma":0.00035112572,"threshold_uncertainty_score":0.7867545},"labels":[],"label_agreement":null},{"id":"W2178163879","doi":"10.1007/s10479-015-2008-0","title":"On the use of the $$L_{p}$$ L p distance in reference point-based approaches for multiobjective optimization","year":2015,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Theory of computation; Multi-objective optimization; Point (geometry); Mathematical optimization; Mathematics; Computer science; Algorithm; Geometry","score_opus":0.6088702347949255,"score_gpt":0.4441123919419863,"score_spread":0.1647578428529392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2178163879","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":[0.0022457675,0.000027466333,0.9911208,0.0049065812,0.000041136725,0.0012499846,0.000043711607,0.000012941113,0.00035160518],"genre_scores_gemma":[0.7094341,0.000014407052,0.2896981,0.00019566095,0.000010666402,0.000353705,0.0000118306925,0.000011610488,0.00026994527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795574,0.00061025587,0.00030416204,0.00031142935,0.000580465,0.00023797018],"domain_scores_gemma":[0.9955593,0.0012083579,0.00007056832,0.0007356977,0.0023736202,0.000052453408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014278169,0.00009955615,0.00013918443,0.0002270325,0.00023423391,0.00011067825,0.0007719056,0.000055297452,0.0000041127655],"category_scores_gemma":[0.004922524,0.000062867555,0.000048442438,0.0013898002,0.00028335772,0.0006910191,0.00015580449,0.0002396215,0.0000019547972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043518106,0.0001804194,0.000051815507,0.000005534366,0.000005487668,1.4532314e-7,0.0006670829,0.91617334,0.000025140418,0.08122065,0.00023670263,0.0013901845],"study_design_scores_gemma":[0.0004033731,0.00014501139,0.00033012932,0.000042612544,7.851736e-7,2.4964262e-7,0.00023946005,0.9886501,0.008874851,0.0011042055,0.00014122299,0.00006800978],"about_ca_topic_score_codex":0.00012752734,"about_ca_topic_score_gemma":0.00019757383,"teacher_disagreement_score":0.7071883,"about_ca_system_score_codex":0.00006974694,"about_ca_system_score_gemma":0.00041766622,"threshold_uncertainty_score":0.58930767},"labels":[],"label_agreement":null},{"id":"W2185256081","doi":"10.1115/1.4031982","title":"Optimization on Metamodeling-Supported Iterative Decomposition","year":2015,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mathematical optimization; Metamodeling; Optimization problem; Computer science; Variable (mathematics); Decomposition; Algorithm; Mathematics","score_opus":0.06555496768357283,"score_gpt":0.32385930365157944,"score_spread":0.2583043359680066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2185256081","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":[0.000058060425,0.000037344067,0.9983539,0.00058589753,0.000582486,0.00017982512,9.219665e-7,0.00005093053,0.00015061724],"genre_scores_gemma":[0.05990295,0.000018224717,0.93944234,0.00047078464,0.00010639431,0.000004171851,0.0000018223012,0.00001428413,0.000039048802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980006,0.00041563457,0.00056566944,0.00022935458,0.00060681504,0.00018190453],"domain_scores_gemma":[0.9975116,0.00022541107,0.00053810794,0.00022615156,0.0012182965,0.0002804292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011747882,0.00015861221,0.00028181344,0.00023993908,0.00006847624,0.00011831617,0.00047854224,0.0000881254,0.000018480852],"category_scores_gemma":[0.00053782115,0.00012972276,0.000101508354,0.0004180779,0.000015235494,0.0011251299,0.000060054947,0.0002586345,0.000019318251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000113581074,0.00015204355,2.8430395e-7,9.362872e-7,0.000030934694,0.000045734127,0.00015368787,0.9892703,0.00085894886,0.0052014412,0.00032531415,0.0038467816],"study_design_scores_gemma":[0.0012494476,0.0010865808,0.0000016708592,0.0000312572,0.000018653192,0.00014370064,0.00002545624,0.97173417,0.016437002,0.0090790205,0.000055916058,0.00013713408],"about_ca_topic_score_codex":5.003876e-7,"about_ca_topic_score_gemma":8.509012e-8,"teacher_disagreement_score":0.059844892,"about_ca_system_score_codex":0.00020829999,"about_ca_system_score_gemma":0.0001952968,"threshold_uncertainty_score":0.528994},"labels":[],"label_agreement":null},{"id":"W2189581131","doi":"10.1007/0-306-48102-2_19","title":"Random Search Under Additive Noise","year":2002,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Advanced Multi-Objective Optimization Algorithms","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":"Concordia University; McGill University","funders":"","keywords":"Random noise; Noise (video); Computer science; Mathematics; Algorithm; Artificial intelligence","score_opus":0.06007800885750432,"score_gpt":0.39346730394705137,"score_spread":0.33338929508954707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2189581131","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022909204,0.0001294929,0.11117327,0.02120336,0.0035178587,0.005632362,0.00022562657,0.00027277542,0.8576162],"genre_scores_gemma":[0.043226976,0.013463412,0.16276693,0.00047793373,0.0004825009,0.002581224,0.00037391542,0.00015415382,0.7764729],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97470266,0.00060293474,0.0019975759,0.0043815593,0.015600064,0.002715237],"domain_scores_gemma":[0.9889153,0.00045360197,0.00014833377,0.0027623489,0.007082682,0.0006376807],"candidate_categories":["metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["sts","scholarly_communication","open_science","insufficient_payload"],"category_scores_codex":[0.016252873,0.000955291,0.00071199413,0.01910789,0.004515613,0.0078705065,0.016452504,0.00029238075,0.0034238412],"category_scores_gemma":[0.0014087317,0.0010134339,0.00021939866,0.009057342,0.011748825,0.017122902,0.012472882,0.002797223,0.0012970326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010680262,0.00033111477,0.000021982514,0.000027454882,0.00010121727,0.0003453856,0.00078095973,0.32350767,0.00013629613,0.66837925,0.0007657035,0.005496137],"study_design_scores_gemma":[0.0041002035,0.00038400467,0.002194102,0.0011041951,0.000019104718,0.0001185796,0.0047283564,0.79957694,0.000754425,0.046867993,0.13811558,0.0020364923],"about_ca_topic_score_codex":0.0004657947,"about_ca_topic_score_gemma":0.0020701804,"teacher_disagreement_score":0.6215113,"about_ca_system_score_codex":0.009263866,"about_ca_system_score_gemma":0.0011572387,"threshold_uncertainty_score":0.9995034},"labels":[],"label_agreement":null},{"id":"W2190344412","doi":"10.1145/2832987.2833057","title":"An Extension of DIRECT Algorithm Using Kriging Metamodel for Global Optimization","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Metamodeling; Kriging; Mathematical optimization; Benchmark (surveying); Computer science; Computation; Algorithm; Global optimization; Black box; Function (biology); Mathematics; Machine learning; Artificial intelligence","score_opus":0.06137164201412445,"score_gpt":0.34704289690860424,"score_spread":0.2856712548944798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2190344412","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":[0.00010871872,0.000053783646,0.9979586,0.000030506963,0.0003147074,0.0003403308,0.000010387977,0.00021199964,0.0009709664],"genre_scores_gemma":[0.012521599,0.0000045587476,0.98726124,0.00008293725,0.000041259573,0.000010473227,0.000012270411,0.000014175334,0.00005148706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986888,0.00006518709,0.00028090092,0.00045990807,0.00027923117,0.00022595197],"domain_scores_gemma":[0.9982351,0.000041157396,0.00017469675,0.00045427485,0.00093501137,0.0001597547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035830346,0.00015159154,0.00023166843,0.00009389598,0.000086327615,0.00006642304,0.0003666124,0.00005677635,0.0000032847893],"category_scores_gemma":[0.00011201384,0.00014087014,0.0000590068,0.0005584506,0.000036196616,0.0015440985,0.000103676015,0.00003379903,8.7046385e-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":[0.0000064106725,0.000071639406,0.000017443577,0.0000027339966,0.000009105603,8.2695226e-7,0.00008161363,0.96001935,0.00016599304,0.0028565808,0.000013383029,0.036754947],"study_design_scores_gemma":[0.00070537085,0.0001006354,0.000009868148,0.000008624886,0.000012811916,0.000010865311,0.000069594294,0.9948934,0.0024002884,0.0015744173,0.00004144664,0.00017269173],"about_ca_topic_score_codex":0.00004223498,"about_ca_topic_score_gemma":0.000002210864,"teacher_disagreement_score":0.036582258,"about_ca_system_score_codex":0.00013973897,"about_ca_system_score_gemma":0.00014838547,"threshold_uncertainty_score":0.57445174},"labels":[],"label_agreement":null},{"id":"W2206713289","doi":"10.5539/ijsp.v5n1p98","title":"Rules for Identifying the Initial Design Points for Use in the Quick Convergent Inflow Algorithm","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Vertex (graph theory); Boundary (topology); Mathematics; Interior point method; Inflow; Mathematical optimization; Algorithm; Point (geometry); Convergence (economics); Combinatorics; Geometry; Mathematical analysis; Graph","score_opus":0.12476307881015815,"score_gpt":0.3674341375937134,"score_spread":0.24267105878355527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2206713289","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":[0.0010332256,0.000049542417,0.99641263,0.00092862186,0.00083510624,0.0005091221,0.00022422247,0.000004220634,0.000003289748],"genre_scores_gemma":[0.01719788,0.000024776042,0.98229706,0.00030284558,0.00012313353,0.000034736106,0.000009222927,0.000005054026,0.000005261047],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987712,0.0001696486,0.00042320942,0.00014177788,0.00037878926,0.000115393865],"domain_scores_gemma":[0.9960815,0.0017662956,0.0003074493,0.00012077603,0.0016695631,0.000054422682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020242266,0.00008667786,0.00011965646,0.0000650537,0.000073409356,0.0002896113,0.0005877407,0.000022955563,0.0000018584559],"category_scores_gemma":[0.0018417513,0.00005409311,0.000040768828,0.000058489702,0.000087249085,0.00052847934,0.00007894447,0.000113654816,4.7285798e-7],"study_design_candidate":"design_other","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":[0.0010232511,0.0008353868,0.0020551104,0.00007034128,0.00035647288,0.000098246404,0.01270662,0.056411542,0.000030440538,0.2558716,0.0047746934,0.6657663],"study_design_scores_gemma":[0.0013419247,0.00016967121,0.0014722156,0.00001768008,0.000009160729,0.000071238625,0.00011122111,0.60392123,0.00007875254,0.39164197,0.0010927288,0.00007220954],"about_ca_topic_score_codex":0.000016973312,"about_ca_topic_score_gemma":0.000013733827,"teacher_disagreement_score":0.6656941,"about_ca_system_score_codex":0.00010329802,"about_ca_system_score_gemma":0.00017290856,"threshold_uncertainty_score":0.27927282},"labels":[],"label_agreement":null},{"id":"W2207562545","doi":"10.1115/omae2015-41392","title":"Response Surface Models for Analyzing Sinkage and Trim Effects on Planing Hull Motions in a Vertical Plane","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Trim; Hull; Center of gravity; Vertical plane; Marine engineering; Engineering; Plane (geometry); Structural engineering; Geometry; Mathematics","score_opus":0.03662129864729768,"score_gpt":0.29212999300101805,"score_spread":0.25550869435372037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2207562545","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":[0.102354825,0.000030786727,0.89643425,0.00046492968,0.000079593294,0.00033676386,0.0000033438268,0.000109351466,0.00018613535],"genre_scores_gemma":[0.5569373,0.0000023271114,0.44276655,0.00014613794,0.000009349376,0.00001346585,0.0000027536062,0.000008902893,0.000113210765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988958,0.00017083356,0.00016778869,0.0003688218,0.00015330937,0.00024340619],"domain_scores_gemma":[0.998442,0.0010757473,0.00002613699,0.00022702252,0.00007615788,0.00015294294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005609538,0.00012485666,0.00017047896,0.00015837695,0.00007092254,0.000074101015,0.00017221464,0.000057859634,6.8467193e-7],"category_scores_gemma":[0.00058539293,0.000115813695,0.00002220366,0.00033439326,0.000023448352,0.00048358232,0.000091881564,0.00010532169,0.0000049650375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003583207,0.000102966325,0.00026612694,0.000009959198,0.000009989919,0.000029224044,0.001237174,0.982341,0.0014095288,0.011853183,0.00008800808,0.0022945115],"study_design_scores_gemma":[0.0019919935,0.00029444552,0.0008502988,0.000030209623,0.0000031156922,0.000008131542,0.000047054054,0.9907685,0.0034164998,0.002421355,0.000025097135,0.0001433097],"about_ca_topic_score_codex":0.00001339339,"about_ca_topic_score_gemma":0.000009550459,"teacher_disagreement_score":0.45458248,"about_ca_system_score_codex":0.00011276752,"about_ca_system_score_gemma":0.00006475407,"threshold_uncertainty_score":0.47227454},"labels":[],"label_agreement":null},{"id":"W2221306040","doi":"10.1007/s11081-015-9301-2","title":"Use of a biobjective direct search algorithm in the process design of material science applications","year":2015,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; McGill University; Polytechnique Montréal","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Process (computing); Search algorithm; Mathematical optimization; Software; Algorithm; Mathematics; Programming language","score_opus":0.03504315994626296,"score_gpt":0.2741445349296213,"score_spread":0.23910137498335837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2221306040","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":[0.00046955195,0.000017633103,0.9989817,0.00002005856,0.000047232395,0.00037046638,0.000005251574,0.000034821536,0.00005328784],"genre_scores_gemma":[0.14433056,0.000023463503,0.8555585,0.0000079013425,0.000009990625,0.000056124114,0.00000236173,0.0000065849677,0.000004502889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920636,0.000036202844,0.00017479758,0.00020244977,0.00024755741,0.00013260299],"domain_scores_gemma":[0.999264,0.00008383675,0.00006104778,0.00019077375,0.00035175987,0.0000485725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004584228,0.00008029152,0.000112600275,0.00026274953,0.000042418953,0.00006903722,0.00029206267,0.0000259762,8.9640196e-7],"category_scores_gemma":[0.00012129964,0.000067189816,0.000009386663,0.0014831836,0.00009624567,0.000755489,0.00006830368,0.00005088172,2.0926214e-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":[0.0000027936583,0.000028577912,0.000023198161,0.000009063866,0.0000021086441,3.6042582e-7,0.0013731944,0.994842,0.00046454964,0.00036853412,6.2413807e-7,0.0028850315],"study_design_scores_gemma":[0.00017400656,0.000032860426,0.00013298914,0.000013327768,0.000001888818,0.0000050263393,0.0001367187,0.9920692,0.00733363,0.000021584508,0.0000090409385,0.000069757596],"about_ca_topic_score_codex":0.00001313257,"about_ca_topic_score_gemma":1.8051173e-7,"teacher_disagreement_score":0.14386101,"about_ca_system_score_codex":0.000038807797,"about_ca_system_score_gemma":0.00010960354,"threshold_uncertainty_score":0.27399212},"labels":[],"label_agreement":null},{"id":"W2246906915","doi":"10.1109/iisa.2015.7388081","title":"An effective identification of the induction machine parameters using a classic genetic algorithm, NSGA II and θ-NSGA III","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer 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":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Genetic algorithm; Stator; Induction motor; Robustness (evolution); Identification (biology); Computer science; Automation; Inductance; Engineering; Algorithm; Voltage; Machine learning","score_opus":0.022223546437156575,"score_gpt":0.280275502639169,"score_spread":0.25805195620201243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2246906915","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":[0.14403899,0.000059943213,0.8548367,0.00007274517,0.0004062952,0.00048586342,0.0000030186102,0.00006896761,0.000027474303],"genre_scores_gemma":[0.5062001,0.000004365462,0.4936544,0.000040078405,0.00002068138,0.000018247396,0.0000016578899,0.000009279755,0.00005124023],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854356,0.00026414014,0.00029688346,0.00043769454,0.00029461744,0.00016310638],"domain_scores_gemma":[0.9987005,0.000056875684,0.00027682478,0.00055754423,0.0003006655,0.00010761243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037792022,0.0001496645,0.00016170702,0.00012732153,0.00020498417,0.00007731641,0.00038010464,0.00006695293,0.000001561263],"category_scores_gemma":[0.000099161494,0.00011388246,0.000037171278,0.0006321263,0.00014886324,0.0008597583,0.00022666661,0.00012328982,0.0000013386453],"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":[0.000023029846,0.00032374533,0.0010713215,0.000013588803,0.00006222215,0.0000023093837,0.004984927,0.27945137,0.028582228,0.0014152363,0.000017634535,0.68405235],"study_design_scores_gemma":[0.0006581481,0.00013080782,0.012021727,0.000008805677,0.000015738433,0.000032692817,0.00014073978,0.96253425,0.022628829,0.0016814076,0.000009170525,0.00013765608],"about_ca_topic_score_codex":0.00014290935,"about_ca_topic_score_gemma":0.00001067651,"teacher_disagreement_score":0.6839147,"about_ca_system_score_codex":0.00014100618,"about_ca_system_score_gemma":0.00005538475,"threshold_uncertainty_score":0.4643992},"labels":[],"label_agreement":null},{"id":"W2256082675","doi":"10.1002/cjce.22353","title":"Optimization of both operating costs and energy efficiency in the alumina evaporation process by a multi‐objective state transition algorithm","year":2015,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Mathematical optimization; Benchmark (surveying); Pareto principle; Process (computing); Task (project management); Computer science; Evolutionary algorithm; Multi-objective optimization; Optimization problem; Operating cost; Algorithm; Mathematics; Engineering","score_opus":0.007813357490828723,"score_gpt":0.2198140969412945,"score_spread":0.21200073945046577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2256082675","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":[0.01144569,0.00022902443,0.9879124,0.00024320195,0.000058976064,0.000083173465,0.000004928431,0.0000065012587,0.000016078446],"genre_scores_gemma":[0.89379936,0.00000456545,0.10608919,0.00006243373,0.000023673378,0.0000058618616,0.0000033285075,0.000009114589,0.000002455911],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991812,0.000054918604,0.0002806934,0.0001101801,0.00021302621,0.00015998795],"domain_scores_gemma":[0.9992358,0.00007155554,0.00014543725,0.00009377384,0.00030739902,0.00014603203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049323717,0.00009934048,0.00013260113,0.0001257124,0.000049230217,0.000071731665,0.00029461036,0.00003722804,4.9071997e-7],"category_scores_gemma":[0.00020467516,0.00007227401,0.000020359268,0.00044970875,0.00005368875,0.00046424678,0.000009616061,0.00017471757,4.804869e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022405623,0.000012914543,0.00000519079,0.0000041122503,0.000005457587,0.0000059669164,0.005717088,0.988455,0.0014313381,0.00007795816,0.0000049682703,0.0042777513],"study_design_scores_gemma":[0.00049301103,0.000046049638,0.000009813891,0.000043462893,0.000004625936,0.000072560084,0.00018630183,0.987374,0.011637508,0.000048235623,0.000004454972,0.00007995251],"about_ca_topic_score_codex":0.00069759705,"about_ca_topic_score_gemma":0.00011577992,"teacher_disagreement_score":0.88235366,"about_ca_system_score_codex":0.00022694148,"about_ca_system_score_gemma":0.0003192014,"threshold_uncertainty_score":0.29472485},"labels":[],"label_agreement":null},{"id":"W2264467697","doi":"10.1007/s12206-015-0422-5","title":"Development of a metamodel assisted sampling approach to aerodynamic shape optimization problems","year":2015,"lang":"en","type":"article","venue":"Journal of Mechanical Science and Technology","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 Victoria; Simon Fraser University","funders":"","keywords":"Airfoil; Metamodeling; Aerodynamics; Turbomachinery; Computer science; Surrogate model; Mathematical optimization; Shape optimization; Sampling (signal processing); Engineering; Aerospace engineering; Mathematics; Finite element method; Structural engineering","score_opus":0.059395005705395514,"score_gpt":0.2959650373075293,"score_spread":0.2365700316021338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2264467697","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":[0.014208765,0.00005114633,0.9848286,0.0005207547,0.00011638656,0.00015894334,2.7007914e-7,0.000041444415,0.000073694784],"genre_scores_gemma":[0.2298005,0.0000069710027,0.7701346,0.00003872513,0.000006229021,0.0000055095866,1.4633146e-7,0.0000039236766,0.0000033517226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982313,0.000023215061,0.0005324868,0.00030902852,0.0006615774,0.00024240674],"domain_scores_gemma":[0.9975296,0.000026847529,0.0004035732,0.00021182251,0.0016165148,0.00021160499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001898812,0.00010677769,0.0002718564,0.0007594481,0.0001200118,0.000052204367,0.0010107271,0.00009404705,0.0000010151592],"category_scores_gemma":[0.0009079979,0.00008380708,0.000022406337,0.002798923,0.00015503544,0.00066240766,0.00048253319,0.0001811306,8.006902e-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":[0.000021645472,0.0004448414,0.000021683149,0.00001660703,0.000033520268,0.000004080379,0.00089901785,0.48788002,0.07395785,0.05696478,0.0000071136396,0.37974882],"study_design_scores_gemma":[0.00042720718,0.00018444895,0.000031707008,0.000026289224,0.0000058917963,0.00015044273,0.00018984305,0.99159706,0.00434689,0.0028397383,0.00009568294,0.000104816325],"about_ca_topic_score_codex":7.3247185e-7,"about_ca_topic_score_gemma":0.0000013530815,"teacher_disagreement_score":0.503717,"about_ca_system_score_codex":0.0001897929,"about_ca_system_score_gemma":0.0006310512,"threshold_uncertainty_score":0.34175533},"labels":[],"label_agreement":null},{"id":"W2277109028","doi":"10.1504/ijedpo.2015.072803","title":"Distance correlation-based nearly orthogonal space-filling experimental designs","year":2015,"lang":"en","type":"article","venue":"International Journal of Experimental Design and Process Optimisation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Orthogonality; Computer science; Design of experiments; Metric (unit); Context (archaeology); Correlation; Orthogonal array; Function (biology); Sampling (signal processing); Space (punctuation); Algorithm; Mathematical optimization; Mathematics; Statistics; Machine learning; Geometry; Taguchi methods","score_opus":0.048761867406958544,"score_gpt":0.32687539531102355,"score_spread":0.278113527904065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2277109028","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":[0.008573524,0.0011568972,0.9885594,0.00030587296,0.0008891404,0.00021854792,0.0000022846789,0.00004650265,0.0002477778],"genre_scores_gemma":[0.61060846,0.000010672887,0.38909945,0.00012525912,0.00009904508,0.000009828894,0.0000059035347,0.000012494622,0.00002886402],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804115,0.0001255435,0.0004976475,0.0002903956,0.0008730576,0.00017221054],"domain_scores_gemma":[0.9979156,0.00015205731,0.0005653026,0.00012094542,0.0010205341,0.00022555511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004507763,0.00020758616,0.00019415993,0.00024731085,0.00010836288,0.00030910128,0.0005339321,0.00007049386,0.000025448462],"category_scores_gemma":[0.00013393165,0.00020021766,0.00006680213,0.00020849318,0.000090050504,0.002058279,0.000055525004,0.00016070971,0.0000055108526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006093386,0.00047005966,0.00045192297,0.0000039318697,0.00007060636,0.00006358589,0.0057612364,0.9717775,0.012368178,0.0051912735,0.00010722705,0.0031251274],"study_design_scores_gemma":[0.0022754138,0.00045158638,0.000063116604,0.000057794743,0.0000063430607,0.0001458139,0.0009513414,0.84539264,0.14942573,0.00089398463,0.0001049885,0.00023122986],"about_ca_topic_score_codex":0.0000037690818,"about_ca_topic_score_gemma":1.648188e-7,"teacher_disagreement_score":0.6020349,"about_ca_system_score_codex":0.00033349256,"about_ca_system_score_gemma":0.000333531,"threshold_uncertainty_score":0.81646395},"labels":[],"label_agreement":null},{"id":"W2278053714","doi":"10.1214/15-sts531","title":"Analysis Methods for Computer Experiments: How to Assess and What Counts?","year":2016,"lang":"en","type":"article","venue":"Statistical Science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Smoothness; Computer science; Gaussian process; Bayesian probability; Bayes' theorem; Regression; Regression analysis; Code (set theory); Exponential function; Function (biology); Machine learning; Statistics; Algorithm; Econometrics; Artificial intelligence; Gaussian; Data mining; Mathematics","score_opus":0.050557874987754145,"score_gpt":0.425066255779265,"score_spread":0.37450838079151083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2278053714","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":[0.000014204388,0.000021507194,0.9984357,0.0007039174,0.0005443088,0.00013716839,0.000011000175,0.00003765089,0.00009459258],"genre_scores_gemma":[0.0047228853,0.000012355067,0.994805,0.00028064047,0.000024705525,0.00002578148,6.195653e-7,0.0000033519677,0.0001247015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891436,0.00004455049,0.00008882953,0.0004935049,0.00021211393,0.00024661628],"domain_scores_gemma":[0.9985144,0.00080557074,0.000033567234,0.00023316806,0.00021393436,0.00019932308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050246005,0.00007710162,0.0001232566,0.00013104218,0.0001318232,0.0005328524,0.00040506548,0.000016064965,0.000031145686],"category_scores_gemma":[0.00053803035,0.000051961124,0.000017485801,0.0008574218,0.00025718522,0.0013233164,0.00021088845,0.000020752252,0.0000137192565],"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":[0.0000017516419,0.00001353629,0.000029701045,0.0000013926622,0.000011595897,9.1631995e-7,0.0000873218,0.00011458584,0.0013156103,0.08413006,0.0001484767,0.91414505],"study_design_scores_gemma":[0.00026975104,0.00011522698,0.0023455035,0.000013326691,0.000018917232,0.0000021540184,0.00003911765,0.97925055,0.0030928086,0.004833137,0.009779171,0.00024032396],"about_ca_topic_score_codex":9.596188e-7,"about_ca_topic_score_gemma":4.4451536e-7,"teacher_disagreement_score":0.979136,"about_ca_system_score_codex":0.00006956003,"about_ca_system_score_gemma":0.00004644133,"threshold_uncertainty_score":0.5138308},"labels":[],"label_agreement":null},{"id":"W2279033113","doi":"10.1080/00401706.2015.1044119","title":"Comment: Expected Improvement for Efficient Blackbox Constrained Optimization","year":2016,"lang":"en","type":"article","venue":"Technometrics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"University of Florida","keywords":"Computer science; Image (mathematics); Work (physics); Artificial intelligence; Engineering","score_opus":0.015552989953244714,"score_gpt":0.25632255971756585,"score_spread":0.24076956976432112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279033113","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":[0.00013227976,0.000046364843,0.9951161,0.002421756,0.0002616153,0.0009651129,0.00003777717,0.00077827595,0.00024067743],"genre_scores_gemma":[0.09273391,0.000032612224,0.9064988,0.0003439672,0.000026671552,0.00020179583,0.000010075503,0.000023692079,0.00012845777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847686,0.000014947521,0.00033715132,0.0005002351,0.00031223567,0.00035854403],"domain_scores_gemma":[0.99831516,0.00033686362,0.00021908358,0.00059110817,0.00043671567,0.00010108665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026357017,0.00018772433,0.00018565176,0.00096213474,0.0001541788,0.00007160021,0.0006466702,0.00008955883,0.000025063076],"category_scores_gemma":[0.00078109535,0.00014540215,0.00007003689,0.0031279954,0.00009249894,0.00024406196,0.00022891963,0.000058770856,0.000010485442],"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":[0.000027693139,0.0007616602,0.00009869598,0.00003266102,0.00006283737,0.0000050853664,0.00022542846,0.29789987,0.0038505811,0.15029743,0.0008804426,0.5458576],"study_design_scores_gemma":[0.002332571,0.00026094745,0.000020792166,0.000018920691,0.0000075781827,0.0000028667407,0.000041134233,0.97760683,0.015604884,0.00093173014,0.002858094,0.00031364852],"about_ca_topic_score_codex":8.635089e-7,"about_ca_topic_score_gemma":1.2540869e-7,"teacher_disagreement_score":0.679707,"about_ca_system_score_codex":0.00033972933,"about_ca_system_score_gemma":0.00004952111,"threshold_uncertainty_score":0.59293276},"labels":[],"label_agreement":null},{"id":"W2287252250","doi":"10.1109/tmag.2015.2491301","title":"Kriging-Assisted Multi-Objective Design of Permanent Magnet Motor for Position Sensorless Control","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Torque ripple; Kriging; Computer science; Torque; Control theory (sociology); Surrogate model; Computation; Magnet; Position (finance); Finite element method; Ripple; Evolutionary algorithm; Mathematical optimization; Direct torque control; Control engineering; Control (management); Algorithm; Mathematics; Induction motor; Power (physics); Engineering; Artificial intelligence; Machine learning; Mechanical engineering; Physics; Voltage","score_opus":0.042524462057089894,"score_gpt":0.28266874074291504,"score_spread":0.24014427868582514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2287252250","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":[0.00027617058,0.00004602558,0.99681795,0.00019020599,0.00075919455,0.0014679201,0.00017296284,0.00019433585,0.00007521417],"genre_scores_gemma":[0.37578404,0.000016374548,0.6231541,0.00013507552,0.000027247366,0.00018234544,0.0000042424267,0.00002939574,0.00066718017],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982725,0.00015824205,0.00040444804,0.00048297524,0.00035717175,0.00032467683],"domain_scores_gemma":[0.9979174,0.00033124807,0.00020078022,0.00046884952,0.0008864326,0.00019529933],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023517502,0.00026783632,0.00030913806,0.00028471852,0.0001533447,0.000060772494,0.00036355515,0.00012323193,0.000015164801],"category_scores_gemma":[0.000027500826,0.00028095263,0.00013112722,0.00039371033,0.00011008818,0.00033576885,0.0000031608695,0.00017845632,0.000017522494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034116194,0.0008100165,0.0000032075477,0.000024284172,0.000056588655,0.000008301635,0.00089255924,0.9547122,0.014049584,0.00026515467,0.00006330334,0.028773606],"study_design_scores_gemma":[0.004929443,0.0015520346,0.00017887352,0.000022849757,0.00006973838,0.000032632244,0.00016950107,0.962186,0.03024212,0.00024662272,0.00006364442,0.0003065364],"about_ca_topic_score_codex":0.000017342983,"about_ca_topic_score_gemma":0.0000074008412,"teacher_disagreement_score":0.37550786,"about_ca_system_score_codex":0.0001892677,"about_ca_system_score_gemma":0.0001395421,"threshold_uncertainty_score":0.99996424},"labels":[],"label_agreement":null},{"id":"W2291284919","doi":"10.1109/tmag.2015.2487969","title":"On the Role of Robustness in Multi-Objective Robust Optimization: Application to an IPM Motor Design Problem","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Computer science; Robust optimization; Mathematical optimization; Optimization problem; Control theory (sociology); Control engineering; Mathematics; Artificial intelligence; Engineering; Algorithm","score_opus":0.036092940105732836,"score_gpt":0.2628985279704792,"score_spread":0.22680558786474636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2291284919","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":[0.00023694754,0.000017490078,0.9973622,0.0002570236,0.00016358156,0.0016408821,0.00001520913,0.00012863458,0.00017803679],"genre_scores_gemma":[0.20751233,0.000009922507,0.79177445,0.0001182509,0.000013557201,0.00042078324,0.0000019639313,0.000028530472,0.00012021428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815387,0.00027624785,0.0003676211,0.0005429164,0.00039564187,0.00026372686],"domain_scores_gemma":[0.99816304,0.00024264627,0.00014081044,0.0007440829,0.00052980817,0.00017963107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038678036,0.00023503385,0.00020485018,0.0003372737,0.00013280765,0.00006678182,0.0006791741,0.000108992426,0.000016258413],"category_scores_gemma":[0.000038281716,0.0002086802,0.000046756228,0.0012803142,0.000073964366,0.00041442958,0.000007568881,0.0002544869,0.000018092825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007077688,0.0005947074,0.000002959632,0.000002801711,0.0000068117265,9.298804e-7,0.0012408288,0.97676456,0.000371361,0.00059585756,0.0000055205683,0.02034289],"study_design_scores_gemma":[0.00075365836,0.00080833834,0.000038906408,0.000019851805,0.000009067034,0.0000038136545,0.00037933077,0.98316795,0.014156975,0.0004204999,0.000019169114,0.00022242634],"about_ca_topic_score_codex":0.0000399578,"about_ca_topic_score_gemma":0.00005331293,"teacher_disagreement_score":0.20727538,"about_ca_system_score_codex":0.00021505462,"about_ca_system_score_gemma":0.0001439979,"threshold_uncertainty_score":0.8509731},"labels":[],"label_agreement":null},{"id":"W2293460382","doi":"10.1162/evco_a_00175","title":"Global WASF-GA: An Evolutionary Algorithm in Multiobjective Optimization to Approximate the Whole Pareto Optimal Front","year":2016,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Ministry of Economic Affairs","keywords":"Multi-objective optimization; Evolutionary algorithm; Weighting; Mathematical optimization; Nadir; Weight; Metric (unit); Mathematics; Set (abstract data type); Function (biology); Genetic algorithm; Pareto principle; Algorithm; Weight function; Global optimization; Computer science; Statistics","score_opus":0.010317740785484399,"score_gpt":0.26596375357615065,"score_spread":0.25564601279066623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293460382","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":[0.001319493,0.00016998514,0.99351144,0.0022580172,0.0007348978,0.0012273442,0.00012800624,0.00045311774,0.00019771917],"genre_scores_gemma":[0.112519346,0.000016212698,0.8864199,0.00028644325,0.00019351344,0.0002657716,0.00013380029,0.000030776373,0.00013422377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963737,0.00050281064,0.0006340254,0.0011413941,0.0007298079,0.0006182534],"domain_scores_gemma":[0.9979016,0.00023717212,0.00026710067,0.00061344804,0.0007397973,0.00024084492],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042505594,0.0003965878,0.00029998677,0.00033328723,0.00050485105,0.00012320917,0.00085890415,0.00014821136,0.000023767749],"category_scores_gemma":[0.0002006671,0.0003043187,0.00009582197,0.0014039684,0.00016496678,0.0029975509,0.00042424037,0.00015969023,0.00013864614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044926353,0.00025384492,0.00069177226,0.0000031694087,0.00001792465,0.000011942419,0.0006302462,0.9339746,0.000037421123,0.0015083444,0.00063191494,0.06219386],"study_design_scores_gemma":[0.0013304966,0.00021520387,0.056978676,0.000048185724,0.0000074059744,0.000060306364,0.00021085334,0.9366601,0.000021480915,0.003787943,0.00025199962,0.0004273483],"about_ca_topic_score_codex":0.000095572235,"about_ca_topic_score_gemma":0.000035706005,"teacher_disagreement_score":0.111199856,"about_ca_system_score_codex":0.0019325756,"about_ca_system_score_gemma":0.0003019436,"threshold_uncertainty_score":0.9999409},"labels":[],"label_agreement":null},{"id":"W2300775146","doi":"10.1115/detc2015-47535","title":"Constrained Multi-Objective Wind Farm Layout Optimization: Introducing a Novel Constraint Handling Approach Based on Constraint Programming","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Constraint (computer-aided design); Mathematical optimization; Computer science; Wind power; Optimization problem; Constraint logic programming; Constraint programming; Constrained optimization; Genetic algorithm; Global optimization; Stochastic programming; Engineering; Mathematics","score_opus":0.038412644678301584,"score_gpt":0.27547881905220367,"score_spread":0.2370661743739021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2300775146","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":[0.000047769758,0.000024802845,0.9871428,0.00056488445,0.00055274396,0.0016035365,0.000026169157,0.00094391126,0.009093357],"genre_scores_gemma":[0.13252525,0.0000011176004,0.8661289,0.0008528057,0.00014220928,0.00009116866,0.00005510562,0.000052476313,0.00015097117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99572515,0.00017900507,0.0007967258,0.001631284,0.0008058287,0.00086202053],"domain_scores_gemma":[0.9961214,0.00031915095,0.0004208301,0.00094505283,0.0016169851,0.00057655986],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011418968,0.0006344211,0.0006013686,0.0005009868,0.00037722025,0.00054460485,0.0008229071,0.00022592943,0.00002980154],"category_scores_gemma":[0.0014958848,0.00060181663,0.00016502877,0.0012535943,0.0006666259,0.00068844977,0.00025538713,0.00055586576,0.000018434688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003685174,0.0007076308,0.00006110002,0.000015210874,0.00005437708,0.000019608033,0.0014493607,0.96014404,0.00009998946,0.010493233,0.000010696038,0.026907895],"study_design_scores_gemma":[0.0066432743,0.00027636514,0.000024024443,0.00006767176,0.00002063649,0.00012787027,0.0028928977,0.9882625,0.00077592226,0.00003916024,0.00013993678,0.0007297738],"about_ca_topic_score_codex":0.000038586517,"about_ca_topic_score_gemma":0.000008034285,"teacher_disagreement_score":0.13247748,"about_ca_system_score_codex":0.00052161765,"about_ca_system_score_gemma":0.0010490067,"threshold_uncertainty_score":0.9996433},"labels":[],"label_agreement":null},{"id":"W2312272181","doi":"10.2514/6.2005-2204","title":"Collaboration Pursuing Method for MDO Problems","year":2005,"lang":"en","type":"article","venue":"46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science","score_opus":0.011503235781471341,"score_gpt":0.2876740148508082,"score_spread":0.27617077906933685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2312272181","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":[0.04811795,0.00022357391,0.944562,0.0013055452,0.0022168537,0.0020646588,0.00085750275,0.00042746242,0.00022445468],"genre_scores_gemma":[0.46516493,0.00012067618,0.5333321,0.00029597685,0.00031185083,0.00013608791,0.00031416045,0.000060204387,0.00026399083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953238,0.00028879705,0.0011079435,0.001617811,0.00061586505,0.001045798],"domain_scores_gemma":[0.99650866,0.00023434807,0.0008357465,0.0009520105,0.0010971479,0.00037210473],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005514296,0.0008819859,0.0009757368,0.0003294886,0.00076880195,0.0018665807,0.0012800742,0.00041199083,0.00014089617],"category_scores_gemma":[0.00027318404,0.0007979674,0.00012504857,0.0006088993,0.00022936301,0.0019988657,0.0005580776,0.00032967888,0.000006596765],"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":[0.00016622453,0.000030094325,0.000066722525,0.00030907508,0.00015317445,0.000010021788,0.0017275632,0.020460617,0.046296775,0.6779096,0.000180349,0.25268978],"study_design_scores_gemma":[0.0025678673,0.00026066025,0.003714333,0.00008924817,0.00007410092,0.00017884425,0.00018118002,0.8893683,0.021079024,0.07909184,0.002010922,0.001383679],"about_ca_topic_score_codex":0.0001398821,"about_ca_topic_score_gemma":0.00054115616,"teacher_disagreement_score":0.8689077,"about_ca_system_score_codex":0.00037359644,"about_ca_system_score_gemma":0.00033181577,"threshold_uncertainty_score":0.9994471},"labels":[],"label_agreement":null},{"id":"W2315532451","doi":"10.2514/6.2006-6930","title":"Optimal Design for Uncertain Load Cases Using Convex Hulls","year":2006,"lang":"en","type":"article","venue":"11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Christian Studies; University of Toronto","funders":"","keywords":"Hull; Convex hull; Computer science; Regular polygon; Mathematical optimization; Mathematics; Engineering; Marine engineering; Geometry","score_opus":0.04647131534114375,"score_gpt":0.31098976970359415,"score_spread":0.2645184543624504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2315532451","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":[0.0022515478,0.00024947865,0.9958451,0.00029129596,0.0001359797,0.0008412089,0.000058579637,0.00021570931,0.00011110731],"genre_scores_gemma":[0.20203494,0.00009096597,0.7970811,0.000053911324,0.00007038765,0.00008734507,0.00013505878,0.000028663004,0.00041761014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99666864,0.00023764967,0.0007705898,0.0012627852,0.00047146215,0.0005888912],"domain_scores_gemma":[0.99632645,0.0007888281,0.00052997505,0.00068009866,0.0014547233,0.00021991618],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00059659476,0.00049419183,0.00072188285,0.00057302136,0.0008856063,0.0005317908,0.0005975484,0.00018427406,0.00008184466],"category_scores_gemma":[0.00027833326,0.0004852774,0.00024869174,0.002064664,0.00022455586,0.0014657596,0.00034370154,0.00015978303,0.0000036756862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060202256,0.00015488052,0.00085326674,0.000018756402,0.00017270794,0.000024533927,0.00033374768,0.99479026,0.00019769982,0.002247599,0.000030084724,0.0011162849],"study_design_scores_gemma":[0.0011379789,0.000162679,0.00061887706,0.000028181728,0.00038566467,0.000031278483,0.000200322,0.99536383,0.0010052688,0.00039711478,0.00004918978,0.00061958976],"about_ca_topic_score_codex":0.00021664516,"about_ca_topic_score_gemma":0.000051783212,"teacher_disagreement_score":0.19978338,"about_ca_system_score_codex":0.00020589738,"about_ca_system_score_gemma":0.00039517562,"threshold_uncertainty_score":0.9997599},"labels":[],"label_agreement":null},{"id":"W2316430657","doi":"10.2514/6.2008-5808","title":"A Strategy for Multi-Objective Shape Optimization for Turbine Stages in Three-Dimensional Flow","year":2008,"lang":"en","type":"article","venue":"12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference","topic":"Advanced Multi-Objective Optimization Algorithms","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":"Concordia University","funders":"","keywords":"Computer science; Turbine; Flow (mathematics); Mechanical engineering; Engineering; Mechanics; Physics","score_opus":0.05073634765538736,"score_gpt":0.30925384147323776,"score_spread":0.2585174938178504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316430657","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":[0.0043563787,0.0002027644,0.9928152,0.00028776278,0.0001460715,0.0017860772,0.00019237837,0.00018629707,0.000027092248],"genre_scores_gemma":[0.19576128,0.00018850871,0.8026456,0.000057351033,0.00004771586,0.00047901002,0.00059933064,0.00003945607,0.00018170816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963924,0.0001303821,0.00091580086,0.0015278803,0.0004021702,0.0006313489],"domain_scores_gemma":[0.9965421,0.0004778481,0.0005192285,0.00059433485,0.0016084624,0.00025800266],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047788135,0.00056408666,0.000848222,0.0010407671,0.0008800485,0.00022054404,0.0005707767,0.0002488645,0.00015685413],"category_scores_gemma":[0.00032720278,0.0005556821,0.0002884124,0.0023180111,0.00022905716,0.0018123892,0.00031777745,0.00022664774,0.000001843846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015410487,0.0003776798,0.0037043921,0.00003156652,0.00021639437,0.00001004748,0.00079688366,0.98946476,0.000034129436,0.0006097665,0.000009366768,0.0045909216],"study_design_scores_gemma":[0.0035500508,0.00031160325,0.007851086,0.000038048565,0.00017047844,0.0000120590885,0.00014206613,0.9868154,0.00020244074,0.00023982221,0.0000057375605,0.0006611943],"about_ca_topic_score_codex":0.00005710082,"about_ca_topic_score_gemma":0.00031538383,"teacher_disagreement_score":0.1914049,"about_ca_system_score_codex":0.00015350044,"about_ca_system_score_gemma":0.0003350696,"threshold_uncertainty_score":0.99968946},"labels":[],"label_agreement":null},{"id":"W2317669480","doi":"10.1016/j.ifacol.2015.06.285","title":"Inventory replenishment planning and staggering","year":2015,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Heuristics; Mathematical optimization; Computer science; Pareto principle; Pareto optimal; Population; Heuristic; Operations research; Order (exchange); Space (punctuation); Multi-objective optimization; Mathematics; Economics","score_opus":0.04709039033167884,"score_gpt":0.29784809703990955,"score_spread":0.2507577067082307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2317669480","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":[0.01767255,0.00095392886,0.97798777,0.0008674417,0.0004916759,0.00019847677,0.0000043265222,0.0003452133,0.0014786216],"genre_scores_gemma":[0.018394923,0.000022029293,0.9802521,0.00045478527,0.00011794954,0.000012915707,0.000008141837,0.000017372435,0.00071977585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987659,0.000042044576,0.00019649233,0.00044765457,0.00028283297,0.00026510542],"domain_scores_gemma":[0.99914557,0.00004050139,0.0000991095,0.0003513713,0.00011721691,0.00024625903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023510434,0.00016099852,0.00016949579,0.00009567617,0.00009642042,0.00008977074,0.00028980352,0.0000491345,0.0000068205327],"category_scores_gemma":[0.00014059963,0.00015800566,0.000027518781,0.00022477937,0.00005311091,0.0005981108,0.0002839172,0.0001390166,0.000016187874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021082297,0.0010625292,0.039332647,0.00018143553,0.00035940707,0.0011518315,0.07229859,0.47744036,0.011046022,0.017552169,0.000453738,0.37891045],"study_design_scores_gemma":[0.0015418323,0.00017477982,0.0011997242,0.0000584173,0.000007843688,0.00007361878,0.0009331014,0.98949283,0.0012029952,0.00048260996,0.0043993676,0.00043287993],"about_ca_topic_score_codex":0.000013606515,"about_ca_topic_score_gemma":0.0000036361198,"teacher_disagreement_score":0.5120525,"about_ca_system_score_codex":0.00012305364,"about_ca_system_score_gemma":0.00007467338,"threshold_uncertainty_score":0.64432836},"labels":[],"label_agreement":null},{"id":"W2321338579","doi":"10.2514/6.2013-2652","title":"Sensitivity-Based Sequential Sampling of Cokriging Response Surfaces for Aerodynamic Data","year":2013,"lang":"en","type":"article","venue":"31st AIAA Applied Aerodynamics Conference","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"Compute Canada; McGill University","keywords":"Sensitivity (control systems); Aerodynamics; Sampling (signal processing); Computer science; Engineering; Aerospace engineering; Electronic engineering; Computer vision","score_opus":0.05740063859704398,"score_gpt":0.30207328786964016,"score_spread":0.2446726492725962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2321338579","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":[0.08777169,0.000009680933,0.90950656,0.00034275273,0.0003035283,0.001320148,0.00026103394,0.00028172808,0.00020287545],"genre_scores_gemma":[0.55307424,0.000004758619,0.4464529,0.00009093045,0.000019323197,0.000074164585,0.00021693099,0.000029801226,0.00003692527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967444,0.00021669146,0.00066116237,0.0012725191,0.00046490386,0.0006403411],"domain_scores_gemma":[0.9949306,0.0015347565,0.00053937477,0.0020354392,0.0007884056,0.00017141733],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013432722,0.00042666783,0.0005555548,0.00025161827,0.0002738846,0.00031512094,0.00173436,0.0001635849,0.000021993834],"category_scores_gemma":[0.00036946012,0.00045858623,0.00008477614,0.00059762876,0.00027901388,0.00095800974,0.0008153114,0.00027906007,0.000031631607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003424439,0.00024839293,0.00019878006,0.00013921897,0.00008902049,0.000005086951,0.00040653016,0.5356797,0.37213728,0.06451221,0.000041053703,0.026200255],"study_design_scores_gemma":[0.0011309005,0.00005606344,0.0018898301,0.00004727163,0.000019374816,0.0000045821803,0.00009369008,0.9908445,0.0032620698,0.0020492633,0.00010570716,0.00049675466],"about_ca_topic_score_codex":0.000113039205,"about_ca_topic_score_gemma":0.00018610967,"teacher_disagreement_score":0.4653026,"about_ca_system_score_codex":0.00016339046,"about_ca_system_score_gemma":0.00056949875,"threshold_uncertainty_score":0.9997866},"labels":[],"label_agreement":null},{"id":"W2323402416","doi":"10.2514/6.2002-5464","title":"Effective Multi-Mission Aircraft Conceptual Design Optimization Using a Hybrid Multi-Objective Evolutionary Method","year":2002,"lang":"en","type":"article","venue":"9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Conceptual design; Computer science; Evolutionary algorithm; Evolutionary computation; Multi-objective optimization; Systems engineering; Engineering; Artificial intelligence; Human–computer interaction; Machine learning","score_opus":0.03141683009952633,"score_gpt":0.3019477032560249,"score_spread":0.27053087315649854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2323402416","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":[0.0003000136,0.00041841884,0.99577713,0.00029283523,0.00044348065,0.0020757355,0.000067202425,0.00053209707,0.00009309166],"genre_scores_gemma":[0.03681435,0.00047591448,0.9613757,0.00013346504,0.0001114646,0.0001657906,0.00022559005,0.00010394954,0.0005937325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9929239,0.0017972735,0.0011245562,0.002430148,0.0008480746,0.0008760568],"domain_scores_gemma":[0.9955597,0.0008658519,0.0009190891,0.001098739,0.001015256,0.00054136425],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00089981296,0.000996781,0.0011230654,0.0017853706,0.0017552585,0.0003420613,0.0007512726,0.00036554376,0.00016811964],"category_scores_gemma":[0.00032670095,0.00097585545,0.00047964594,0.003980775,0.00034973264,0.002605198,0.000617196,0.00055906165,0.000024247465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012936897,0.0010842262,0.0009217304,0.000021744485,0.00057418254,0.00004430668,0.0023656262,0.9920941,0.0005773096,0.0001270766,0.000026153406,0.0020341945],"study_design_scores_gemma":[0.003414129,0.0005259104,0.0010350675,0.00009637609,0.00072071794,0.000048492315,0.00035758398,0.99085903,0.0017886182,0.0000270869,0.000013078516,0.0011139336],"about_ca_topic_score_codex":0.00005418061,"about_ca_topic_score_gemma":0.000004134924,"teacher_disagreement_score":0.036514338,"about_ca_system_score_codex":0.00073131593,"about_ca_system_score_gemma":0.000101496575,"threshold_uncertainty_score":0.9995443},"labels":[],"label_agreement":null},{"id":"W2323491979","doi":"10.1080/01621459.2016.1159211","title":"A Method of Constructing Space-Filling Orthogonal Designs","year":2016,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Orthogonality; Generality; Orthogonal array; Simplicity; Space (punctuation); Class (philosophy); Latin hypercube sampling; Hypercube; Computer science; Mathematics; Theoretical computer science; Algebra over a field; Algorithm; Mathematical optimization; Pure mathematics; Discrete mathematics; Artificial intelligence; Geometry; Statistics; Monte Carlo method","score_opus":0.016654755919725307,"score_gpt":0.30833159826350426,"score_spread":0.29167684234377894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2323491979","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":[0.002366284,0.0000064958904,0.99578756,0.0014300754,0.0002158545,0.00005847329,0.0000216356,0.000010977265,0.00010262294],"genre_scores_gemma":[0.12979493,0.000011215467,0.86998206,0.000087784,0.00006173633,5.578267e-7,1.1821533e-7,0.000006572692,0.00005503908],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981411,0.00048408378,0.00049430644,0.00012724582,0.0005719864,0.00018128655],"domain_scores_gemma":[0.9933082,0.0031370984,0.0026517373,0.00013956928,0.0006922672,0.000071167255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010085503,0.00008873381,0.00031117143,0.00008280561,0.00006855962,0.00002706643,0.00037025387,0.000024057803,0.00001170915],"category_scores_gemma":[0.00384838,0.000051069837,0.00009800128,0.0004532335,0.000108139386,0.00029919334,0.00008950976,0.00014811585,0.0000022832653],"study_design_candidate":"design_other","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":[0.00007409163,0.00012388949,0.03745749,0.000008759988,0.0002715142,0.000013253935,0.0005518725,0.0044288817,0.029391153,0.10450731,0.0003981787,0.8227736],"study_design_scores_gemma":[0.009571582,0.0027262343,0.30659327,0.000902054,0.0006178827,0.001163655,0.0027170277,0.34507263,0.09046023,0.23584352,0.0024593212,0.0018726004],"about_ca_topic_score_codex":0.000005335886,"about_ca_topic_score_gemma":0.0000011958196,"teacher_disagreement_score":0.820901,"about_ca_system_score_codex":0.00037634897,"about_ca_system_score_gemma":0.00016241243,"threshold_uncertainty_score":0.46071482},"labels":[],"label_agreement":null},{"id":"W2325647503","doi":"10.1115/detc2015-47532","title":"Optimization of Cutting Conditions in Vibration Assisted Drilling of Composites via a Multi-Objective EGO Implementation","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; National Research Council Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pareto principle; Computer science; Vibration; Multi-objective optimization; Mathematical optimization; Kriging; Task (project management); Mathematics; Engineering; Machine learning","score_opus":0.03348011170114482,"score_gpt":0.3294746287325604,"score_spread":0.29599451703141555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2325647503","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":[0.0045231506,0.0000161958,0.994341,0.000053907774,0.00011380651,0.00047737424,0.000010823757,0.00008424393,0.0003794946],"genre_scores_gemma":[0.4579585,0.0000023921066,0.5419185,0.000022952996,0.0000063878815,0.000018679664,0.000057411213,0.0000071195955,0.000008035388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984723,0.00015658695,0.0006210992,0.00030644113,0.00027721326,0.00016638756],"domain_scores_gemma":[0.99824935,0.00014602182,0.00049624516,0.00022394632,0.0008187421,0.00006570589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003018923,0.00013657687,0.00023521097,0.00040886222,0.00006209153,0.00003082779,0.00021489804,0.000057571964,0.00001840359],"category_scores_gemma":[0.00012422811,0.00014565092,0.000044093777,0.0010615133,0.00005591479,0.0013182294,0.00009782044,0.00007652826,0.0000018947228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008757468,0.00017249139,0.004096525,0.000009997115,0.000016546352,9.038115e-7,0.0025528697,0.9759686,0.013122991,0.0016108891,0.00000331611,0.0024361182],"study_design_scores_gemma":[0.0016189218,0.00009237948,0.0067333844,0.000026222877,0.000006995212,0.0000047693557,0.001258317,0.92205936,0.067811854,0.00025808965,8.6415656e-7,0.00012881619],"about_ca_topic_score_codex":0.000163599,"about_ca_topic_score_gemma":0.00008630244,"teacher_disagreement_score":0.45343536,"about_ca_system_score_codex":0.00016842723,"about_ca_system_score_gemma":0.0001167285,"threshold_uncertainty_score":0.5939472},"labels":[],"label_agreement":null},{"id":"W2325902698","doi":"10.1109/jsyst.2015.2406874","title":"MOSOA-Based Multiobjective Design of Power Distribution Systems","year":2015,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Hydro-Québec","funders":"","keywords":"Recloser; Sorting; Reliability engineering; Pareto principle; Reliability (semiconductor); Genetic algorithm; Mathematical optimization; Multi-objective optimization; Computer science; Metaheuristic; Evolutionary algorithm; Failure rate; Fault (geology); Automation; Engineering; Electric power system; Power (physics); Circuit breaker; Algorithm; Mathematics","score_opus":0.04356989531453067,"score_gpt":0.27602803322796865,"score_spread":0.23245813791343797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2325902698","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":[0.0005261316,0.0005204059,0.99242234,0.000035306206,0.0055867904,0.00058668613,0.000029650828,0.00011080758,0.00018188324],"genre_scores_gemma":[0.93498206,0.000005864887,0.06457588,0.00001474039,0.00022368862,0.000034990513,0.0000041325934,0.000022666678,0.00013597352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970167,0.0007239009,0.0007306729,0.0003386266,0.0008245948,0.0003654765],"domain_scores_gemma":[0.9960707,0.00018953097,0.0008618753,0.0004454269,0.0020901102,0.00034237636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014682319,0.0002361126,0.00041061608,0.00020235467,0.00016760426,0.00025791876,0.0006389044,0.00012491249,0.0000022211498],"category_scores_gemma":[0.00030953647,0.00020662036,0.00009713054,0.00059442566,0.00007348561,0.000925023,0.00004131324,0.00029589605,0.0000304086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031763113,0.00009227682,0.00010588839,0.000015031536,0.00003981606,0.000037962818,0.00039039375,0.9970587,0.00058908894,0.00054569624,0.0008169923,0.00027639186],"study_design_scores_gemma":[0.001778388,0.00029991704,0.00011537973,0.00015899588,0.000011014554,0.00052549876,0.00027372868,0.994326,0.0016873094,0.00008949104,0.0004874275,0.00024686864],"about_ca_topic_score_codex":0.00004941503,"about_ca_topic_score_gemma":3.7829815e-7,"teacher_disagreement_score":0.93445593,"about_ca_system_score_codex":0.0006791736,"about_ca_system_score_gemma":0.000560524,"threshold_uncertainty_score":0.84257334},"labels":[],"label_agreement":null},{"id":"W2329042936","doi":"10.2514/6.2002-3000","title":"Adaptive Response Surface Method for Thermal Optimization: Application to Aircraft Engine Cooling System","year":2002,"lang":"en","type":"article","venue":"8th AIAA/ASME Joint Thermophysics and Heat Transfer Conference","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Finite element method; Quadrilateral; Discretization; Thermal; Surface (topology); Mechanical engineering; Thermal conduction; Computer science; Control theory (sociology); Engineering; Materials science; Structural engineering; Mathematics; Physics; Thermodynamics; Mathematical analysis; Geometry","score_opus":0.029009069144137926,"score_gpt":0.2514149912023029,"score_spread":0.22240592205816495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2329042936","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":[0.0033279727,0.000103645,0.99327743,0.0009576209,0.0001392927,0.0015641279,0.00005959387,0.00032504872,0.0002452702],"genre_scores_gemma":[0.507247,0.000027214817,0.4922194,0.00019072415,0.000054594882,0.0001653292,0.000006221743,0.000033076132,0.000056462184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770546,0.0002328202,0.00043884688,0.00087048305,0.00029639702,0.00045596907],"domain_scores_gemma":[0.99820864,0.00032186366,0.000036553378,0.0005851924,0.00058862043,0.00025912237],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045546953,0.00038704916,0.00047557225,0.0001122814,0.000339787,0.00017945649,0.0004383519,0.00011834319,0.00002070503],"category_scores_gemma":[0.000024022343,0.00037669676,0.00011176027,0.00052545,0.000060118156,0.0005331254,0.00007877383,0.00019715152,0.000019448735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026022724,0.00009162124,0.0000020196062,0.000049338687,0.000043882883,0.0000034607694,0.002504746,0.9040146,0.036604386,0.029718611,0.000008247898,0.026698876],"study_design_scores_gemma":[0.001025639,0.00031934926,0.00010256948,0.00007902797,0.000027435042,0.000011870978,0.00015934432,0.98403126,0.013518051,0.00018435418,0.000096581454,0.00044450478],"about_ca_topic_score_codex":0.000023174924,"about_ca_topic_score_gemma":0.0000024716765,"teacher_disagreement_score":0.503919,"about_ca_system_score_codex":0.0001238737,"about_ca_system_score_gemma":0.00007701219,"threshold_uncertainty_score":0.9998685},"labels":[],"label_agreement":null},{"id":"W2330289077","doi":"10.2514/6.2014-1486","title":"Surrogate-assisted Self-accelerated Particle Swarm Optimization","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Particle swarm optimization; Surrogate model; Computer science; Mathematical optimization; Algorithm; Mathematics; Machine learning","score_opus":0.0176824750901915,"score_gpt":0.25356721563116885,"score_spread":0.23588474054097736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330289077","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":[0.0017021995,0.000008967264,0.9910049,0.0004811482,0.00026571643,0.00020066225,6.1118243e-7,0.0013627846,0.0049730027],"genre_scores_gemma":[0.27183732,0.000008547867,0.7271366,0.00042788644,0.000028660212,0.000021013564,0.0000067706965,0.000016113532,0.0005170683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985501,0.00013377317,0.00027483137,0.00047235526,0.0002448139,0.0003240993],"domain_scores_gemma":[0.9987587,0.00010105466,0.000112974005,0.0005164703,0.00035787522,0.00015295962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023660326,0.000170273,0.00016492256,0.000074284624,0.0001835217,0.0002060029,0.00046712285,0.00006575101,0.0000925715],"category_scores_gemma":[0.00011413316,0.00015895102,0.000042601052,0.0009131694,0.00002817405,0.0009905539,0.00015282448,0.000088314206,0.000152522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052636424,0.00020116052,0.00028361933,0.000004601557,0.000018753884,0.0000032954754,0.00024050655,0.967098,0.0005554704,0.01670932,0.00014385856,0.0147361485],"study_design_scores_gemma":[0.0007960976,0.0000576672,0.0009753895,0.0000036866102,0.000004430489,0.000008908609,0.000014949348,0.9852037,0.01178234,0.00014844714,0.0007875019,0.0002168883],"about_ca_topic_score_codex":0.000015480753,"about_ca_topic_score_gemma":0.0000064017813,"teacher_disagreement_score":0.2701351,"about_ca_system_score_codex":0.00008376478,"about_ca_system_score_gemma":0.0000441891,"threshold_uncertainty_score":0.64818347},"labels":[],"label_agreement":null},{"id":"W2330340987","doi":"10.2514/6.2007-1330","title":"Characterizing Global Behavior of Approximation Functions","year":2007,"lang":"en","type":"article","venue":"45th AIAA Aerospace Sciences Meeting and Exhibit","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science","score_opus":0.016788751471541758,"score_gpt":0.28049530853826926,"score_spread":0.2637065570667275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330340987","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":[0.212533,0.00010703083,0.78119576,0.00025444178,0.00039882984,0.00018940457,0.0000044829817,0.00014325943,0.0051737963],"genre_scores_gemma":[0.68358904,0.000012832754,0.3161535,0.00007239212,0.000043130054,0.000008335028,0.000001976543,0.0000044885082,0.00011427587],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983694,0.000039274673,0.00029849005,0.0005000146,0.0004093699,0.00038346992],"domain_scores_gemma":[0.99911076,0.00008932046,0.0002736719,0.00022787222,0.00016998763,0.00012841636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012213034,0.0001472315,0.00017793561,0.00011346646,0.00048647885,0.00014770421,0.00035074883,0.00005874925,0.0000039874444],"category_scores_gemma":[0.00017178552,0.00013713013,0.00004345256,0.0011828513,0.00031263632,0.00091250293,0.00018589346,0.00009044561,0.0000072853054],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000661475,0.00085458544,0.30450153,0.00017155109,0.000048936778,0.000036529433,0.009794417,0.018777987,0.2041246,0.06818798,0.00020785016,0.3932279],"study_design_scores_gemma":[0.0032513463,0.0013285792,0.34946308,0.0006262298,0.000102044156,0.00036414876,0.008970141,0.46614295,0.16380043,0.0018321153,0.0017862291,0.0023326934],"about_ca_topic_score_codex":0.00005486432,"about_ca_topic_score_gemma":0.000024603916,"teacher_disagreement_score":0.47105607,"about_ca_system_score_codex":0.000061651415,"about_ca_system_score_gemma":0.00007006638,"threshold_uncertainty_score":0.5592004},"labels":[],"label_agreement":null},{"id":"W2330813385","doi":"10.2514/6.2008-5932","title":"Progressive Validity Trust Region Optimization","year":2008,"lang":"en","type":"article","venue":"12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Trust region; Computer science; Computer security","score_opus":0.04354315104184983,"score_gpt":0.28348413629810026,"score_spread":0.23994098525625043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330813385","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":[0.0032449195,0.00018048011,0.994021,0.0006042277,0.00019162321,0.00060478656,0.00001621665,0.00042825338,0.00070850324],"genre_scores_gemma":[0.3665034,0.00076894416,0.6317618,0.00006512674,0.000063333246,0.000086247404,0.00020508269,0.00002830816,0.0005177639],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996281,0.00029933412,0.00080009573,0.0014508592,0.0006121224,0.0005565482],"domain_scores_gemma":[0.996378,0.00018437601,0.00071533147,0.0010217485,0.0013176949,0.00038281476],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00031016456,0.00054291234,0.0007244169,0.0008596505,0.001345006,0.00034331734,0.000794076,0.00024023966,0.00023829441],"category_scores_gemma":[0.00023994136,0.00052327005,0.00025272195,0.0035241814,0.0003900156,0.0025750548,0.00059241964,0.0003012294,0.000010513982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003571495,0.00021884893,0.009673138,0.000013659611,0.0001805301,0.00010702916,0.0012592402,0.98591673,0.0000113493425,0.0010229052,0.00003062242,0.0015302484],"study_design_scores_gemma":[0.00090634194,0.00014995427,0.006175751,0.000027869935,0.00023070624,0.0001276062,0.00015767095,0.9911382,0.00027234407,0.00014993887,0.000029424753,0.0006341541],"about_ca_topic_score_codex":0.000027096314,"about_ca_topic_score_gemma":0.000007825399,"teacher_disagreement_score":0.36325848,"about_ca_system_score_codex":0.0001235863,"about_ca_system_score_gemma":0.00022174585,"threshold_uncertainty_score":0.9999551},"labels":[],"label_agreement":null},{"id":"W2330990173","doi":"10.2514/6.2009-2203","title":"A Comparison of Surrogate Models in the Framework of an MDO Tool for Wing Design","year":2009,"lang":"en","type":"article","venue":"50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Wing; Surrogate model; Computer science; Engineering; Aerospace engineering; Machine learning","score_opus":0.03670067804279188,"score_gpt":0.32314822911510355,"score_spread":0.2864475510723117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330990173","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":[0.3577615,0.00010716991,0.64003676,0.00012392273,0.0005363803,0.0010930083,0.00023764637,0.00007551541,0.000028085416],"genre_scores_gemma":[0.6535315,0.000045298984,0.34610593,0.0001203359,0.000065183216,0.000026920425,0.00007411966,0.000022297752,0.0000084215135],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959814,0.00047915458,0.0012952032,0.0009730327,0.0005533058,0.00071793655],"domain_scores_gemma":[0.9969443,0.00044927938,0.00090799364,0.0010183639,0.0005353548,0.0001446837],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006614998,0.0006206639,0.0011931234,0.000281497,0.0002700955,0.00038026157,0.0016582509,0.00034118336,0.000027238717],"category_scores_gemma":[0.000296775,0.0004923242,0.00011702606,0.00044145255,0.0003475012,0.0012579557,0.00020887051,0.00034510356,4.281299e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053898833,0.00009334386,0.00023388439,0.0002557837,0.00007546854,0.000011391423,0.008310577,0.07686266,0.015000179,0.80163896,0.000013661703,0.096965104],"study_design_scores_gemma":[0.0011771617,0.00045534634,0.01953545,0.00011716724,0.000033734294,0.00003461768,0.0003548643,0.6461003,0.0121039655,0.31955615,0.000004167265,0.0005270646],"about_ca_topic_score_codex":0.00014182484,"about_ca_topic_score_gemma":0.00008845741,"teacher_disagreement_score":0.56923765,"about_ca_system_score_codex":0.00010397957,"about_ca_system_score_gemma":0.00018621708,"threshold_uncertainty_score":0.9997528},"labels":[],"label_agreement":null},{"id":"W2335942837","doi":"10.1007/s00158-016-1450-1","title":"Multi-start Space Reduction (MSSR) surrogate-based global optimization method","year":2016,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Mathematical optimization; Surrogate model; Kriging; Sequential quadratic programming; Reduction (mathematics); Robustness (evolution); Nonlinear programming; Optimization problem; Global optimization; Mathematics; Latin hypercube sampling; Solver; Computer science; Quadratic programming; Nonlinear system; Machine learning","score_opus":0.01701809646615257,"score_gpt":0.29994265240356777,"score_spread":0.28292455593741517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2335942837","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":[0.0017233421,0.00010046051,0.99375576,0.002082064,0.0009574254,0.0006800415,0.000059255508,0.00054985145,0.00009179934],"genre_scores_gemma":[0.06369692,0.00009087475,0.935587,0.00004245904,0.000110900255,0.000045629688,0.000089684094,0.000034213866,0.00030229733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971931,0.0002673293,0.00050340703,0.0011327671,0.00038783826,0.0005155546],"domain_scores_gemma":[0.99818635,0.0001112439,0.00035914526,0.00057179073,0.0005070885,0.00026438892],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026603803,0.00046340967,0.00034395498,0.00020241225,0.0006987696,0.00017570634,0.00042625237,0.00020414802,0.000059775222],"category_scores_gemma":[0.00014317184,0.00034525793,0.000100189995,0.0008836065,0.0001874895,0.0023056814,0.00034011036,0.000127388,0.000008174992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006918073,0.000041554864,0.0005167397,0.000019296946,0.000019257544,0.0000059160043,0.00019675668,0.98136854,0.0012972568,0.00129896,0.000013869571,0.015152642],"study_design_scores_gemma":[0.0028324686,0.00013209638,0.002191307,0.00005262176,0.00003280865,0.00008865525,0.00008695621,0.9919332,0.0015778592,0.0005228278,0.0000211051,0.0005280535],"about_ca_topic_score_codex":0.000023875564,"about_ca_topic_score_gemma":0.000008146751,"teacher_disagreement_score":0.06197358,"about_ca_system_score_codex":0.00034366763,"about_ca_system_score_gemma":0.00012639251,"threshold_uncertainty_score":0.9998999},"labels":[],"label_agreement":null},{"id":"W235895081","doi":"10.1007/978-3-319-15892-1_17","title":"An Interactive Evolutionary Multiobjective Optimization Method: Interactive WASF-GA","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Multi-objective optimization; Mathematical optimization; Evolutionary algorithm; Convergence (economics); Preference; Point (geometry); Optimization problem; Genetic algorithm; Evolutionary computation; Algorithm; Mathematics; Artificial intelligence; Machine learning","score_opus":0.021300083924614695,"score_gpt":0.31953897782211965,"score_spread":0.29823889389750496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W235895081","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":[0.0000025019247,0.00023773705,0.99121845,0.00024258674,0.0029163924,0.0011092512,0.000036380738,0.00048355426,0.0037531666],"genre_scores_gemma":[0.00807009,0.000033575838,0.9901322,0.0006187734,0.0005281282,0.000057283698,0.00006505999,0.00009824911,0.00039661396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9933017,0.0003113754,0.000860086,0.0031202484,0.0015312243,0.00087535963],"domain_scores_gemma":[0.9928492,0.0011160268,0.0009081969,0.0018837915,0.0027711764,0.00047163543],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011779142,0.0010245836,0.0009078643,0.0019806304,0.00043750482,0.0006086601,0.003507674,0.00052709924,0.0000750925],"category_scores_gemma":[0.00060579216,0.0010271935,0.0001936182,0.0013654667,0.00077853235,0.005487675,0.0016393972,0.0016498396,0.000063068364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041703934,0.00010526585,0.000005987726,0.000007652959,0.000029818168,0.000056358316,0.0025030249,0.78075343,0.00004773421,0.0021200716,0.0000144233645,0.21431454],"study_design_scores_gemma":[0.00075048447,0.0004361275,0.000046215817,0.0002393663,0.000017268147,0.0001624977,0.000006400524,0.94802314,0.0007093848,0.048268203,0.0002883762,0.0010525173],"about_ca_topic_score_codex":0.000062166575,"about_ca_topic_score_gemma":0.00004657823,"teacher_disagreement_score":0.213262,"about_ca_system_score_codex":0.0031026448,"about_ca_system_score_gemma":0.001383465,"threshold_uncertainty_score":0.99921787},"labels":[],"label_agreement":null},{"id":"W2376402869","doi":"","title":"Optimal design of heat pipe based on clonal selection algorithm","year":2011,"lang":"en","type":"article","venue":"Chemical Engineering(China)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Heat pipe; Heat transfer; Computer science; Selection (genetic algorithm); Loop heat pipe; Nominal Pipe Size; Work (physics); Limit (mathematics); Mathematical optimization; Algorithm; Engineering; Mechanical engineering; Mathematics; Materials science; Artificial intelligence; Thermodynamics","score_opus":0.012244836426777,"score_gpt":0.2097259187156755,"score_spread":0.1974810822888985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2376402869","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":[0.00062304124,0.0000143769885,0.9983822,0.000037383365,0.00021408587,0.00018628099,0.00000437006,0.00032435087,0.00021390841],"genre_scores_gemma":[0.120928064,0.0000017429873,0.87888604,0.00003332475,0.00005667869,0.000028916591,0.0000066615144,0.000028012295,0.000030525876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986942,0.000024394647,0.0002278329,0.000433777,0.00029932882,0.0003205135],"domain_scores_gemma":[0.9992834,0.000079261896,0.000058074056,0.000321744,0.00010674006,0.00015080432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001391391,0.00024066614,0.00023439451,0.00015923375,0.000037101447,0.00001929556,0.00045983796,0.00011871311,0.000039080387],"category_scores_gemma":[0.0001200421,0.00024385407,0.00008385288,0.00054069416,0.000043551263,0.00025584892,0.00008044842,0.0002684024,0.00001117389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023773524,0.00017171867,0.000007290244,0.000013181307,0.000013061538,0.000006397664,0.00015069252,0.961584,0.033456896,0.00047988046,0.000034557965,0.0040585925],"study_design_scores_gemma":[0.0003549304,0.00012132674,0.00019044268,0.000017236152,0.0000039964284,0.000011051684,7.421615e-7,0.68810266,0.31095573,0.000034963366,0.000032403852,0.00017449463],"about_ca_topic_score_codex":0.0000066504253,"about_ca_topic_score_gemma":1.5598548e-8,"teacher_disagreement_score":0.27749884,"about_ca_system_score_codex":0.00012356226,"about_ca_system_score_gemma":0.000058489873,"threshold_uncertainty_score":0.9944081},"labels":[],"label_agreement":null},{"id":"W2395916875","doi":"10.48550/arxiv.1605.06394","title":"Bayesian Hyperparameter Optimization for Ensemble Learning","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université Laval","funders":"","keywords":"Hyperparameter; Hyperparameter optimization; Bayesian optimization; Ensemble learning; Computer science; Machine learning; Artificial intelligence; Bayesian probability; Classifier (UML); Greedy algorithm; Algorithm; Support vector machine","score_opus":0.05067038483577868,"score_gpt":0.2047557273018738,"score_spread":0.15408534246609512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2395916875","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":[0.0004407064,0.000023216724,0.9957665,0.00011000346,0.00063944975,0.00063334405,0.000013523635,0.00045943307,0.0019138262],"genre_scores_gemma":[0.49481508,0.00009103553,0.5008265,0.00007938532,0.00009630345,0.000007788471,0.000029873036,0.000044736513,0.0040093055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779534,0.00014420589,0.00022327946,0.0013252384,0.00009101154,0.00042090364],"domain_scores_gemma":[0.9978847,0.00027325843,0.0003645725,0.00086179696,0.00045272036,0.00016293435],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019249448,0.0003525844,0.0003394994,0.00031731665,0.00026975232,0.00013962091,0.0011208458,0.0003087238,0.0000366591],"category_scores_gemma":[0.00018235271,0.00037574544,0.00022094563,0.00040995816,0.00008139643,0.0007175926,0.0010280691,0.00036729366,0.00003501466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022056875,0.00003714431,0.00013539418,0.000025596753,0.00004504911,0.000021880564,0.00008668835,0.9755057,0.000023618888,0.020834941,0.000045941015,0.00321598],"study_design_scores_gemma":[0.00079428294,0.00006146735,0.000016544807,0.0000649839,0.000032243763,0.0000037001992,0.000021762782,0.9758557,0.00026312761,0.021898067,0.00051418156,0.0004739166],"about_ca_topic_score_codex":0.0000068803797,"about_ca_topic_score_gemma":0.000002577229,"teacher_disagreement_score":0.49494,"about_ca_system_score_codex":0.0003256316,"about_ca_system_score_gemma":0.00016359649,"threshold_uncertainty_score":0.99986947},"labels":[],"label_agreement":null},{"id":"W2398485474","doi":"10.1007/978-3-319-00795-3_76","title":"On the Application of a Multi-Objective Genetic Algorithm to the LORA-Spares Problem","year":2013,"lang":"en","type":"book-chapter","venue":"Operations research proceedings","topic":"Advanced Multi-Objective Optimization Algorithms","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":"Department of National Defence; University of Waterloo","funders":"","keywords":"Computer science; Genetic algorithm; Algorithm; Machine learning","score_opus":0.05072552233507193,"score_gpt":0.33448200437743697,"score_spread":0.28375648204236503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2398485474","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":[0.000011075255,0.000109423025,0.9383593,0.0042244755,0.000068708956,0.0064276205,0.00004451835,0.00009949313,0.050655343],"genre_scores_gemma":[0.0020975566,0.00018638716,0.8141289,0.00043570387,0.00024513016,0.005360281,0.000018870682,0.00011328305,0.1774139],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964142,0.00007323262,0.000532705,0.0010316735,0.0014313865,0.00051679584],"domain_scores_gemma":[0.9933889,0.0004109804,0.0001588987,0.00089491403,0.004971752,0.00017453593],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010893482,0.00039022643,0.00032107448,0.00055958086,0.0011280751,0.0006444274,0.0023809236,0.00021526491,0.00010831214],"category_scores_gemma":[0.00054419914,0.00024519904,0.000103439284,0.0007910954,0.00040113286,0.0005023615,0.0008879848,0.0010791343,0.00095761573],"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":[0.000009983373,0.00014360677,0.0000011816257,0.000036034253,0.00009456556,0.0000014819594,0.0039596236,0.0404566,0.00044896625,0.82094896,0.00776215,0.12613685],"study_design_scores_gemma":[0.00029090364,0.0004023851,0.00004554651,0.0001772576,0.000011246702,0.0000114294735,0.00025822286,0.95133805,0.001018158,0.013756415,0.032291085,0.00039932606],"about_ca_topic_score_codex":0.00014534568,"about_ca_topic_score_gemma":0.000054859483,"teacher_disagreement_score":0.9108814,"about_ca_system_score_codex":0.0003607324,"about_ca_system_score_gemma":0.00034267828,"threshold_uncertainty_score":0.9998927},"labels":[],"label_agreement":null},{"id":"W2407184141","doi":"10.1007/s10479-016-2221-5","title":"Multiobjective variable mesh optimization","year":2016,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer 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":"University of Ottawa","funders":"Eurostars; Erasmus+; European Commission","keywords":"Mathematical optimization; Benchmark (surveying); Multi-objective optimization; Crossover; Theory of computation; Metaheuristic; Pareto principle; Evolutionary algorithm; Computer science; Population; Mathematics; Operator (biology); Algorithm; Artificial intelligence","score_opus":0.14629043189146912,"score_gpt":0.4340246466199914,"score_spread":0.2877342147285223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2407184141","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":[0.00023467201,0.000053373235,0.9906257,0.003063736,0.00009372175,0.0004146739,0.000019330097,0.000078432895,0.005416407],"genre_scores_gemma":[0.12082438,0.00034695488,0.8746156,0.00013766145,0.000058507438,0.00013759833,0.000007357337,0.000018737459,0.0038531832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997868,0.0003666897,0.0003061138,0.00044107475,0.00062343804,0.0003946626],"domain_scores_gemma":[0.99486965,0.00039410972,0.00003932123,0.000647168,0.0039301603,0.00011959269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011394625,0.00011188066,0.00015539513,0.00047729549,0.000376101,0.00012578323,0.0006864078,0.00007304909,0.00017164939],"category_scores_gemma":[0.0015811195,0.00008359284,0.000043427593,0.0015434399,0.00018926268,0.0018539013,0.00029889625,0.00013760556,0.00008213601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000140154,0.00021935173,0.000039320574,0.0000056735985,0.000031819363,0.0000025909553,0.0004625771,0.81878567,0.0051059066,0.15119314,0.00070904725,0.023430863],"study_design_scores_gemma":[0.00057015696,0.00017577296,0.00021056163,0.000046349873,0.0000012782559,0.0000048573334,0.00007065173,0.9505809,0.045303486,0.0018835124,0.0009858741,0.00016662016],"about_ca_topic_score_codex":0.00007563888,"about_ca_topic_score_gemma":0.000012195457,"teacher_disagreement_score":0.14930964,"about_ca_system_score_codex":0.000060716717,"about_ca_system_score_gemma":0.00032245295,"threshold_uncertainty_score":0.3408817},"labels":[],"label_agreement":null},{"id":"W2411403232","doi":"10.1007/s00158-016-1495-1","title":"An evaluation of constraint aggregation strategies for wing box mass minimization","year":2016,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Constraint (computer-aided design); Mathematical optimization; Minification; Computer science; Work (physics); Function (biology); Optimal design; Mathematics; Algorithm; Engineering","score_opus":0.025040273695040215,"score_gpt":0.3142377028888736,"score_spread":0.2891974291938334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2411403232","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":[0.036529623,0.000058166497,0.961733,0.00022419289,0.00032132235,0.00090018136,0.000039168226,0.000114917224,0.00007941596],"genre_scores_gemma":[0.5123286,0.000023140692,0.48747435,0.000006084093,0.000040282237,0.00004572705,0.000060578728,0.000010813882,0.000010422719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831975,0.00015010315,0.00041686124,0.0005249115,0.00037177603,0.00021660922],"domain_scores_gemma":[0.9980423,0.00012586877,0.00035568737,0.00030231403,0.0010845795,0.000089244524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038793095,0.0002105225,0.00020262967,0.00017881741,0.00030769923,0.000100173405,0.00022395005,0.000102611935,0.000019429554],"category_scores_gemma":[0.00013409788,0.00015735233,0.00004747502,0.00027095125,0.00015594148,0.0031226422,0.000060646613,0.000040792245,3.711831e-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":[0.000038605845,0.000017408127,0.00024531645,0.000026905678,0.0000146484335,2.6141964e-7,0.0009498875,0.88631004,0.011546408,0.011059336,0.0000016851934,0.089789495],"study_design_scores_gemma":[0.0019793755,0.00019305112,0.0019336938,0.000053995376,0.000048122518,0.000009108712,0.00059250795,0.97174823,0.005071669,0.018132124,0.0000017379981,0.00023640395],"about_ca_topic_score_codex":0.0000034689017,"about_ca_topic_score_gemma":0.000003884874,"teacher_disagreement_score":0.47579896,"about_ca_system_score_codex":0.00010315455,"about_ca_system_score_gemma":0.0001663669,"threshold_uncertainty_score":0.64166415},"labels":[],"label_agreement":null},{"id":"W2413296822","doi":"10.1080/00401706.2015.1115674","title":"RETRACTED: Design and Analysis of Experiments on Nonconvex Regions","year":2015,"lang":"en","type":"article","venue":"Technometrics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":14,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Scaling; Geodesic; Engineering design process; Process (computing); Metamodeling; Multidimensional scaling; Gaussian process; Mathematical optimization; Emphasis (telecommunications); Industrial engineering; Gaussian; Algorithm; Mathematics; Machine learning; Engineering; Software engineering; Mechanical engineering","score_opus":0.0923340093963908,"score_gpt":0.32333102581249307,"score_spread":0.23099701641610226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2413296822","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":[0.0015682423,0.00017401058,0.9971344,0.00006448498,0.00006995602,0.00015996111,0.0000051998018,0.00017376065,0.0006500028],"genre_scores_gemma":[0.26664492,0.0000425456,0.73314065,0.000050324645,0.000005037835,0.000011526938,0.0000033702504,0.0000073831393,0.00009425589],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896294,0.000031342166,0.00019904823,0.00030783343,0.000361339,0.00013749186],"domain_scores_gemma":[0.99874824,0.00018651834,0.00016573013,0.0005364755,0.0002512591,0.00011179555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033025845,0.00010394397,0.00021952017,0.0029408394,0.000040645788,0.000030890613,0.0004046603,0.00009272237,0.000003055899],"category_scores_gemma":[0.0006417839,0.00010045751,0.00004106696,0.013879332,0.000062574385,0.00026110004,0.00014583363,0.00011997995,0.0000036180425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011246581,0.0027413655,0.016229387,0.000030728446,0.0018285469,0.000099344965,0.005242418,0.41119567,0.002584209,0.17207251,0.002978453,0.3848849],"study_design_scores_gemma":[0.0005452376,0.0002887433,0.0028245414,0.0000067105416,0.0000697773,0.000002714471,0.00010403011,0.98427534,0.009750308,0.0014688435,0.0004505555,0.00021316868],"about_ca_topic_score_codex":0.0000051690104,"about_ca_topic_score_gemma":1.712032e-7,"teacher_disagreement_score":0.5730797,"about_ca_system_score_codex":0.00010701507,"about_ca_system_score_gemma":0.000036916772,"threshold_uncertainty_score":0.66685593},"labels":[],"label_agreement":null},{"id":"W2460058546","doi":"10.1007/s00158-016-1489-z","title":"Numerical investigation of non-hierarchical coordination for distributed multidisciplinary design optimization with fixed computational budget","year":2016,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada); McGill University; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multidisciplinary design optimization; Multidisciplinary approach; Engineering design process; Computer science; Mathematical optimization; Industrial engineering; Control engineering; Engineering; Mathematics; Mechanical engineering; Sociology","score_opus":0.01323132199692577,"score_gpt":0.2644054959547941,"score_spread":0.25117417395786834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460058546","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":[0.0065125185,0.000019656243,0.99020517,0.0013628254,0.00019505924,0.0014205117,0.0001003679,0.00017414898,0.000009746605],"genre_scores_gemma":[0.35113534,0.000011822807,0.648209,0.000013562739,0.000042527536,0.00010733469,0.00041838796,0.000026740427,0.000035321493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976067,0.0001755115,0.00062578806,0.00081640005,0.00040806975,0.00036756071],"domain_scores_gemma":[0.9974376,0.00062296516,0.0005125612,0.00031238608,0.00090646854,0.0002080021],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022801786,0.0003835251,0.0003940938,0.00027130396,0.00057145365,0.00007912942,0.00033595672,0.00016426881,0.000012917213],"category_scores_gemma":[0.00016223275,0.00026849887,0.00007436252,0.0007417094,0.0003395879,0.001721358,0.0002020343,0.00011278845,0.0000010515869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033033793,0.00003911455,0.0017713489,0.000052063206,0.000035972418,0.0000019999723,0.0005186839,0.9908592,0.0009384079,0.00082050305,0.000018923078,0.0046134456],"study_design_scores_gemma":[0.00364542,0.00063220423,0.014839859,0.00009749386,0.00003942324,0.000037764166,0.00006016643,0.97575766,0.0015683435,0.0028996586,0.0000014080867,0.00042061304],"about_ca_topic_score_codex":0.000005280319,"about_ca_topic_score_gemma":0.0000010136824,"teacher_disagreement_score":0.34462282,"about_ca_system_score_codex":0.00014063886,"about_ca_system_score_gemma":0.00016228069,"threshold_uncertainty_score":0.9999767},"labels":[],"label_agreement":null},{"id":"W2460675832","doi":"10.7939/r34p5w","title":"Regularization in reinforcement learning","year":2011,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Markov decision process; Minimax; Regularization (linguistics); Mathematical optimization; Bellman equation; Estimator; Computer science; Mathematics; Algorithm; Markov process; Artificial intelligence","score_opus":0.013995230567726578,"score_gpt":0.22862049801719714,"score_spread":0.21462526744947055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460675832","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":[0.00087336684,0.00010832685,0.9926488,0.00031280288,0.00010485789,0.0005130943,6.1746016e-7,0.00078949006,0.00464865],"genre_scores_gemma":[0.32652286,0.00007457582,0.67030114,0.0005284136,0.000024651432,0.00022344691,0.000009062836,0.000030735002,0.0022851492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807316,0.00013898115,0.00044164504,0.0004992621,0.00030295158,0.0005439743],"domain_scores_gemma":[0.9987115,0.000043735545,0.0002307174,0.0007022741,0.00014568001,0.00016606238],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00049361255,0.00024045384,0.00022338904,0.0005768396,0.00016587826,0.00010097915,0.0007277058,0.00016188977,0.000035403886],"category_scores_gemma":[0.00022785187,0.00026398315,0.00005828743,0.0011033381,0.000048394384,0.0011125535,0.00034433458,0.00037398058,0.000023632598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000454328,0.00025552537,0.0174304,0.000016776772,0.000023092249,0.00010865531,0.003137685,0.5921095,0.0021289054,0.3299554,0.00008623138,0.054702394],"study_design_scores_gemma":[0.0004969328,0.00012712569,0.014172732,0.00002877859,0.0000034876787,0.00003284027,0.000057609777,0.96699023,0.010159233,0.007237183,0.0003786355,0.00031523244],"about_ca_topic_score_codex":0.0011093081,"about_ca_topic_score_gemma":0.00024059664,"teacher_disagreement_score":0.3748807,"about_ca_system_score_codex":0.0004943458,"about_ca_system_score_gemma":0.0001257295,"threshold_uncertainty_score":0.9999812},"labels":[],"label_agreement":null},{"id":"W2464411218","doi":"10.1007/s11227-016-1806-8","title":"A novel fruit fly framework for multi-objective shape design of tubular linear synchronous motor","year":2016,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Initialization; Benchmark (surveying); Computer science; Convergence (economics); Point (geometry); Feature (linguistics); Mathematical optimization; Population; Optimization algorithm; Interior point method; Algorithm; Mathematics","score_opus":0.04898033443481277,"score_gpt":0.30189309775653383,"score_spread":0.2529127633217211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2464411218","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":[0.0074626263,0.00028543078,0.9908703,0.00031745358,0.0004961596,0.00050719443,0.000009811409,0.0000464453,0.0000045909765],"genre_scores_gemma":[0.21911308,0.000037939106,0.78041947,0.00010123109,0.00028046675,0.0000037757666,1.4918824e-7,0.000029056806,0.000014829021],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997936,0.00020693798,0.000781727,0.00026636667,0.00041032702,0.00039863563],"domain_scores_gemma":[0.9948607,0.002809072,0.0006756342,0.00042257132,0.0011117684,0.00012021815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017194335,0.00024274628,0.0004539652,0.00020821443,0.00024210374,0.000037811034,0.0013327723,0.00011496819,0.000010404487],"category_scores_gemma":[0.0014646915,0.0001441875,0.0001986084,0.00038991752,0.00014414468,0.00072596327,0.0002634382,0.0002951444,0.0000055517685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000497024,0.0009556719,0.00024054144,0.00008024469,0.00059778,0.000029658651,0.016485991,0.42355233,0.3622454,0.006244125,0.00004823098,0.189023],"study_design_scores_gemma":[0.0019263459,0.00061667344,0.00042551255,0.0004141668,0.000040721698,0.00024923368,0.00025538704,0.9773001,0.01702105,0.00149567,0.000029443463,0.00022567266],"about_ca_topic_score_codex":0.0000063390935,"about_ca_topic_score_gemma":4.4284477e-7,"teacher_disagreement_score":0.5537478,"about_ca_system_score_codex":0.00018569376,"about_ca_system_score_gemma":0.00021887408,"threshold_uncertainty_score":0.58797956},"labels":[],"label_agreement":null},{"id":"W2468065612","doi":"10.1007/978-3-319-50349-3_3","title":"MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":61,"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; University of British Columbia","funders":"","keywords":"Configurator; Computer science; Algorithm; Set (abstract data type); State (computer science); Range (aeronautics)","score_opus":0.015865622772823666,"score_gpt":0.27262503268348987,"score_spread":0.2567594099106662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2468065612","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":[0.0000021150722,0.00033422123,0.9932662,0.00040152468,0.0025048659,0.0011618219,0.000025343392,0.00065405184,0.0016498585],"genre_scores_gemma":[0.0031750728,0.000074243035,0.9941687,0.0010832397,0.00050755334,0.000060747683,0.0000066138537,0.00008379782,0.00084001175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9938564,0.00010811283,0.0009236081,0.0026483773,0.0014229371,0.0010405978],"domain_scores_gemma":[0.9948961,0.0013620983,0.0007647983,0.00173182,0.0009226483,0.00032255036],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007865867,0.0009895905,0.00090555777,0.0012840277,0.00047828013,0.00068852457,0.0033484858,0.0007131639,0.0000610946],"category_scores_gemma":[0.0005848541,0.00084714056,0.00021643713,0.0010911706,0.0011458639,0.001726323,0.0011283907,0.0012150616,0.00027797962],"study_design_candidate":"design_other","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":[0.000002879838,0.00005375985,0.0000044337316,0.000019171503,0.000025392917,0.00006652917,0.0010084957,0.03224158,0.000061953324,0.017950283,0.000003755559,0.9485618],"study_design_scores_gemma":[0.0005562349,0.00014131954,0.0000740208,0.000685915,0.000009980492,0.00007459546,5.3103906e-7,0.82873774,0.0012003062,0.1674112,0.00019700454,0.0009111748],"about_ca_topic_score_codex":0.000010056097,"about_ca_topic_score_gemma":0.000013033618,"teacher_disagreement_score":0.9476506,"about_ca_system_score_codex":0.0011533117,"about_ca_system_score_gemma":0.0008147899,"threshold_uncertainty_score":0.99939793},"labels":[],"label_agreement":null},{"id":"W2475118633","doi":"10.4236/ojs.2016.63045","title":"Inverse Problem for a Time-Series Valued Computer Simulator via Scalarization","year":2016,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Acadia University","keywords":"Computer science; Inverse; Focus (optics); Set (abstract data type); Simulation; Series (stratigraphy); Inverse problem; Mathematical optimization; Algorithm; Scalar (mathematics); Applied mathematics; Mathematics; Mathematical analysis","score_opus":0.01456073951975799,"score_gpt":0.2751931832559599,"score_spread":0.26063244373620187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2475118633","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":[0.000017431285,0.000006143502,0.99867374,0.00040001638,0.0003116386,0.00043377982,0.00007380971,0.000016543161,0.00006687708],"genre_scores_gemma":[0.0008013235,0.000012680993,0.99803215,0.00017167,0.000103487255,0.000006762987,0.000005009189,0.000019021829,0.0008478751],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988117,0.00007678424,0.00047761854,0.00019415953,0.0002529109,0.00018678681],"domain_scores_gemma":[0.9975939,0.00027871417,0.00061744166,0.00020450041,0.0011727982,0.00013267428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038538803,0.00013228568,0.00025504272,0.00010076573,0.00012620607,0.00022993225,0.0008035872,0.000043415253,0.000047770744],"category_scores_gemma":[0.00021279228,0.000094102594,0.00004123245,0.00018893165,0.00006558797,0.0018609118,0.00024974457,0.000066758475,0.000027418608],"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":[0.00046060135,0.00048426355,0.000372879,0.00007540029,0.00028301415,0.00010842154,0.0013419349,0.16506216,0.004601406,0.120005675,0.025181383,0.68202287],"study_design_scores_gemma":[0.002884243,0.0006870885,0.0001911755,0.000113056165,0.00002325139,0.00008055182,0.000007145042,0.9567069,0.0011152772,0.033972144,0.0039893077,0.00022986453],"about_ca_topic_score_codex":0.0000012076822,"about_ca_topic_score_gemma":0.0000014121008,"teacher_disagreement_score":0.79164475,"about_ca_system_score_codex":0.0001005563,"about_ca_system_score_gemma":0.00017684382,"threshold_uncertainty_score":0.38373923},"labels":[],"label_agreement":null},{"id":"W2479558876","doi":"10.1145/2908961.2931652","title":"Maintaining Diversity in The Bounded Pareto-Set","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Ontario Tech University","funders":"","keywords":"Multi-objective optimization; Pareto principle; Mathematical optimization; Computer science; Population; Benchmark (surveying); Optimization problem; Machine learning; Mathematics; Geography","score_opus":0.03961643472876244,"score_gpt":0.27795692221809387,"score_spread":0.23834048748933143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2479558876","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":[0.0018826536,0.000004612977,0.98760283,0.0024062688,0.00009561665,0.00010925504,0.0000012311582,0.000090281224,0.0078072487],"genre_scores_gemma":[0.82064116,0.000008481593,0.1778571,0.00097441714,0.000021393493,0.0000067752076,4.4203796e-7,0.0000032146477,0.00048703127],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992008,0.000086423075,0.00009314022,0.000233867,0.00019136952,0.00019440235],"domain_scores_gemma":[0.9993259,0.00021592883,0.000037644582,0.00034128161,0.0000473603,0.000031903095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000309576,0.00007207324,0.00006627629,0.000057437413,0.00018957468,0.000052958185,0.0007872981,0.000024189194,0.000043832748],"category_scores_gemma":[0.00009289855,0.000037892776,0.000023468627,0.00028375565,0.00005149616,0.0005522008,0.0005727979,0.00005582148,0.00006127795],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018876712,0.00013887418,0.051159475,0.0000028978664,0.00002109856,0.00012159274,0.010616492,0.002105305,0.00017171752,0.8223552,0.0014280851,0.11186038],"study_design_scores_gemma":[0.008770405,0.00038028602,0.21255565,0.00010642637,0.000012999657,0.00013065817,0.004995308,0.43630472,0.0025237717,0.3129165,0.019578325,0.0017249507],"about_ca_topic_score_codex":0.000033150354,"about_ca_topic_score_gemma":0.000066081426,"teacher_disagreement_score":0.8187585,"about_ca_system_score_codex":0.000093170966,"about_ca_system_score_gemma":0.000022775415,"threshold_uncertainty_score":0.15452226},"labels":[],"label_agreement":null},{"id":"W2512575610","doi":"10.5539/ijef.v2n3p152","title":"Using Multiobjective Algorithms to Solve the Discrete Mean-Variance Portfolio Selection","year":2010,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Benchmark (surveying); Multi-objective optimization; Maximization; Set (abstract data type); Selection (genetic algorithm); Portfolio; Variance (accounting); Minification; Computer science; Population; Integer (computer science); Evolutionary algorithm; Pareto principle; Mathematics; Economics; Artificial intelligence; Finance","score_opus":0.016374436807119485,"score_gpt":0.28027030077976783,"score_spread":0.26389586397264836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2512575610","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":[0.067386635,0.00004351164,0.9288908,0.0010831864,0.0022641812,0.000103588616,0.00000940605,0.00000684585,0.00021183607],"genre_scores_gemma":[0.34679875,0.00035110593,0.651937,0.0003759895,0.00044440012,0.0000039206316,5.3020716e-7,0.000010270524,0.000078063276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991522,0.000013024409,0.0003610905,0.00022579439,0.00010806674,0.00013984376],"domain_scores_gemma":[0.9986478,0.000075662676,0.00048756663,0.00014018685,0.0005952574,0.00005351135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030917278,0.00011712156,0.00014922097,0.00014622201,0.00012474477,0.00020671998,0.0006458164,0.000046052002,0.0000063134657],"category_scores_gemma":[0.000090776,0.00009682511,0.00006570301,0.00013143879,0.00005771859,0.0009542908,0.00015033595,0.0002717743,0.0000029431249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006684958,0.00007413871,0.00026813004,0.000001104212,0.00013405333,0.000022469249,0.0013263318,0.6924767,0.0013672466,0.1689136,0.00006184387,0.13528754],"study_design_scores_gemma":[0.000511739,0.000062322564,0.00275526,0.0000149343405,0.000005625991,0.00050660415,0.000039547704,0.97821736,0.0018389517,0.008440508,0.007455038,0.0001521309],"about_ca_topic_score_codex":0.00002761086,"about_ca_topic_score_gemma":0.000048868616,"teacher_disagreement_score":0.28574064,"about_ca_system_score_codex":0.00010673111,"about_ca_system_score_gemma":0.00012564378,"threshold_uncertainty_score":0.39484134},"labels":[],"label_agreement":null},{"id":"W2515178122","doi":"10.1007/s11590-017-1226-6","title":"Robust optimization of noisy blackbox problems using the Mesh Adaptive Direct Search algorithm","year":2018,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Computer science; Algorithm; Noise (video); Computational intelligence; Mathematical optimization; Optimization problem; Resampling; Function (biology); Mathematics; Artificial intelligence; Image (mathematics)","score_opus":0.0369056004540894,"score_gpt":0.2605318215021814,"score_spread":0.223626221048092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515178122","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":[0.00006953047,0.000037502286,0.99638575,0.001125941,0.00048637923,0.00084048614,0.000016912198,0.00022494502,0.0008125438],"genre_scores_gemma":[0.0056558675,0.000051492832,0.9929317,0.0008939611,0.00022359278,0.00003559658,0.000028920067,0.000059939845,0.00011888536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970726,0.00035986563,0.0005869003,0.0007654988,0.000708094,0.00050704606],"domain_scores_gemma":[0.997258,0.00017678304,0.00045470518,0.0008523475,0.0011373514,0.000120817276],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005681703,0.00034238183,0.00033835572,0.0003485496,0.000529771,0.00020231251,0.0009928836,0.00011713729,0.000106329426],"category_scores_gemma":[0.000107653315,0.00029161185,0.00011384627,0.0020564124,0.00054144935,0.0014765038,0.00033797752,0.00022448765,0.000012440159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011071729,0.000065633954,0.000022970607,0.000007647155,0.000051606137,0.0000025053266,0.0014397276,0.99512416,0.00034572274,0.0002779248,0.00013923849,0.0025117693],"study_design_scores_gemma":[0.0006198631,0.00010869467,0.000016635911,0.00004845812,0.00002531451,0.000016500908,0.00011998515,0.9961774,0.0024516033,0.00001193887,0.00007417619,0.0003294715],"about_ca_topic_score_codex":0.00005404594,"about_ca_topic_score_gemma":0.0000028244351,"teacher_disagreement_score":0.0055863373,"about_ca_system_score_codex":0.00023309786,"about_ca_system_score_gemma":0.0001388341,"threshold_uncertainty_score":0.9999536},"labels":[],"label_agreement":null},{"id":"W2520360696","doi":"10.1109/hpcsim.2016.7568398","title":"MultiObjective GPU design space exploration optimization","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; University of Victoria","keywords":"Pareto principle; Computer science; Multi-objective optimization; Range (aeronautics); Mathematical optimization; Pareto optimal; Power (physics); Space (punctuation); Design space exploration; Optimal design; General-purpose computing on graphics processing units; Software; Graphics; Mathematics; Engineering; Machine learning","score_opus":0.030664163677567333,"score_gpt":0.26001078807122846,"score_spread":0.22934662439366113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2520360696","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":[0.00000488537,0.000013076405,0.99469876,0.001728369,0.00029768425,0.00045488292,0.0000014160004,0.0006016056,0.002199321],"genre_scores_gemma":[0.018139616,0.000058292462,0.97895396,0.00016313174,0.00005332013,0.000082933075,0.0000019620584,0.000020005207,0.0025267662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985812,0.00015515435,0.00021366208,0.00052996894,0.00026209824,0.0002579441],"domain_scores_gemma":[0.99863076,0.00025166533,0.0001342701,0.00045807016,0.00042526948,0.00009998116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021121286,0.00017728207,0.00013307456,0.00017438238,0.0001500304,0.000098014614,0.00037554657,0.00006975015,0.000092517395],"category_scores_gemma":[0.00028734255,0.00012454343,0.000041025156,0.0005510811,0.000050473223,0.0037335027,0.0001253076,0.0000518341,0.00019089437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013404246,0.000061570754,0.000017759286,0.0000011836987,0.000011418736,0.000003493385,0.00043550975,0.93094534,0.001737877,0.021171236,0.0002631902,0.04533803],"study_design_scores_gemma":[0.00088794826,0.000083635605,0.00005837528,0.000013807881,0.0000028037703,0.000006141359,0.00005360386,0.9668509,0.02876747,0.0027858003,0.00024211212,0.00024744216],"about_ca_topic_score_codex":0.0000058228984,"about_ca_topic_score_gemma":0.0000022067054,"teacher_disagreement_score":0.04509059,"about_ca_system_score_codex":0.00019603848,"about_ca_system_score_gemma":0.00007340631,"threshold_uncertainty_score":0.50787336},"labels":[],"label_agreement":null},{"id":"W2521992880","doi":"10.1115/gt2016-56741","title":"A High-Dimensional Model Representation Guided PSO Methodology With Application on Compressor Airfoil Shape Optimization","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Metamodeling; Particle swarm optimization; Metaheuristic; Mathematical optimization; Computer science; Benchmark (surveying); Airfoil; Parallel metaheuristic; Black box; Multi-swarm optimization; Algorithm; Mathematics; Engineering; Artificial intelligence","score_opus":0.06024691598685311,"score_gpt":0.32695195456930554,"score_spread":0.2667050385824524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2521992880","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":[0.00074647984,0.000006161126,0.9950189,0.0024606455,0.00011248092,0.00051582383,0.00000638026,0.00043659253,0.0006965443],"genre_scores_gemma":[0.04453444,0.000012357604,0.9533667,0.0008434532,0.00004175242,0.00017472704,0.000025210165,0.000027236145,0.0009741334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978157,0.00022937277,0.00035983106,0.0008809006,0.00044074998,0.00027343197],"domain_scores_gemma":[0.9978351,0.00044767183,0.00027203167,0.0007745152,0.00055932417,0.000111338515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028641202,0.00023036652,0.0002501684,0.00027497867,0.00016268663,0.00004672671,0.0004630584,0.000099442404,0.00008431244],"category_scores_gemma":[0.00016283072,0.00015171985,0.000043191463,0.0006253495,0.000093651666,0.00088658545,0.0001612536,0.00008507583,0.00005915892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000641272,0.000081100436,0.000027220483,0.0000023883454,0.000016726492,0.000001634882,0.00005081816,0.91742384,0.0027235798,0.04627464,0.0001599458,0.03317395],"study_design_scores_gemma":[0.0016173087,0.00010068537,0.00016021203,0.00001647342,0.000008163268,0.00001807116,0.000004996317,0.97985053,0.013995312,0.0039599617,0.000027315662,0.00024099837],"about_ca_topic_score_codex":0.000029611761,"about_ca_topic_score_gemma":0.0000052768974,"teacher_disagreement_score":0.062426634,"about_ca_system_score_codex":0.0001395753,"about_ca_system_score_gemma":0.00007904899,"threshold_uncertainty_score":0.6186956},"labels":[],"label_agreement":null},{"id":"W2530530944","doi":"10.11159/cdsr16.123","title":"Automated Model Tuning Using A Genetic Algorithm","year":2016,"lang":"en","type":"article","venue":"Proceedings of the International Conference of Control, Dynamic systems, and Robotics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Ontario Centres of Excellence","keywords":"Computer science; Genetic algorithm; Algorithm; Machine learning","score_opus":0.02311677357971546,"score_gpt":0.2637542770978292,"score_spread":0.24063750351811378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2530530944","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":[0.004262118,0.00009637803,0.99408984,0.0004639242,0.00044373624,0.00026175613,0.000027476624,0.000090421716,0.00026432113],"genre_scores_gemma":[0.65880185,0.00004796911,0.34090626,0.000017948034,0.000018361532,0.0000057938605,2.9376707e-7,0.000009632055,0.00019186635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985661,0.000014067855,0.0005035309,0.0002862306,0.00045176462,0.00017828746],"domain_scores_gemma":[0.997335,0.00008464255,0.0006761776,0.00014091375,0.001707533,0.000055692435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021298928,0.00016585563,0.00030096047,0.00014444928,0.00007834829,0.00009963651,0.0009502939,0.00007092418,0.0000013313655],"category_scores_gemma":[0.0001661014,0.000107337066,0.00006104811,0.00014222218,0.0001556253,0.00053465174,0.00026298824,0.00007035118,4.959631e-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":[0.000024078836,0.00006918113,0.0012833959,0.000090078,0.0002034342,0.0000010402797,0.00033787452,0.8227298,0.068169944,0.098873705,0.00001510117,0.008202324],"study_design_scores_gemma":[0.0008998459,0.00002731973,0.00037633014,0.00038189755,0.000023395112,0.00004678079,0.00006736776,0.9955281,0.00024041662,0.002272437,0.0000032032904,0.00013291798],"about_ca_topic_score_codex":0.000021013038,"about_ca_topic_score_gemma":8.150547e-7,"teacher_disagreement_score":0.65453976,"about_ca_system_score_codex":0.0001081912,"about_ca_system_score_gemma":0.00012821871,"threshold_uncertainty_score":0.43770787},"labels":[],"label_agreement":null},{"id":"W2532756054","doi":"10.1109/icmmt.2004.1411647","title":"Space mapping: a novel design and modeling methodology","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Space mapping; Space (punctuation); Surrogate model; Design space exploration; Solid modeling; Systems engineering; Algorithm; Artificial intelligence; Engineering; Machine learning; Embedded system","score_opus":0.1728761670291491,"score_gpt":0.33522813430809806,"score_spread":0.16235196727894896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2532756054","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":[0.000057999914,0.00007688695,0.99684405,0.0017115724,0.00006885801,0.00015709626,1.7132439e-7,0.00020676033,0.000876611],"genre_scores_gemma":[0.007758161,0.000021170694,0.99093574,0.00055725436,0.000038175178,0.000010560101,2.1661315e-7,0.00000855578,0.0006701365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991079,0.00008789028,0.00013885231,0.00036570735,0.00009672927,0.00020294297],"domain_scores_gemma":[0.99930114,0.00024281864,0.000041996103,0.00024064272,0.00009629532,0.000077140496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000427895,0.000106337466,0.00012958831,0.00011317367,0.00008160395,0.000052068306,0.00024622903,0.000048202342,0.000010738954],"category_scores_gemma":[0.00013541653,0.00009822463,0.000018636283,0.0002446528,0.00002710193,0.00052537804,0.00018192806,0.00008314412,0.00001536232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019414038,0.000020223331,0.000002497508,0.0000011457116,0.00000584819,8.841128e-7,0.00053514517,0.9318852,0.0021269727,0.034265008,0.000018338902,0.031136798],"study_design_scores_gemma":[0.00035883326,0.000016655766,0.000007250049,0.0000026880766,0.0000013845697,0.00003713283,0.00005032456,0.9947212,0.002206789,0.0018973153,0.0005735728,0.0001268879],"about_ca_topic_score_codex":0.0000083749865,"about_ca_topic_score_gemma":0.0000017681683,"teacher_disagreement_score":0.06283597,"about_ca_system_score_codex":0.000037169473,"about_ca_system_score_gemma":0.000031453095,"threshold_uncertainty_score":0.4005484},"labels":[],"label_agreement":null},{"id":"W2533175436","doi":"10.1007/s40860-016-0030-x","title":"Optimization of sensor deployment using multi-objective evolutionary algorithms","year":2016,"lang":"en","type":"article","venue":"Journal of Reliable Intelligent Environments","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":20,"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":"Agence Universitaire de la Francophonie","keywords":"Wireless sensor network; Computer science; Cover (algebra); Evolutionary algorithm; Software deployment; Key distribution in wireless sensor networks; Position (finance); Algorithm; Point (geometry); Multi-objective optimization; Field (mathematics); Real-time computing; Mathematical optimization; Wireless; Wireless network; Computer network; Artificial intelligence; Machine learning; Mathematics; Telecommunications; Engineering","score_opus":0.02495240038548186,"score_gpt":0.27026518915702813,"score_spread":0.24531278877154628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2533175436","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":[0.0023513583,0.00040911048,0.99616194,0.000095532145,0.00066383544,0.0002545002,0.000008457626,0.00001951779,0.000035756413],"genre_scores_gemma":[0.029197866,0.0014744517,0.9686039,0.000048600225,0.00007393541,0.000004540262,0.0000010457443,0.00003302502,0.00056264334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975403,0.00011531278,0.0009408646,0.00034839098,0.0007419676,0.00031317776],"domain_scores_gemma":[0.99784344,0.00013247984,0.0012065537,0.00039889672,0.00024638994,0.00017223027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035929607,0.00024340721,0.00036621888,0.0003393563,0.000096780095,0.000022490298,0.00058176677,0.00010369665,0.00008077072],"category_scores_gemma":[0.00015393713,0.000181361,0.00017582791,0.00031358103,0.00013110491,0.001144732,0.00023446327,0.00015658778,0.000022038814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041036896,0.0004567939,0.0018085579,0.000006598758,0.000117738105,0.000025554255,0.00016917125,0.97675633,0.009739794,0.00006546487,0.000030540785,0.010782387],"study_design_scores_gemma":[0.0016783779,0.0004039587,0.0011235144,0.0002556609,0.00004986456,0.00020433191,0.00012623437,0.8977094,0.096639454,0.00024517343,0.0012340768,0.00032992344],"about_ca_topic_score_codex":0.000009128581,"about_ca_topic_score_gemma":1.5503309e-7,"teacher_disagreement_score":0.08689966,"about_ca_system_score_codex":0.0008949708,"about_ca_system_score_gemma":0.00010183909,"threshold_uncertainty_score":0.7395687},"labels":[],"label_agreement":null},{"id":"W2545690268","doi":"10.1109/mesa.2006.296978","title":"Fuzzy Modeling in Response Surface Method for Complex Computer Model Based Design Optimization","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metamodeling; Computer science; Fuzzy logic; Surrogate model; Computation; Response surface methodology; Black box; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics; Machine learning","score_opus":0.06820640544669193,"score_gpt":0.3186955791557559,"score_spread":0.250489173709064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2545690268","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":[0.000044239794,0.000009298635,0.9978959,0.00054213166,0.00007914614,0.0008950982,0.000006861228,0.00032709815,0.00020021215],"genre_scores_gemma":[0.012581805,0.0000010369126,0.9864792,0.00055126415,0.000025539073,0.00005171663,0.00002314931,0.000034175093,0.00025207963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977982,0.0004232763,0.0004597846,0.0006775729,0.00025034347,0.00039083455],"domain_scores_gemma":[0.9981913,0.00078621093,0.000114998045,0.00045271852,0.00038568475,0.00006908574],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012964526,0.00023955316,0.0002711729,0.00025524106,0.00014497172,0.0001340627,0.0005021956,0.00009748758,0.000005920465],"category_scores_gemma":[0.000069264555,0.00024698864,0.00007322583,0.0006493913,0.000021517633,0.00069915265,0.000107988206,0.000100295656,0.0000035160515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017287933,0.000100944104,0.0000029618661,0.000004694607,0.000002937345,0.0000018554831,0.000079070654,0.99353266,0.00082485273,0.0037316692,0.000118327946,0.0014271411],"study_design_scores_gemma":[0.0017645594,0.00006398337,0.0000076006804,0.000010066271,0.0000032743271,0.000002408778,0.000007969322,0.9922884,0.0005631762,0.0049710968,0.000008647565,0.00030879513],"about_ca_topic_score_codex":0.00004987848,"about_ca_topic_score_gemma":0.000009364276,"teacher_disagreement_score":0.012537565,"about_ca_system_score_codex":0.00020643417,"about_ca_system_score_gemma":0.000175038,"threshold_uncertainty_score":0.9999982},"labels":[],"label_agreement":null},{"id":"W2551080769","doi":"10.5267/j.ijiec.2016.11.002","title":"Comments on “A note on multi-objective improved teaching-learning based optimization algorithm (MO-ITLBO)”","year":2016,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Optimization algorithm; Computer science; Algorithm; Mathematical optimization; Mathematics","score_opus":0.019908308112149244,"score_gpt":0.2859615023326546,"score_spread":0.2660531942205054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2551080769","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":[0.00020275987,0.000004832842,0.9946507,0.0017042813,0.0029900118,0.00021595207,0.000024321445,0.0001492754,0.00005781773],"genre_scores_gemma":[0.21029507,0.0000064404758,0.78854495,0.00030764635,0.0007056625,0.000012652904,0.00001329087,0.00004241556,0.00007185517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978723,0.00016955912,0.0006727462,0.00032950536,0.00070382276,0.0002520633],"domain_scores_gemma":[0.99691296,0.0011057525,0.0006569385,0.00016899155,0.0009885045,0.00016687738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048395488,0.00027456775,0.00026768373,0.0008718377,0.00015837041,0.00018493325,0.00086067314,0.00013988037,0.000013823709],"category_scores_gemma":[0.001910367,0.0002263762,0.00016282746,0.00030426562,0.00003177403,0.0007670604,0.0001041252,0.0007577509,0.000014406121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004507327,0.00022305475,0.000023007822,7.2014916e-7,0.000096335905,0.000019710182,0.00011347784,0.8883471,0.00026372235,0.00060665637,0.000054142787,0.110207036],"study_design_scores_gemma":[0.0050140424,0.00046849542,0.00011360168,0.0003015621,0.00001130341,0.000031183088,0.000014191302,0.99174464,0.001246027,0.000046294714,0.0007647029,0.00024396855],"about_ca_topic_score_codex":0.000007302473,"about_ca_topic_score_gemma":2.798233e-7,"teacher_disagreement_score":0.21009232,"about_ca_system_score_codex":0.00077641674,"about_ca_system_score_gemma":0.00018724248,"threshold_uncertainty_score":0.9231354},"labels":[],"label_agreement":null},{"id":"W2559258189","doi":"10.1109/cec.2016.7744255","title":"Pareto-based many-objective optimization using knee points","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Mathematical optimization; Multi-objective optimization; Pareto principle; Computer science; Metric (unit); Particle swarm optimization; Optimization problem; Evolutionary algorithm; Point (geometry); Set (abstract data type); Mathematics; Engineering","score_opus":0.018446446833743173,"score_gpt":0.26396857241189736,"score_spread":0.2455221255781542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2559258189","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":[0.00020767334,0.000011175858,0.99591047,0.0005316723,0.00032797156,0.00030266083,0.000004687687,0.00045543537,0.0022482753],"genre_scores_gemma":[0.06409092,0.0000042695174,0.9347793,0.00051562116,0.000047022288,0.000019869753,0.0000022638221,0.00002564894,0.0005150791],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838245,0.00010183466,0.0002606099,0.0006002065,0.0003076902,0.00034723151],"domain_scores_gemma":[0.9985793,0.0001777976,0.00015616763,0.00056742766,0.0003917337,0.00012754982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016844498,0.00021144479,0.00017718869,0.0002192268,0.0001686209,0.00009161744,0.00042098365,0.00007941121,0.00017947759],"category_scores_gemma":[0.00018710167,0.00015328648,0.00006578247,0.0006020533,0.00007482128,0.0013068317,0.00016172454,0.0000655512,0.000077446355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016923785,0.00009575985,0.0009247446,0.0000043344808,0.000021781052,0.000013311605,0.000106568084,0.96248955,0.00081431965,0.011512311,0.00005652086,0.023943907],"study_design_scores_gemma":[0.0011307717,0.000057580797,0.00040831382,0.000030952673,0.000004870259,0.000009327745,0.000017233171,0.99029934,0.0068535237,0.0008112841,0.000098214776,0.00027860142],"about_ca_topic_score_codex":0.000013284964,"about_ca_topic_score_gemma":0.0000028397444,"teacher_disagreement_score":0.06388325,"about_ca_system_score_codex":0.0003000318,"about_ca_system_score_gemma":0.00013810895,"threshold_uncertainty_score":0.6250841},"labels":[],"label_agreement":null},{"id":"W2559295340","doi":"10.1109/cec.2016.7743865","title":"3D-RadVis: Visualization of Pareto front in many-objective optimization","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Visualization; Multi-objective optimization; Computer science; Mathematical optimization; Parallel coordinates; Convergence (economics); Benchmark (surveying); Pareto principle; Range (aeronautics); Process (computing); Data visualization; Mathematics; Data mining; Engineering","score_opus":0.010264703657065201,"score_gpt":0.2659576627734646,"score_spread":0.2556929591163994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2559295340","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":[0.00028181274,0.000029754401,0.995345,0.00015760417,0.00019065921,0.00033538564,0.000002949619,0.00016635141,0.0034905146],"genre_scores_gemma":[0.22662269,0.00008313833,0.77240443,0.000106335654,0.000024943189,0.000042708965,0.000005084525,0.0000203919,0.0006902598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847674,0.00011741436,0.00041545805,0.00046938358,0.00028442717,0.0002365461],"domain_scores_gemma":[0.998832,0.00013348613,0.00021697817,0.00040301072,0.00035199872,0.00006254517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020321684,0.00016342735,0.00021942663,0.0003262333,0.000042693966,0.000027635282,0.00038363042,0.00007932192,0.000120142126],"category_scores_gemma":[0.00022227244,0.0001228069,0.00003958991,0.0006187906,0.000058955335,0.0013155568,0.00015614195,0.00004362905,0.000027870357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004126019,0.00035556566,0.0066739153,0.000018827166,0.000030533756,0.000008930728,0.0021129958,0.85903984,0.0013799942,0.066832304,0.00011024434,0.06339559],"study_design_scores_gemma":[0.0012262807,0.00008874271,0.004287264,0.00006289517,0.0000031343384,0.000003439875,0.0000745156,0.9853518,0.0075605237,0.0010346321,0.000077109056,0.00022964565],"about_ca_topic_score_codex":0.000035012596,"about_ca_topic_score_gemma":0.000029293673,"teacher_disagreement_score":0.22634088,"about_ca_system_score_codex":0.00021626655,"about_ca_system_score_gemma":0.00007095203,"threshold_uncertainty_score":0.500792},"labels":[],"label_agreement":null},{"id":"W2566765582","doi":"10.1016/j.compchemeng.2016.12.011","title":"Practical optimization for cost reduction of a liquefier in an industrial air separation plant","year":2016,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"","keywords":"Particle swarm optimization; Reduction (mathematics); Process engineering; Engineering; Power station; Automotive engineering; Mathematical optimization; Mathematics; Electrical engineering","score_opus":0.03405805684388455,"score_gpt":0.29980101122934877,"score_spread":0.26574295438546425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2566765582","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":[0.0066502057,0.0000035009025,0.9920375,0.00034440384,0.00043294058,0.00039493447,0.000006224444,0.00012605099,0.000004290473],"genre_scores_gemma":[0.22310205,0.0000029688183,0.7766387,0.000016572765,0.00014704095,0.00005502346,0.000020895188,0.000013786343,0.0000029870296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990072,0.00002240129,0.0003018614,0.00033370437,0.00013551734,0.00019928934],"domain_scores_gemma":[0.99927205,0.0001999633,0.00010604283,0.00021224741,0.00012343163,0.00008628881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013895267,0.0001306156,0.00017569716,0.0001447815,0.000019508374,0.000022130487,0.00019808591,0.00012545421,0.0000021379458],"category_scores_gemma":[0.00021317578,0.00011826171,0.000037638805,0.00028655937,0.000023478604,0.0009361802,0.00007036839,0.00010581606,6.927123e-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":[0.000043173615,0.000077991135,0.000009256988,0.000009359063,0.000007169947,0.0000016251263,0.00011580887,0.94331986,0.042108923,0.0019217486,0.00007238898,0.012312705],"study_design_scores_gemma":[0.001076647,0.00006165362,0.00001390836,0.00006579397,0.0000028290417,0.000016796093,0.0000032333687,0.90921044,0.08928357,0.000032993124,0.00009191656,0.00014020565],"about_ca_topic_score_codex":0.0000013986552,"about_ca_topic_score_gemma":1.4258418e-7,"teacher_disagreement_score":0.21645184,"about_ca_system_score_codex":0.00017996672,"about_ca_system_score_gemma":0.00005661632,"threshold_uncertainty_score":0.48225728},"labels":[],"label_agreement":null},{"id":"W2573849483","doi":"10.1109/cefc.2016.7816151","title":"Surrogate-based MOEA/D for electric motor design with scarce function evaluations","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Surrogate model; Decomposition; Computer science; Mathematical optimization; Evolutionary algorithm; Function (biology); Algorithm design; Artificial intelligence; Algorithm; Machine learning; Mathematics","score_opus":0.028464992318812783,"score_gpt":0.2765254300905267,"score_spread":0.24806043777171394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573849483","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":[0.00012603533,0.000016194213,0.9974034,0.00068219396,0.00012827505,0.0010421163,0.0000035495248,0.00036899882,0.00022925447],"genre_scores_gemma":[0.13105355,0.0000028683796,0.8662314,0.0002617701,0.00003718575,0.00039969094,0.0000028722804,0.000020961117,0.0019897164],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986884,0.00009953727,0.0001671882,0.0004620387,0.00029170376,0.00029111365],"domain_scores_gemma":[0.99810815,0.0006106243,0.00010131563,0.00040601066,0.0006827172,0.000091205286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034540365,0.00015223774,0.00012063918,0.0002072693,0.00018805359,0.00006447247,0.0003041398,0.000045445187,0.000044431108],"category_scores_gemma":[0.00019308015,0.00009540403,0.000046721194,0.0006729857,0.00003130114,0.0007070786,0.000024970366,0.00003844894,0.000048880942],"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":[0.0007708246,0.0005161783,0.00051691046,0.0000224853,0.00012712623,0.0000036616646,0.00013644276,0.33355126,0.12844029,0.035402402,0.0010860922,0.49942634],"study_design_scores_gemma":[0.0022660175,0.00074536743,0.00090372236,0.000012982481,0.000015800384,0.0000028133516,0.000004327618,0.95469224,0.03899924,0.0018636301,0.00028454475,0.00020929935],"about_ca_topic_score_codex":0.0000035962094,"about_ca_topic_score_gemma":0.000004501109,"teacher_disagreement_score":0.621141,"about_ca_system_score_codex":0.00017012564,"about_ca_system_score_gemma":0.00027898265,"threshold_uncertainty_score":0.38904634},"labels":[],"label_agreement":null},{"id":"W2573944554","doi":"10.1109/cefc.2016.7816205","title":"Feature selection for facilitation of evolutionary multi-objective design optimization: Application to IPM motor design problems","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Curse of dimensionality; Feature selection; Computer science; Dimensionality reduction; Selection (genetic algorithm); Artificial intelligence; Mathematical optimization; Machine learning; Evolutionary computation; Feature (linguistics); Dimension (graph theory); Optimization problem; Mathematics; Algorithm","score_opus":0.02634091715086941,"score_gpt":0.27072385242484365,"score_spread":0.24438293527397423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573944554","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":[0.0000059700737,0.000034003937,0.9939092,0.0010427538,0.00014614132,0.0044495193,0.000029278246,0.00034632222,0.000036814883],"genre_scores_gemma":[0.0077543682,0.000013852745,0.9880477,0.0000937604,0.000048033315,0.0017779275,0.000013676213,0.00002860521,0.0022220477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804544,0.00020093682,0.0003458466,0.00077698013,0.0003183527,0.00031241606],"domain_scores_gemma":[0.9969398,0.0005874329,0.0002703611,0.00038585585,0.0016871993,0.00012935097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039678274,0.00025087586,0.00023763649,0.00034229422,0.00021370148,0.000037906244,0.00042205397,0.00015238956,0.000012702896],"category_scores_gemma":[0.0005133822,0.0002006244,0.00008143624,0.0010444897,0.000058288817,0.0012555193,0.000077721175,0.000065621054,0.000022132563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009784434,0.00013272147,0.000038481918,0.000015539601,0.000025532303,7.123563e-8,0.0003589225,0.95373905,0.021564268,0.0021428184,0.00057228404,0.021312485],"study_design_scores_gemma":[0.0012593134,0.0005358975,0.00040833122,0.000031101317,0.000009898484,0.0000057386587,0.00003618757,0.97358966,0.022332301,0.0011572797,0.0003530759,0.00028124286],"about_ca_topic_score_codex":0.000008682111,"about_ca_topic_score_gemma":0.0000038022035,"teacher_disagreement_score":0.021031242,"about_ca_system_score_codex":0.00043751815,"about_ca_system_score_gemma":0.00018503804,"threshold_uncertainty_score":0.81812257},"labels":[],"label_agreement":null},{"id":"W2577458315","doi":"10.1109/cefc.2016.7816203","title":"Projection-based objective space reduction for many-objective optimization problems: Application to an induction motor design","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Induction motor; Reduction (mathematics); Projection (relational algebra); Computer science; Space (punctuation); Mathematical optimization; Mathematics; Algorithm; Engineering; Electrical engineering","score_opus":0.023152251847343663,"score_gpt":0.2773372420602802,"score_spread":0.25418499021293656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577458315","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":[0.00013834213,0.0000063420616,0.9899636,0.0013782444,0.0005866289,0.0066846963,0.00001559735,0.0010192874,0.00020728286],"genre_scores_gemma":[0.04904675,0.0000072026273,0.9451784,0.00013430181,0.0002738488,0.004350024,0.000023529994,0.00006725475,0.0009186737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969562,0.00026119818,0.00045293855,0.0014466731,0.00040481595,0.0004781601],"domain_scores_gemma":[0.99678695,0.00021517978,0.00036568375,0.0007835121,0.0016153747,0.00023331365],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000596691,0.00037958327,0.00027267888,0.0006844906,0.00045919465,0.00017004213,0.00049253425,0.00020022514,0.000016728916],"category_scores_gemma":[0.0003447331,0.00031328725,0.00009033987,0.0014821659,0.00007545142,0.0029290805,0.00007436846,0.000115503106,0.000036053167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020025489,0.00028411328,0.000017770702,0.000013992027,0.000027242191,2.361996e-7,0.00071520853,0.8838216,0.039898798,0.005183955,0.000097139644,0.06973967],"study_design_scores_gemma":[0.0013550889,0.0010893393,0.00020632212,0.00003822672,0.000016460177,0.000013744674,0.00021743686,0.9173054,0.07700522,0.002044469,0.0002122619,0.0004960448],"about_ca_topic_score_codex":0.00007113742,"about_ca_topic_score_gemma":0.000013280528,"teacher_disagreement_score":0.069243625,"about_ca_system_score_codex":0.001106079,"about_ca_system_score_gemma":0.00032066612,"threshold_uncertainty_score":0.99993193},"labels":[],"label_agreement":null},{"id":"W2577945521","doi":"10.1007/978-3-319-47715-2_8","title":"Planning Robust Sensor Relocation Trajectories for a Mobile Robot with Evolutionary Multi-objective Optimization","year":2017,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Larus Technologies (Canada); University of Ottawa","funders":"","keywords":"Wireless sensor network; Relocation; Robot; Computer science; Trajectory; Mobile robot; Real-time computing; Simulation; Artificial intelligence; Computer network","score_opus":0.11252698568355275,"score_gpt":0.368449150476389,"score_spread":0.25592216479283625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577945521","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":[0.000001075647,0.0027883546,0.99100167,0.00010500802,0.0008895888,0.0019327656,0.00007464212,0.00020767041,0.00299922],"genre_scores_gemma":[0.002215775,0.0004874055,0.98065287,0.00006506933,0.00020200243,0.0006904822,0.0001956984,0.00008673647,0.015403936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968642,0.000055688743,0.00076574006,0.0013082448,0.0005980274,0.0004081242],"domain_scores_gemma":[0.9943837,0.0013213338,0.00093448476,0.00059968326,0.0026768693,0.000083911254],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030395377,0.0006494738,0.00070415944,0.0005174829,0.0007936411,0.00013856532,0.0008377237,0.00025146408,0.000009523805],"category_scores_gemma":[0.00046239092,0.00064510724,0.00013378564,0.00017145355,0.0006898182,0.0009455199,0.00033950977,0.00044818386,0.000013226537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007427963,0.000064853346,0.00002700877,0.00008910449,0.00020676691,0.000027938371,0.0026523778,0.9664026,3.2598908e-7,0.025582936,0.00005950249,0.0048122876],"study_design_scores_gemma":[0.0004791803,0.00032824828,0.000091246184,0.00081102026,0.000036471825,0.00005382782,0.00066843623,0.9757567,0.000020643238,0.02064404,0.00036642517,0.0007437778],"about_ca_topic_score_codex":0.000010751904,"about_ca_topic_score_gemma":0.000029040071,"teacher_disagreement_score":0.012404716,"about_ca_system_score_codex":0.00093761587,"about_ca_system_score_gemma":0.00042907093,"threshold_uncertainty_score":0.9996},"labels":[],"label_agreement":null},{"id":"W2588372621","doi":"10.1007/978-3-319-54157-0_5","title":"Automatically Configuring Multi-objective Local Search Using Multi-objective Optimisation","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Iterated local search; Leverage (statistics); Local search (optimization); Component (thermodynamics); Mathematical optimization; Guided Local Search; Context (archaeology); Machine learning; Artificial intelligence; Mathematics","score_opus":0.04043317929019216,"score_gpt":0.3127122113113464,"score_spread":0.2722790320211542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588372621","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":[0.000044911285,0.00018107354,0.9943934,0.00013871747,0.0018625045,0.0014925966,0.000019871626,0.00047198712,0.0013949289],"genre_scores_gemma":[0.064919785,0.00003071318,0.9337195,0.00032484139,0.0003191108,0.00002698961,0.000010242185,0.000111937195,0.0005368401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99246645,0.0001702233,0.0009993735,0.0032034647,0.0017703573,0.0013901256],"domain_scores_gemma":[0.9937045,0.00079994166,0.00086720847,0.0025416103,0.0016318462,0.0004548783],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0014454139,0.0011591257,0.0011262285,0.0016036843,0.0013482972,0.0013249682,0.0045761587,0.0007153856,0.00003090279],"category_scores_gemma":[0.0006094054,0.0011771638,0.0002761968,0.0005979482,0.0026144134,0.0024418808,0.002546428,0.0018525955,0.00007912412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012046811,0.00009473884,0.000030229723,0.000039464558,0.000039609105,0.00014352567,0.0022593667,0.7358192,0.00029967062,0.0018018153,4.8520684e-7,0.25945982],"study_design_scores_gemma":[0.001365231,0.00016282497,0.0004995108,0.00063750887,0.000021241927,0.00016374452,0.000003936068,0.9859241,0.004425119,0.0055109323,0.0000312877,0.0012545318],"about_ca_topic_score_codex":0.00015613578,"about_ca_topic_score_gemma":0.00014731106,"teacher_disagreement_score":0.2582053,"about_ca_system_score_codex":0.0023823695,"about_ca_system_score_gemma":0.0019453425,"threshold_uncertainty_score":0.99995184},"labels":[],"label_agreement":null},{"id":"W2591485304","doi":"10.1007/s11081-017-9370-5","title":"Locally weighted regression models for surrogate-assisted design optimization","year":2017,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Polytechnique Montréal; McGill University; Group for Research in Decision Analysis","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Hydro-Québec","keywords":"Mathematical optimization; Metric (unit); Surrogate model; Optimization problem; Computer science; Smoothing; Continuous optimization; Mathematics; Multi-swarm optimization; Statistics; Engineering","score_opus":0.027546568764959138,"score_gpt":0.25657014413999807,"score_spread":0.22902357537503892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591485304","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":[0.000012102111,0.00006833045,0.99826545,0.00026114943,0.0003499199,0.00049399637,0.0000041238022,0.00034239484,0.00020253213],"genre_scores_gemma":[0.013023105,0.00022075139,0.98632544,0.000042128646,0.000041209474,0.00008121417,0.000028360842,0.00004087242,0.00019689652],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880093,0.000027288901,0.00026228192,0.00046086757,0.00017262796,0.00027599922],"domain_scores_gemma":[0.99867564,0.00009775886,0.00021479731,0.00054501626,0.00032611532,0.00014069617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002254675,0.00023338085,0.00021357271,0.00016730349,0.00058931205,0.0004896891,0.00044753597,0.00012320517,0.00000778564],"category_scores_gemma":[0.00019363202,0.00022859826,0.000045444205,0.0001664586,0.000035469304,0.0019920205,0.00015308433,0.000088719826,0.0000010712021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000127707,0.00002183305,0.0000053444996,0.00001733441,0.000013152224,0.0000021027297,0.00008486383,0.99178785,0.00008346831,0.0024306204,0.000028179138,0.005512471],"study_design_scores_gemma":[0.001106875,0.000041120093,0.00005007888,0.00006899093,0.00000953133,0.000010193456,0.000006779076,0.99775755,0.00047538304,0.00012413308,0.00007156245,0.0002777779],"about_ca_topic_score_codex":0.0000035722642,"about_ca_topic_score_gemma":4.4104803e-7,"teacher_disagreement_score":0.013011003,"about_ca_system_score_codex":0.00006627594,"about_ca_system_score_gemma":0.000041417763,"threshold_uncertainty_score":0.9321966},"labels":[],"label_agreement":null},{"id":"W2593689740","doi":"","title":"Global Robust Optimization of Computationally Expensive Systems: A Lavel Rotor Suspended by Fluid Film Bearings","year":2016,"lang":"en","type":"article","venue":"Research Repository (Delft University of Technology)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Kriging; Mathematical optimization; Sensitivity (control systems); Rotor (electric); Computer science; Stochastic optimization; Optimization problem; Process (computing); Control theory (sociology); Engineering; Mathematics; Artificial intelligence; Machine learning","score_opus":0.019480658537162462,"score_gpt":0.25961022969451936,"score_spread":0.2401295711573569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593689740","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":[0.009374764,0.00013863297,0.9876776,0.00060765294,0.00010134489,0.00057513523,0.00003664155,0.00023113319,0.0012571017],"genre_scores_gemma":[0.5655542,0.000082062266,0.43247414,0.000004908734,0.000016491958,0.000004939576,0.000008437662,0.000014304241,0.0018405018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978189,0.00019300845,0.0002965602,0.0005779223,0.0007481102,0.00036551675],"domain_scores_gemma":[0.99626946,0.00018822563,0.00031658998,0.00063342904,0.0024787518,0.00011354336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003363137,0.0001532458,0.00030132098,0.0005873792,0.00031444937,0.00003086559,0.0013702466,0.0002461577,0.000010787397],"category_scores_gemma":[0.00028391188,0.00015202221,0.00007149854,0.0013673528,0.00077010633,0.00067906326,0.00079559407,0.00016501576,0.000006948533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019098702,0.00051168376,0.0022768434,0.000108266104,0.0002401696,0.00020987309,0.00030747236,0.8871249,0.04256105,0.058918897,0.004559547,0.0029903087],"study_design_scores_gemma":[0.0025123323,0.0005274496,0.00084976794,0.00030187643,0.000015827054,0.000105980034,0.0015913477,0.96700454,0.025114976,0.00063306466,0.00097147416,0.00037139232],"about_ca_topic_score_codex":0.00019112071,"about_ca_topic_score_gemma":0.000003303543,"teacher_disagreement_score":0.55617946,"about_ca_system_score_codex":0.0005756574,"about_ca_system_score_gemma":0.00032792,"threshold_uncertainty_score":0.6199286},"labels":[],"label_agreement":null},{"id":"W2602717365","doi":"","title":"Multiobjective Optimization Through a Series of Single-Objective Formulations","year":2007,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Multi-objective optimization; Mathematical optimization; Pareto principle; Series (stratigraphy); Mathematics; Disjoint sets; Optimization problem; Property (philosophy); Pareto analysis","score_opus":0.013955102962833931,"score_gpt":0.2527729620857711,"score_spread":0.23881785912293718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602717365","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":[0.0016199618,0.00020253011,0.9931637,0.00046064745,0.00020021448,0.0010642405,0.00002989092,0.00091112946,0.0023476868],"genre_scores_gemma":[0.2755155,0.000043484364,0.7236527,0.00030479702,0.000054739223,0.00012063619,0.000018909506,0.0000445602,0.00024465277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99724704,0.000120177836,0.0007575625,0.00066721067,0.00049283885,0.00071518216],"domain_scores_gemma":[0.99706554,0.0003120392,0.00059995666,0.0009449042,0.00088671147,0.00019084159],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005954535,0.0003892779,0.0004251136,0.00060622103,0.00035438323,0.00012742277,0.0007325159,0.0002681084,0.00001552188],"category_scores_gemma":[0.00056714565,0.00041868392,0.0001834179,0.0019452902,0.00018766863,0.0031096016,0.00034386886,0.0003042458,0.0000052232663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012699909,0.00056368636,0.0020498026,0.000021078196,0.00007985399,0.000026673382,0.0036564295,0.85470325,0.008928001,0.11375916,0.000046529152,0.016038511],"study_design_scores_gemma":[0.0009812306,0.00037619882,0.008608057,0.000051457617,0.00002397694,0.00013361292,0.00045306954,0.8663152,0.113064215,0.009216433,0.00021496954,0.00056158367],"about_ca_topic_score_codex":0.001122983,"about_ca_topic_score_gemma":0.0008722925,"teacher_disagreement_score":0.27389553,"about_ca_system_score_codex":0.0008542103,"about_ca_system_score_gemma":0.00021851322,"threshold_uncertainty_score":0.9998265},"labels":[],"label_agreement":null},{"id":"W2605533099","doi":"10.1016/j.swevo.2017.04.005","title":"Micro-time variant multi-objective particle swarm optimization (micro-TVMOPSO) of a solar thermal combisystem","year":2017,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Economic Affairs; Concordia University","keywords":"Benchmark (surveying); Computer science; Multi-objective optimization; Mathematical optimization; Pareto principle; Particle swarm optimization; Evolutionary algorithm; Multi-swarm optimization; Metaheuristic; Optimization problem; Population; Engineering optimization; Algorithm; Artificial intelligence; Machine learning; Mathematics","score_opus":0.01362871856067218,"score_gpt":0.2526617303293274,"score_spread":0.23903301176865524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605533099","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":[0.056454085,0.00024028776,0.9416961,0.0002931227,0.00038173658,0.0005311916,0.000025763196,0.0001660996,0.00021163613],"genre_scores_gemma":[0.5594384,0.000023051813,0.44026342,0.000040765975,0.00004686926,0.000020627585,0.000028664164,0.000018475364,0.00011971154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804306,0.0001993122,0.0004919197,0.00061869033,0.00030923984,0.0003377994],"domain_scores_gemma":[0.9979518,0.00013341194,0.0006270033,0.0005298989,0.00062468945,0.00013316495],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033727073,0.00026457256,0.00033568,0.00014058259,0.00092321285,0.00020118222,0.00051936216,0.00012171855,0.000008979341],"category_scores_gemma":[0.0001308267,0.00027450864,0.00008660372,0.00023529308,0.00024401481,0.0016710443,0.00037381175,0.00014705861,0.0000284727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006554544,0.00037990516,0.0017940952,0.000045744247,0.00008014115,0.000019241312,0.0016769676,0.9822163,0.0067397677,0.0009952205,0.000057644378,0.0059294426],"study_design_scores_gemma":[0.0019376777,0.00013436298,0.031382542,0.000056778765,0.000022798178,0.00006720157,0.00008889949,0.9594929,0.005824422,0.000681961,0.000015829402,0.00029459473],"about_ca_topic_score_codex":0.000074185395,"about_ca_topic_score_gemma":0.0000023959983,"teacher_disagreement_score":0.50298434,"about_ca_system_score_codex":0.0001485756,"about_ca_system_score_gemma":0.00014038106,"threshold_uncertainty_score":0.99997073},"labels":[],"label_agreement":null},{"id":"W2606049180","doi":"10.1007/978-3-319-57351-9_16","title":"A Worst-Case Analysis of Constraint-Based Algorithms for Exact Multi-objective Combinatorial Optimization","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Solver; Computer science; Constraint (computer-aided design); Benchmark (surveying); Mathematical optimization; Constraint satisfaction problem; Constraint satisfaction; Algorithm; Linear subspace; Constraint programming; Combinatorial optimization; Mathematics; Artificial intelligence","score_opus":0.027814252064999427,"score_gpt":0.29980466181435933,"score_spread":0.2719904097493599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606049180","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":[0.000004783646,0.00009108019,0.99530345,0.0001003097,0.0024554445,0.0014432796,0.00015493257,0.0001564583,0.00029025666],"genre_scores_gemma":[0.023430122,0.0000111714135,0.975932,0.00017992353,0.00019756223,0.00005762164,0.000060512768,0.000055694338,0.00007538511],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951363,0.00007034672,0.00094795745,0.0021866919,0.00093989156,0.0007187936],"domain_scores_gemma":[0.9928394,0.0014309622,0.0015167425,0.002112784,0.0018523165,0.00024776996],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011670887,0.000778003,0.001349327,0.0027995317,0.0006323298,0.0005156322,0.0027667377,0.00050978165,0.00001882467],"category_scores_gemma":[0.0008176649,0.00078767556,0.000520934,0.0012728929,0.0016254612,0.00097319495,0.00067489693,0.00060250453,0.000002351037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002274481,0.0001081684,0.000019140081,0.000026017775,0.00018343586,0.00015183148,0.00039206987,0.8676425,0.000013118083,0.00443993,8.316653e-7,0.12700017],"study_design_scores_gemma":[0.0019585534,0.00026757226,0.000027246997,0.00016572083,0.00021673748,0.000065119384,9.032852e-7,0.9870065,0.00092938927,0.008552771,0.00003077467,0.00077870156],"about_ca_topic_score_codex":0.000056767258,"about_ca_topic_score_gemma":0.00008397803,"teacher_disagreement_score":0.12622146,"about_ca_system_score_codex":0.0006113024,"about_ca_system_score_gemma":0.0011674796,"threshold_uncertainty_score":0.9994574},"labels":[],"label_agreement":null},{"id":"W2610186569","doi":"10.1155/2017/2034907","title":"Pareto Optimization of a Half Car Passive Suspension Model Using a Novel Multiobjective Heat Transfer Search Algorithm","year":2017,"lang":"en","type":"article","venue":"Modelling and Simulation in Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University; Simon Fraser University","funders":"","keywords":"Sorting; Multi-objective optimization; Mathematical optimization; Genetic algorithm; Pareto principle; Metaheuristic; Solution set; Computer science; Set (abstract data type); Algorithm; Mathematics","score_opus":0.03991106072584448,"score_gpt":0.28435167597690086,"score_spread":0.24444061525105637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610186569","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":[0.030441493,0.000035838995,0.96904874,0.000016908614,0.00009186316,0.00028493666,0.0000069029907,0.00006293117,0.000010375881],"genre_scores_gemma":[0.52111435,0.000015976824,0.47882986,0.0000026986484,0.000012319946,0.000004924558,0.0000027544595,0.000014301692,0.0000028273776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987623,0.000020627906,0.00032324172,0.00041382888,0.00024126012,0.00023873964],"domain_scores_gemma":[0.9991056,0.000105492676,0.000044634035,0.0003599554,0.00031211504,0.00007225217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018218953,0.00018761383,0.00025066032,0.00027871798,0.00022843257,0.0001082297,0.00021401398,0.000105871826,7.200601e-7],"category_scores_gemma":[0.000051686235,0.000209658,0.000044125274,0.00018017268,0.000038039016,0.0010395573,0.000086826454,0.00017100362,1.5038829e-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":[0.000008042164,0.000031952084,0.000036425663,0.000020847228,0.000009031605,0.0000017531274,0.0017284508,0.9955784,0.0012700424,0.00028973425,9.273151e-9,0.0010253093],"study_design_scores_gemma":[0.00094957784,0.000018129998,0.000037577134,0.00010987037,0.0000066125267,0.0000027187432,0.00003842958,0.99667615,0.0018743973,0.000074702,3.898997e-7,0.00021146712],"about_ca_topic_score_codex":0.00016546322,"about_ca_topic_score_gemma":0.0000035671637,"teacher_disagreement_score":0.49067283,"about_ca_system_score_codex":0.0001250215,"about_ca_system_score_gemma":0.000044630364,"threshold_uncertainty_score":0.8549605},"labels":[],"label_agreement":null},{"id":"W2615441104","doi":"","title":"Modeling an Augmented Lagrangian for Improved Blackbox Constrained Optimization","year":2014,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Booth University College","funders":"","keywords":"Augmented Lagrangian method; Mathematical optimization; Computer science; Heuristics; Benchmark (surveying); Bottleneck; Context (archaeology); Lagrangian relaxation; Optimization problem; Sensitivity (control systems); Mathematics","score_opus":0.010613832243558068,"score_gpt":0.24324455807468034,"score_spread":0.23263072583112226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615441104","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":[0.0005799507,0.000054573324,0.99420685,0.0016983127,0.00018740611,0.0014513587,0.000035395668,0.0016088422,0.00017729995],"genre_scores_gemma":[0.12647931,0.000024994868,0.8709537,0.0015862953,0.00011637699,0.00050886057,0.00010078534,0.00007082356,0.00015885555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974467,0.00017661644,0.0005451277,0.0008312962,0.00024599786,0.0007542706],"domain_scores_gemma":[0.9976255,0.000136037,0.00023893516,0.001088595,0.0005235662,0.00038736043],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000804123,0.00039214452,0.00037770355,0.00043784638,0.0004020896,0.00035606066,0.0009622298,0.00024994684,0.000010138004],"category_scores_gemma":[0.00042997918,0.00042123802,0.00015455093,0.0006615381,0.00007826227,0.0014601699,0.00018685877,0.00023609861,0.000003670905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039196835,0.00014488696,0.000030825755,0.000011468261,0.000022144419,0.0000014438242,0.00018189517,0.9372113,0.0032254008,0.030363452,0.000028781984,0.028739246],"study_design_scores_gemma":[0.001469155,0.00026131203,0.000035871275,0.00002062577,0.000018075034,0.000025429668,0.00005243049,0.99215305,0.00261117,0.0026816523,0.0001898359,0.00048136996],"about_ca_topic_score_codex":0.00048174977,"about_ca_topic_score_gemma":0.00030568708,"teacher_disagreement_score":0.12589937,"about_ca_system_score_codex":0.00033603617,"about_ca_system_score_gemma":0.00017935985,"threshold_uncertainty_score":0.9998239},"labels":[],"label_agreement":null},{"id":"W2615864160","doi":"10.1109/tmag.2017.2750901","title":"A Benchmark TEAM Problem for Multi-Objective Pareto Optimization of Electromagnetic Devices","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Benchmark (surveying); Computer science; Context (archaeology); Multi-objective optimization; Mathematical optimization; Pareto principle; Finite element method; Field (mathematics); Sensitivity (control systems); Mathematics; Machine learning; Electronic engineering; Physics; Engineering","score_opus":0.02289956013902422,"score_gpt":0.2879423914286392,"score_spread":0.265042831289615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615864160","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":[0.00025279797,0.000056367262,0.9967995,0.00016706839,0.00044481756,0.0012398288,0.000054743356,0.00014124712,0.00084361556],"genre_scores_gemma":[0.21247907,0.00008928904,0.78645444,0.0000587092,0.000024734074,0.00024065684,0.0000042222887,0.000031999232,0.00061684946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819106,0.00006826936,0.00043380816,0.00061101606,0.00030358174,0.00039227324],"domain_scores_gemma":[0.9976267,0.00017294071,0.00044672182,0.0009967617,0.0006359579,0.00012088632],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018116615,0.00028985247,0.00031207487,0.00023663032,0.00070173293,0.00019609446,0.0009734436,0.00013976575,0.000032978183],"category_scores_gemma":[0.000054892673,0.00030432304,0.00014961517,0.0002741369,0.00018542775,0.00072267774,0.000009997065,0.00020627894,0.0000065927543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006831675,0.0006279626,0.000035282537,0.00006393124,0.000057003133,0.0000017826981,0.0006573779,0.93403536,0.0015354422,0.00047718515,0.000038616265,0.062401738],"study_design_scores_gemma":[0.001794124,0.0012949905,0.0005563904,0.00004877378,0.000056130397,0.000008661806,0.00005452596,0.9691736,0.026216628,0.0003117965,0.00013605825,0.0003483127],"about_ca_topic_score_codex":0.00003257556,"about_ca_topic_score_gemma":0.00010834118,"teacher_disagreement_score":0.21222627,"about_ca_system_score_codex":0.000102259,"about_ca_system_score_gemma":0.00013918226,"threshold_uncertainty_score":0.9999409},"labels":[],"label_agreement":null},{"id":"W2618435777","doi":"10.5267/j.ijiec.2017.5.003","title":"An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty","year":2017,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Metamodeling; Robust optimization; Computer science; Context (archaeology); Probabilistic logic; Taguchi methods; Process (computing); Engineering optimization; Engineering design process; Mathematical optimization; Industrial engineering; Optimization problem; Management science; Engineering; Machine learning; Artificial intelligence; Algorithm; Mathematics","score_opus":0.24396224974893743,"score_gpt":0.40273723422049573,"score_spread":0.1587749844715583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618435777","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":[0.0026579935,0.000054348155,0.9937932,0.0009737632,0.0022372203,0.00020529756,0.0000062807417,0.000053179116,0.000018732666],"genre_scores_gemma":[0.3230501,0.000044556877,0.67653143,0.00008608026,0.00024839627,0.00000946443,0.000007945513,0.000018261559,0.0000037328793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998112,0.00019648681,0.0006809203,0.00029423786,0.000514945,0.00020140808],"domain_scores_gemma":[0.9971249,0.00053010037,0.0007800043,0.00032698194,0.0011163966,0.00012162877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008971613,0.00020172665,0.00031987127,0.0006429392,0.00013762804,0.00033361994,0.0016605627,0.00009126648,0.000008952243],"category_scores_gemma":[0.0016158163,0.00020304503,0.00008873296,0.0002144393,0.00003561699,0.0018786619,0.00008713044,0.00046526483,0.0000016280442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057765097,0.00011984086,0.000016579917,0.0000017706158,0.00006664949,0.000038849015,0.00012484066,0.9821305,0.000054647502,0.0031311421,0.00000617413,0.014251187],"study_design_scores_gemma":[0.002045341,0.00016855967,0.000113729824,0.00014978358,0.000010920736,0.00007948564,0.000035494017,0.9948051,0.00054913433,0.0018359962,0.000020843863,0.00018560953],"about_ca_topic_score_codex":0.000012613096,"about_ca_topic_score_gemma":0.0000017838978,"teacher_disagreement_score":0.3203921,"about_ca_system_score_codex":0.00032583036,"about_ca_system_score_gemma":0.0002975121,"threshold_uncertainty_score":0.82799363},"labels":[],"label_agreement":null},{"id":"W2618778734","doi":"10.1080/17445760.2017.1331439","title":"Cohort intelligence algorithm for discrete and mixed variable engineering problems","year":2017,"lang":"en","type":"article","venue":"International Journal of Parallel Emergent and Distributed Systems","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Metaheuristic; Continuous optimization; Penalty method; Mathematical optimization; Discrete optimization; Sampling (signal processing); Optimization problem; Reduction (mathematics); Algorithm; Computer science; Continuous variable; Integer programming; Nonlinear programming; Integer (computer science); Nonlinear system; Mathematics; Multi-swarm optimization","score_opus":0.017907375520779676,"score_gpt":0.2817691170762407,"score_spread":0.263861741555461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618778734","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":[0.00046759966,0.00062713766,0.995332,0.0003233914,0.0028086577,0.00028434623,0.000104043735,0.00001987533,0.000032967804],"genre_scores_gemma":[0.40429798,0.000878898,0.59390706,0.000018370121,0.00051715545,0.00006225428,0.00005492766,0.000020821359,0.00024254655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986634,0.000024402574,0.0005417045,0.00023349917,0.00035498888,0.00018197765],"domain_scores_gemma":[0.9980573,0.0000792233,0.00066128455,0.0002055737,0.0008454065,0.0001511864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004599606,0.00015577675,0.0002551857,0.00010567616,0.00018247713,0.0004903685,0.0007817436,0.000055162625,0.000002810708],"category_scores_gemma":[0.0002566874,0.00013618296,0.000063406755,0.000052772644,0.00004567592,0.00090681633,0.00021068465,0.0001079397,5.333061e-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":[0.00007094307,0.0001769204,0.0071585923,0.00010174314,0.0012474926,0.000079192534,0.00037945347,0.8804186,0.0006225705,0.056185666,0.0011009765,0.052457854],"study_design_scores_gemma":[0.00067458744,0.00007192018,0.0019486565,0.000111029796,0.000018733708,0.00016982031,0.000048512396,0.99068284,0.00007877759,0.0007026149,0.0053230906,0.0001694198],"about_ca_topic_score_codex":0.000035009725,"about_ca_topic_score_gemma":0.0000015655996,"teacher_disagreement_score":0.40383038,"about_ca_system_score_codex":0.00006798409,"about_ca_system_score_gemma":0.00004001444,"threshold_uncertainty_score":0.555338},"labels":[],"label_agreement":null},{"id":"W2622665449","doi":"","title":"Problem formulations for simulation-based design optimization using statistical surrogates and direct search","year":2014,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Benchmark (surveying); Mathematical optimization; Surrogate model; Computer science; Simulation-based optimization; Metaheuristic; Derivative-free optimization; Optimization problem; Algorithm; Multi-swarm optimization; Mathematics","score_opus":0.025338713765910258,"score_gpt":0.2866958945960644,"score_spread":0.2613571808301542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2622665449","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":[0.00017386294,0.000049803406,0.997054,0.00041928558,0.000044844954,0.0015882546,0.000038162176,0.000577706,0.00005409964],"genre_scores_gemma":[0.2707861,0.0000054518982,0.7286292,0.00021236547,0.00003200415,0.00022967998,0.000035084548,0.00003869394,0.000031428553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979625,0.00025891853,0.00039671027,0.0005696203,0.00029343815,0.00051885576],"domain_scores_gemma":[0.99636996,0.0022795745,0.00017127256,0.00048185786,0.00046688833,0.0002304596],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00088153285,0.00026259533,0.00027651738,0.0003960213,0.0005195222,0.00032517177,0.0003334861,0.00014951032,0.0000065174067],"category_scores_gemma":[0.00086327776,0.00027633327,0.00005832331,0.0005938988,0.00008573393,0.00080082,0.00011952632,0.00014783606,0.0000012016187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000285501,0.000073167736,0.0011077094,0.000021225489,0.000010792718,8.74071e-7,0.00009135688,0.96584487,0.000232878,0.02040462,0.000007598609,0.012176357],"study_design_scores_gemma":[0.0007603017,0.00014899754,0.0007778149,0.00002522635,0.000016810436,0.00000470821,0.000007270584,0.9924841,0.0017684047,0.0036341592,0.00006743152,0.00030480637],"about_ca_topic_score_codex":0.00029467238,"about_ca_topic_score_gemma":0.00006947633,"teacher_disagreement_score":0.27061224,"about_ca_system_score_codex":0.00029920536,"about_ca_system_score_gemma":0.00022704939,"threshold_uncertainty_score":0.9999689},"labels":[],"label_agreement":null},{"id":"W2625480000","doi":"10.1115/detc2002/dac-34091","title":"Adaptive Experimental Design Applied to Ergonomics Testing Procedure","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ryerson University; University of Michigan","keywords":"Computer science; Constraint (computer-aided design); Design of experiments; Sampling (signal processing); Selection (genetic algorithm); Artifact (error); Variety (cybernetics); Sample (material); Artificial intelligence; Machine learning; Mathematical optimization; Mathematics; Computer vision; Statistics","score_opus":0.06754711600764095,"score_gpt":0.2556310382059046,"score_spread":0.18808392219826364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2625480000","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":[0.00018852306,0.000024690642,0.98327166,0.00007343242,0.000085830645,0.00052851334,5.2198993e-7,0.00038002533,0.0154468],"genre_scores_gemma":[0.2077342,5.781367e-7,0.7909516,0.0005656949,0.000034644905,0.00009354991,2.2193801e-7,0.000014216397,0.00060528296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893284,0.000019052775,0.00016294137,0.00048050424,0.00013965742,0.00026502323],"domain_scores_gemma":[0.99932426,0.00010674124,0.000058308204,0.0002802085,0.00009007596,0.00014039365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000068224705,0.00015907595,0.00012310184,0.000089425004,0.00013643528,0.00008573213,0.0004490447,0.00003739384,0.000052564872],"category_scores_gemma":[0.000068097805,0.00015644351,0.000019735888,0.000535242,0.000022506705,0.00036741907,0.00021346215,0.000075950826,0.00028631432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026802612,0.00031121576,0.00001725114,0.0000021921046,0.000018242838,0.000009824508,0.0035624097,0.8916524,0.011298415,0.021150906,0.0015274123,0.070422895],"study_design_scores_gemma":[0.00030222847,0.00012287246,0.000053865246,0.000004303023,9.5198845e-7,0.000010083655,0.00020354312,0.9562303,0.04264308,0.00011775525,0.00008181646,0.00022921218],"about_ca_topic_score_codex":0.0000024502385,"about_ca_topic_score_gemma":4.7174987e-7,"teacher_disagreement_score":0.20754567,"about_ca_system_score_codex":0.00014826767,"about_ca_system_score_gemma":0.000025234522,"threshold_uncertainty_score":0.6379581},"labels":[],"label_agreement":null},{"id":"W2626235566","doi":"10.1007/s00521-017-3049-x","title":"Multi-objective sine-cosine algorithm (MO-SCA) for multi-objective engineering design problems","year":2017,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":145,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sorting; Sine; Benchmark (surveying); Trigonometric functions; Algorithm; Multi-objective optimization; Mathematical optimization; Set (abstract data type); Mathematics; Pareto principle; Computer science; Discrete cosine transform; Artificial intelligence","score_opus":0.04358581292389558,"score_gpt":0.3086182583295261,"score_spread":0.26503244540563053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626235566","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":[0.00023053425,0.00014312293,0.99636835,0.00020117081,0.00018413813,0.0022865457,0.00003462496,0.0005233025,0.000028225044],"genre_scores_gemma":[0.10124102,0.000015959438,0.89769197,0.000086014734,0.00021308083,0.0005569246,0.000013615903,0.00004782328,0.00013359262],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978232,0.00005525826,0.00037905812,0.0010268985,0.00019215565,0.00052346534],"domain_scores_gemma":[0.99773383,0.0003766891,0.00040999887,0.00076602394,0.00052208477,0.00019137493],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0003101698,0.00037902224,0.0003503394,0.00016165648,0.0017996852,0.0005355645,0.00094368117,0.0001153345,6.049575e-7],"category_scores_gemma":[0.00019616281,0.00038798997,0.00010271649,0.00029380448,0.0001417678,0.00068589946,0.00046366954,0.0003006563,0.000007800081],"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":[0.000012192777,0.00044156794,0.00019380735,0.00007187863,0.00009276934,0.00000393623,0.0012883571,0.49421814,0.005715275,0.0028609743,0.000044314977,0.49505678],"study_design_scores_gemma":[0.0015507657,0.00009363986,0.0040419823,0.000044343833,0.000017325945,0.000032811793,0.00003897483,0.99119365,0.001867719,0.00026914242,0.0004179919,0.00043165206],"about_ca_topic_score_codex":0.000036004134,"about_ca_topic_score_gemma":0.0000042478428,"teacher_disagreement_score":0.4969755,"about_ca_system_score_codex":0.00010441531,"about_ca_system_score_gemma":0.00005170826,"threshold_uncertainty_score":0.9998572},"labels":[],"label_agreement":null},{"id":"W2649938423","doi":"10.1016/j.jcde.2017.06.003","title":"∊-constraint heat transfer search (∊-HTS) algorithm for solving multi-objective engineering design problems","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Ministry of Economic Affairs","keywords":"Benchmark (surveying); Mathematical optimization; Multi-objective optimization; Reducer; Pareto principle; Engineering design process; Algorithm; Engineering optimization; Truss; Computer science; Optimization problem; Engineering; Mathematics; Structural engineering; Mechanical engineering","score_opus":0.03419872405366283,"score_gpt":0.26749690153505484,"score_spread":0.233298177481392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2649938423","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":[0.00028146556,0.00022941227,0.99839866,0.00011870977,0.00041011465,0.00049041386,0.0000044542953,0.000062828316,0.0000039641664],"genre_scores_gemma":[0.09287599,0.00003646509,0.90687186,0.00001922358,0.0001260471,0.000029371524,0.0000010340267,0.000030859115,0.000009160662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985471,0.000036887053,0.00045942471,0.00027644634,0.00034261495,0.00033756968],"domain_scores_gemma":[0.99820423,0.00070780906,0.00008588949,0.00017160756,0.00063350867,0.00019698241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009019871,0.00024400574,0.00033514985,0.0003192921,0.00032000898,0.00040983796,0.0004990503,0.00007815185,0.0000019801403],"category_scores_gemma":[0.00020422724,0.00023950887,0.00011704284,0.00012125748,0.00004863552,0.0012969654,0.00006560523,0.00028555264,9.036534e-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":[0.000010134992,0.00003823235,0.000004299417,0.0000307129,0.00008660577,0.000019641902,0.0005704385,0.9665164,0.0022608098,0.0007670972,0.0000058620512,0.029689789],"study_design_scores_gemma":[0.001673904,0.00019812942,0.00081157783,0.0001487576,0.00001534091,0.00029181215,0.000019906918,0.99317145,0.002894311,0.0004871987,0.00002914825,0.0002584849],"about_ca_topic_score_codex":0.0000022273011,"about_ca_topic_score_gemma":8.6713804e-8,"teacher_disagreement_score":0.09259453,"about_ca_system_score_codex":0.00012795639,"about_ca_system_score_gemma":0.00016033168,"threshold_uncertainty_score":0.9766888},"labels":[],"label_agreement":null},{"id":"W2667143831","doi":"10.1109/ccece.2017.7946703","title":"FIR filter design using Multiobjective Artificial Bee Colony algorithm","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Pruning; Multi-objective optimization; Mathematical optimization; Finite impulse response; Stopband; Evolutionary algorithm; Computer science; Passband; Algorithm; Artificial bee colony algorithm; Mathematics; Engineering; Band-pass filter; Electronic engineering","score_opus":0.07647161330285383,"score_gpt":0.3284302122035312,"score_spread":0.25195859890067734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2667143831","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":[0.00018772057,0.000013087719,0.9965507,0.0002749265,0.0006873252,0.000510081,0.0000079816355,0.00026367724,0.0015045122],"genre_scores_gemma":[0.015607072,0.0000046066248,0.98275006,0.00026891273,0.00018373704,0.000033454082,0.0000015555603,0.000025456498,0.0011251693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981381,0.00011516266,0.00028700763,0.000695358,0.00032045928,0.00044385553],"domain_scores_gemma":[0.99809927,0.00016402102,0.00029935446,0.0009099582,0.00038258918,0.00014480851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002983607,0.00025065753,0.0002474234,0.00013275405,0.0011923767,0.00064299075,0.0012186947,0.000112996095,0.00010376803],"category_scores_gemma":[0.00030972264,0.00024130556,0.0000868474,0.00017278873,0.000181354,0.0019459169,0.0005402117,0.00018444599,0.00012673352],"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":[0.000047643825,0.00046507618,0.00014972793,0.00000810391,0.00012511991,0.00017512521,0.0020032702,0.17657067,0.013078967,0.006842805,0.00081932027,0.79971415],"study_design_scores_gemma":[0.00047683128,0.00006880246,0.0009485274,0.000010496884,0.0000074364216,0.000022630808,0.00003206437,0.9708362,0.023952827,0.0029957234,0.0003215827,0.00032685153],"about_ca_topic_score_codex":0.00013226793,"about_ca_topic_score_gemma":0.00001706093,"teacher_disagreement_score":0.79938734,"about_ca_system_score_codex":0.0001908113,"about_ca_system_score_gemma":0.00014137247,"threshold_uncertainty_score":0.9840155},"labels":[],"label_agreement":null},{"id":"W2693076760","doi":"10.1016/j.ejor.2018.02.045","title":"Trade-off preservation in inverse multi-objective convex optimization","year":2018,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Inverse; Linear programming; Computer science; Pareto principle; Mathematics; Optimization problem; Convex optimization; Regular polygon","score_opus":0.08503374081602867,"score_gpt":0.3640516131639794,"score_spread":0.2790178723479507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2693076760","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":[0.0038129573,0.000073500894,0.99006534,0.0017957456,0.00030795857,0.0002998401,0.00000296989,0.000022437629,0.0036192623],"genre_scores_gemma":[0.28246596,0.00011103803,0.7160032,0.00033567633,0.00046014896,0.0000061466426,0.0000075374264,0.000031865235,0.0005784583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99555624,0.0019814097,0.000666739,0.00034462588,0.0011165616,0.00033441352],"domain_scores_gemma":[0.99639505,0.0003105939,0.0002312138,0.00028890162,0.0026023171,0.00017189929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004661056,0.00014330167,0.00018897715,0.0008113687,0.00030609863,0.0002794615,0.000953532,0.000038434435,0.00011384939],"category_scores_gemma":[0.0021169083,0.00013286054,0.000056279107,0.0014046232,0.000263592,0.0029984142,0.00025471373,0.00060235494,0.00008394566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017459643,0.000524404,0.0012544789,0.000009059152,0.000045432378,0.00026783254,0.0070338766,0.9599548,0.0019928731,0.00598246,0.0017140129,0.021046171],"study_design_scores_gemma":[0.002114149,0.00057045906,0.01819756,0.00005740213,0.0000018152137,0.0000701736,0.00024373819,0.975071,0.0010607931,0.00013747164,0.0023181066,0.00015731138],"about_ca_topic_score_codex":0.0000054768634,"about_ca_topic_score_gemma":0.000011787897,"teacher_disagreement_score":0.278653,"about_ca_system_score_codex":0.00030317518,"about_ca_system_score_gemma":0.00047572103,"threshold_uncertainty_score":0.54178953},"labels":[],"label_agreement":null},{"id":"W2705978056","doi":"","title":"Simulation and optimization of Mixed Fluid Cascade process to produce Liquefied natural gas","year":2017,"lang":"en","type":"article","venue":"Nova Journal of Engineering and Applied Sciences","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Liquefied natural gas; Natural gas; Gas compressor; Liquefaction; Cascade; Volume (thermodynamics); Genetic algorithm; Work (physics); Process engineering; Interfacing; Process (computing); Engineering; Energy consumption; Environmental science; Computer science; Mechanical engineering; Mathematical optimization; Waste management; Mathematics; Thermodynamics; Physics","score_opus":0.022561665980629524,"score_gpt":0.29740248159344324,"score_spread":0.2748408156128137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2705978056","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":[0.21141738,0.000081999104,0.78797495,0.00018744545,0.00020119359,0.000088967834,3.3461393e-7,0.0000125468605,0.000035206627],"genre_scores_gemma":[0.6602989,0.000008221736,0.33964327,0.000008310908,0.000033924705,9.620142e-7,7.494042e-8,0.0000029777268,0.000003400291],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992067,0.0000063454245,0.00022546345,0.00019332353,0.00024472363,0.00012341667],"domain_scores_gemma":[0.9992068,0.000080367885,0.00027558266,0.00013026074,0.00023420322,0.00007282735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000416935,0.00009204513,0.00015593345,0.0001744553,0.00018815298,0.00016984872,0.00037413268,0.000027238964,6.3337245e-7],"category_scores_gemma":[0.00041723682,0.00007555355,0.000014414049,0.00023310978,0.00008139414,0.000687235,0.00007804209,0.00009131519,1.1819663e-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":[0.000006649483,0.000008445984,0.00004481525,0.000013479027,0.0000040482137,7.0881686e-7,0.00031065318,0.98781836,0.0060899,0.0004945876,9.606115e-7,0.0052073696],"study_design_scores_gemma":[0.00023242742,0.00006930993,0.0013673312,0.000044277956,0.0000034927023,0.000017880304,0.000032422795,0.9756392,0.022428185,0.00006579358,0.000008339707,0.00009133875],"about_ca_topic_score_codex":0.0000011271335,"about_ca_topic_score_gemma":4.2101047e-7,"teacher_disagreement_score":0.44888148,"about_ca_system_score_codex":0.000012550204,"about_ca_system_score_gemma":0.000040880757,"threshold_uncertainty_score":0.30809844},"labels":[],"label_agreement":null},{"id":"W2715235799","doi":"10.2139/ssrn.2986630","title":"Optimizing Objective Functions Determined from Random Forests","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Random forest; Environmental science; Computer science; Artificial intelligence","score_opus":0.010855445793266371,"score_gpt":0.2625532758994349,"score_spread":0.25169783010616853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2715235799","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":[0.0051836967,0.00053163344,0.99124306,0.00047085402,0.0009524436,0.0002101604,0.000004612861,0.00012410093,0.0012794129],"genre_scores_gemma":[0.8665517,0.00054883095,0.13035946,0.00007915076,0.0005092714,0.00002648585,0.000004696637,0.000037232632,0.0018831815],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697316,0.00011229529,0.0003241215,0.00048271896,0.00033614246,0.0017715493],"domain_scores_gemma":[0.9979763,0.00016186677,0.0005452529,0.00086472597,0.00029404438,0.00015781488],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.000741001,0.0002489633,0.0002851187,0.0001618243,0.0018676113,0.00068704766,0.001556854,0.00009140625,0.000021948752],"category_scores_gemma":[0.00045633162,0.00022962589,0.00018082137,0.0001401741,0.00010128493,0.0022673684,0.0002924288,0.0013817842,0.000061109116],"study_design_candidate":"design_other","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":[0.0006752031,0.00046547962,0.009271399,0.000005841546,0.0013891044,0.00016464593,0.0034044213,0.09781054,0.0017370404,0.07867381,0.00015093162,0.8062516],"study_design_scores_gemma":[0.013695997,0.0005751676,0.013213057,0.00007123887,0.00009824354,0.0009143703,0.0010317853,0.54794395,0.0011894929,0.41935048,0.00084850885,0.0010676932],"about_ca_topic_score_codex":0.0001140826,"about_ca_topic_score_gemma":0.0014875121,"teacher_disagreement_score":0.861368,"about_ca_system_score_codex":0.00096119766,"about_ca_system_score_gemma":0.0013169443,"threshold_uncertainty_score":0.99943185},"labels":[],"label_agreement":null},{"id":"W2728804035","doi":"10.1007/s00158-017-1726-0","title":"Compact implementation of non-hierarchical analytical target cascading for coordinating distributed multidisciplinary design optimization problems","year":2017,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","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":"McGill University; Group for Research in Decision Analysis","funders":"McGill University","keywords":"Multidisciplinary design optimization; Multidisciplinary approach; Computer science; Engineering design process; Mathematical optimization; Distributed computing; Function (biology); Theoretical computer science; Engineering; Mathematics; Mechanical engineering","score_opus":0.02814911412164767,"score_gpt":0.3436362931700707,"score_spread":0.31548717904842305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2728804035","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":[0.008457076,0.000035996538,0.9883723,0.0006620223,0.0003539368,0.0017855068,0.0001739905,0.00011705043,0.000042091895],"genre_scores_gemma":[0.42746037,0.000021242106,0.5720404,0.0000049219243,0.00005928519,0.0000470849,0.00031896285,0.000025131141,0.000022660488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99740267,0.00012928253,0.00077113515,0.00084108877,0.00034272714,0.0005131249],"domain_scores_gemma":[0.99738914,0.00027388253,0.00091068976,0.00061148626,0.0005949093,0.00021992263],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00046089638,0.00041164432,0.0005132515,0.00023879147,0.0021838509,0.0003445145,0.0006813443,0.00016236272,0.000018798617],"category_scores_gemma":[0.00024380304,0.00037546764,0.00012878182,0.00030787653,0.0003087563,0.002346285,0.0005442804,0.0001968314,4.5659377e-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":[0.00007690913,0.00004168365,0.0036262537,0.00009646837,0.00004900963,0.0000031673299,0.0011247225,0.98948914,0.00038434405,0.0008968019,0.0000129410155,0.0041985903],"study_design_scores_gemma":[0.0027096563,0.0003559633,0.022444157,0.00006988092,0.000058127498,0.000026088097,0.0003810642,0.9710697,0.0012795905,0.0011662273,0.0000018535779,0.0004377237],"about_ca_topic_score_codex":0.00004287084,"about_ca_topic_score_gemma":0.0000056312974,"teacher_disagreement_score":0.41900328,"about_ca_system_score_codex":0.00014008273,"about_ca_system_score_gemma":0.00011819946,"threshold_uncertainty_score":0.9998697},"labels":[],"label_agreement":null},{"id":"W2729858878","doi":"10.1109/cec.2017.7969592","title":"Fusion-based hybrid many-objective optimization algorithm","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Benchmark (surveying); Algorithm; Optimization problem; Convergence (economics); Test functions for optimization; Curse of dimensionality; Mathematical optimization; Machine learning; Multi-swarm optimization; Mathematics","score_opus":0.012558301475420716,"score_gpt":0.26710910919382436,"score_spread":0.25455080771840366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2729858878","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":[0.00002637185,0.000016813026,0.9839693,0.00076093775,0.00075163174,0.00038679587,0.000010293755,0.0004913174,0.013586593],"genre_scores_gemma":[0.016224945,0.000016410855,0.98113424,0.00054956466,0.00010383766,0.000048733607,0.000015393483,0.000028724202,0.0018781291],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981225,0.000066470915,0.0002720831,0.0007474179,0.00041015574,0.00038137098],"domain_scores_gemma":[0.99726754,0.00010339048,0.0003391104,0.0016180357,0.00050156063,0.0001703848],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022074813,0.0002618685,0.00023016114,0.00018481693,0.0011093493,0.0006228892,0.0015633953,0.000068285335,0.000178503],"category_scores_gemma":[0.00027252166,0.00024828577,0.00009776786,0.0001832188,0.0001407505,0.001820239,0.00047436738,0.00016537322,0.000117400334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000784539,0.00015304427,0.00013727415,0.0000046402965,0.000021203814,0.000053696927,0.00008712658,0.7595389,0.000049764156,0.0046764994,0.00031491148,0.23495512],"study_design_scores_gemma":[0.0009879948,0.00005965187,0.00085377804,0.000013566086,0.000005290383,0.000014009094,0.000015233134,0.99088746,0.0056642722,0.0007294474,0.00043503966,0.00033426925],"about_ca_topic_score_codex":0.00005971686,"about_ca_topic_score_gemma":0.0000037940981,"teacher_disagreement_score":0.23462084,"about_ca_system_score_codex":0.00015246985,"about_ca_system_score_gemma":0.00015016057,"threshold_uncertainty_score":0.99999696},"labels":[],"label_agreement":null},{"id":"W2732794531","doi":"10.1007/s00158-017-1730-4","title":"Multi-fidelity POD surrogate-assisted optimization: Concept and aero-design study","year":2017,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Safran Electronics (Canada)","funders":"Association Nationale de la Recherche et de la Technologie; Fédération Wallonie-Bruxelles","keywords":"Subspace topology; Surrogate model; Fidelity; Curse of dimensionality; Field (mathematics); Mathematical optimization; Computer science; Algorithm; Dimensionality reduction; Mathematics; Artificial intelligence","score_opus":0.03683139169342397,"score_gpt":0.3174067330130303,"score_spread":0.2805753413196063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2732794531","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":[0.0098582115,0.00017119142,0.9871432,0.00045784007,0.0006098633,0.0013878562,0.000022545895,0.00027965294,0.00006962722],"genre_scores_gemma":[0.37438586,0.000074651565,0.62520856,0.00002334468,0.00005052917,0.000036435344,0.00003481139,0.000023917122,0.00016189815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973121,0.0002630285,0.0005319336,0.0011395316,0.00033928413,0.00041411974],"domain_scores_gemma":[0.99762046,0.00012057464,0.0005452636,0.0009795928,0.00046932118,0.0002648012],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0003152167,0.00046009294,0.00043374172,0.00014970152,0.0028794673,0.000823572,0.000726064,0.00015206276,0.00002566512],"category_scores_gemma":[0.00024597897,0.00040948982,0.000055490873,0.00023351905,0.0003927523,0.002950141,0.0011094877,0.0002266784,0.0000017982408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004710033,0.00010604129,0.0021523738,0.000012926692,0.00003978808,0.00002363651,0.0023641353,0.985513,0.00008962247,0.0001704684,0.0000061747187,0.009474737],"study_design_scores_gemma":[0.0033041597,0.00024250813,0.110461295,0.000020903319,0.000042456606,0.00005959331,0.00051386794,0.8847006,0.00007435462,0.00012036446,0.0000019700449,0.0004579115],"about_ca_topic_score_codex":0.000059389877,"about_ca_topic_score_gemma":0.000019743306,"teacher_disagreement_score":0.36452767,"about_ca_system_score_codex":0.00008220471,"about_ca_system_score_gemma":0.00006782102,"threshold_uncertainty_score":0.99983567},"labels":[],"label_agreement":null},{"id":"W2737342012","doi":"","title":"Numerical investigation of non-hierarchical coordination for distributed multidisciplinary design optimization with fixed computational budget","year":2015,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada); McGill University; Group for Research in Decision Analysis","funders":"","keywords":"Mathematical optimization; MATLAB; Computer science; Convergence (economics); Augmented Lagrangian method; Optimization problem; Consistency (knowledge bases); Mathematics; Algorithm","score_opus":0.01947192149283435,"score_gpt":0.2563932753391899,"score_spread":0.23692135384635551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737342012","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":[0.0026621385,0.000010570478,0.9951093,0.0010744549,0.00013746598,0.00081147306,0.000027903414,0.00014308053,0.000023601204],"genre_scores_gemma":[0.20997284,0.0000012329799,0.7893548,0.00006302533,0.0000424974,0.00010235183,0.00041340344,0.000020138224,0.000029698384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983375,0.00018067996,0.0003742445,0.00045291142,0.0004193928,0.00023527307],"domain_scores_gemma":[0.9978002,0.00047420716,0.00031064975,0.0002351989,0.0009738091,0.00020594077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032316532,0.00020942416,0.00025904563,0.00018728623,0.00023913427,0.00006550358,0.0003304788,0.00013356417,0.0000022148288],"category_scores_gemma":[0.00031592717,0.00019570667,0.000053194384,0.0007872998,0.00028657375,0.000723076,0.000057714147,0.00016651046,0.0000023692364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000141367,0.000082326944,0.0009483649,0.000015198651,0.000025781726,0.0000032436235,0.0014553644,0.9905466,0.000090976966,0.0046463516,0.00024470157,0.0017997682],"study_design_scores_gemma":[0.002428042,0.00042250965,0.0027476347,0.000018497269,0.000013117368,0.000018974266,0.00009841604,0.98523515,0.0009886575,0.007771065,0.000020869453,0.00023707213],"about_ca_topic_score_codex":0.000009283076,"about_ca_topic_score_gemma":6.5901764e-7,"teacher_disagreement_score":0.2073107,"about_ca_system_score_codex":0.00026369133,"about_ca_system_score_gemma":0.0002750069,"threshold_uncertainty_score":0.79806864},"labels":[],"label_agreement":null},{"id":"W2737659035","doi":"10.1108/compel-02-2017-0110","title":"Analysis and design of electrical machines with material uncertainties in iron and permanent magnet","year":2017,"lang":"en","type":"article","venue":"COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"McGill University","funders":"","keywords":"Robustness (evolution); Magnet; Computer science; Mechanical engineering; Control engineering; Engineering","score_opus":0.01109181690410946,"score_gpt":0.2702947412406307,"score_spread":0.2592029243365212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737659035","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":[0.10580046,0.00032932137,0.8933251,0.00033170698,0.000046054993,0.00015034928,0.000001046433,0.000008718226,0.000007241188],"genre_scores_gemma":[0.84667194,0.00062819436,0.15261878,0.000019653644,0.00002549257,0.000010762734,0.0000017139023,0.0000074341956,0.000016017371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923784,0.000022572709,0.00024686416,0.00015487484,0.00016037146,0.00017746011],"domain_scores_gemma":[0.9993425,0.00030093672,0.00015689667,0.00007011936,0.00009022511,0.000039343522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003285856,0.00011308971,0.00020520759,0.0002896228,0.00011265428,0.00024761618,0.00023338904,0.000029599372,6.2589805e-7],"category_scores_gemma":[0.00009962934,0.00008104008,0.000019868117,0.0001401538,0.000049679813,0.0002092462,0.00008258648,0.00016762575,2.6628582e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002579221,0.00020181459,0.002847472,0.00009263326,0.00038797897,0.000020640771,0.0018496322,0.7573897,0.0018161788,0.13076828,0.000007341656,0.10436036],"study_design_scores_gemma":[0.0007158465,0.00016038565,0.0044465675,0.000025914966,0.000024983356,0.00016697758,0.000012451526,0.9797233,0.00011610106,0.014506458,0.0000074542886,0.00009360585],"about_ca_topic_score_codex":0.000010514087,"about_ca_topic_score_gemma":0.000011172966,"teacher_disagreement_score":0.7408715,"about_ca_system_score_codex":0.00006896806,"about_ca_system_score_gemma":0.000032207172,"threshold_uncertainty_score":0.33047184},"labels":[],"label_agreement":null},{"id":"W2742479298","doi":"10.1109/islped.2017.8009208","title":"A case for efficient accelerator design space exploration via Bayesian optimization","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bayesian optimization; Computer science; Design space exploration; Artificial intelligence; Artificial neural network; Machine learning; Hardware acceleration; Bayesian probability; Space (punctuation); Bayesian network; Deep learning; Computer engineering; Embedded system; Field-programmable gate array","score_opus":0.05832393744523108,"score_gpt":0.30598532207861623,"score_spread":0.24766138463338516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742479298","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":[0.000021461492,0.0000074226828,0.9969018,0.0008878205,0.0004168309,0.0010877743,0.0000027686167,0.00025409655,0.00042000366],"genre_scores_gemma":[0.11058436,0.0000041234644,0.8886749,0.00010075732,0.00006822739,0.0001921252,0.000004468744,0.000021253503,0.00034976294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874824,0.000059350776,0.000222277,0.0005207171,0.00018035184,0.0002690746],"domain_scores_gemma":[0.9981532,0.00010863184,0.00027226578,0.000906877,0.00043226668,0.00012674203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028866038,0.00018062472,0.00015202657,0.00011285866,0.0010594403,0.0007016734,0.0005879732,0.00007115251,0.000022634638],"category_scores_gemma":[0.00026789954,0.00017189849,0.000053545675,0.00015059637,0.000047832174,0.0019483966,0.00016868117,0.000060634397,0.000014844875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012051817,0.000062598294,0.000002665842,0.0000043839455,0.0000072052508,0.000053583244,0.00033720877,0.9835197,0.00013774986,0.004236437,0.00010306068,0.01152336],"study_design_scores_gemma":[0.000807334,0.00008523645,0.0000044954672,0.000005457231,0.0000058490346,0.00013539854,0.000047184232,0.99045014,0.0076061618,0.00055403996,0.00007450641,0.0002241949],"about_ca_topic_score_codex":0.000014188022,"about_ca_topic_score_gemma":0.0000069407556,"teacher_disagreement_score":0.110562906,"about_ca_system_score_codex":0.00010268986,"about_ca_system_score_gemma":0.00007207861,"threshold_uncertainty_score":0.8148465},"labels":[],"label_agreement":null},{"id":"W2745882967","doi":"10.1109/tmag.2017.2661987","title":"Surrogate-Based MOEA/D for Electric Motor Design With Scarce Function Evaluations","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Surrogate model; Computer science; Decomposition; Multi-objective optimization; Mathematical optimization; Evolutionary algorithm; Function (biology); Electric motor; Artificial intelligence; Machine learning; Mathematics; Engineering","score_opus":0.037423525284519796,"score_gpt":0.29388138035126754,"score_spread":0.25645785506674773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2745882967","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":[0.00022531685,0.00002085869,0.99706316,0.00046448613,0.0005739539,0.0012512897,0.000023592886,0.00024277522,0.00013457218],"genre_scores_gemma":[0.43272913,0.000014866544,0.5656976,0.00013555054,0.00003442508,0.00039902548,0.000002646297,0.000032993452,0.00095377804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998417,0.000097448705,0.00022434077,0.0005293273,0.0003881753,0.00034366635],"domain_scores_gemma":[0.9976838,0.00033817292,0.00019827225,0.0010415735,0.00061196845,0.00012620099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027211997,0.00023758932,0.00017710653,0.00027027883,0.0012333913,0.00031293934,0.0006307762,0.000094616415,0.00003308218],"category_scores_gemma":[0.000046080142,0.00022941564,0.00009466517,0.0003358583,0.00009428621,0.00058327685,0.0000023591006,0.00019153088,0.00003298821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022043627,0.0002683793,0.0000069373405,0.000010853155,0.000030663774,0.0000019196511,0.0000634172,0.8689421,0.0048764385,0.00020507714,0.000034570952,0.12533922],"study_design_scores_gemma":[0.0020952872,0.0019103688,0.00064861996,0.000017297258,0.000080182705,0.0000048911616,0.000008192583,0.94875836,0.04556023,0.00045463623,0.00018047732,0.00028148098],"about_ca_topic_score_codex":0.0000107144215,"about_ca_topic_score_gemma":0.000023317369,"teacher_disagreement_score":0.4325038,"about_ca_system_score_codex":0.00013739508,"about_ca_system_score_gemma":0.00027086638,"threshold_uncertainty_score":0.94863737},"labels":[],"label_agreement":null},{"id":"W2752453588","doi":"10.14738/tmlai.55.3580","title":"A Computational Algorithm for Simultaneously Creating Alternatives to Optimal Solutions","year":2017,"lang":"en","type":"article","venue":"Transactions on Machine Learning and Artificial Intelligence","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Benchmark (surveying); Mathematical optimization; Firefly algorithm; Optimization problem; Algorithm; Mathematics","score_opus":0.04137580143151002,"score_gpt":0.3428928418152673,"score_spread":0.30151704038375726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752453588","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":[0.0003085214,0.000020829084,0.99761677,0.0010997106,0.00021387386,0.0003421908,0.00003661222,0.00018705867,0.0001744366],"genre_scores_gemma":[0.30781946,0.000020002362,0.69147503,0.00007306556,0.000051172647,0.000053281452,0.0000048782717,0.000015291165,0.00048778308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986315,0.00006218185,0.00027708602,0.00052276935,0.00019771836,0.0003087404],"domain_scores_gemma":[0.9986493,0.0005622603,0.00015330756,0.00024713084,0.00023771412,0.00015027866],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00024525172,0.0001907881,0.00017527794,0.00016855705,0.002987586,0.00049561536,0.00042449048,0.00004706482,0.000021217744],"category_scores_gemma":[0.000277125,0.00019843638,0.00007371381,0.0001403266,0.00013072655,0.00042795762,0.000033416192,0.00029179352,0.000030530093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010248706,0.000050282742,0.0000021976439,0.0000018338325,0.000010972533,0.0000022187596,0.0007433222,0.5404037,0.00003681603,0.0030805115,3.505866e-7,0.45565748],"study_design_scores_gemma":[0.00008653943,0.00030087426,0.00001832332,0.000027820524,0.000008450695,0.000015597263,0.0002331607,0.99365526,0.0014660825,0.0035734526,0.00039267325,0.00022178449],"about_ca_topic_score_codex":0.000075248936,"about_ca_topic_score_gemma":0.00002947069,"teacher_disagreement_score":0.45543572,"about_ca_system_score_codex":0.000045003988,"about_ca_system_score_gemma":0.00003649325,"threshold_uncertainty_score":0.9983104},"labels":[],"label_agreement":null},{"id":"W2754588405","doi":"10.1609/aaai.v32i1.11532","title":"Warmstarting of Model-Based Algorithm Configuration","year":2018,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Deutsche Forschungsgemeinschaft","keywords":"Exploit; Computer science; Benchmark (surveying); Solver; Process (computing); Scratch; Algorithm; Programming language","score_opus":0.08355056564429254,"score_gpt":0.32335339257047163,"score_spread":0.2398028269261791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754588405","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":[0.001886128,0.000022523722,0.991195,0.0008923027,0.00068853097,0.00083632953,0.00003348969,0.0001370417,0.00430866],"genre_scores_gemma":[0.5722018,0.000024834806,0.42737386,0.00011487742,0.00008358025,0.000058627622,0.000003893861,0.000026336109,0.00011222343],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965137,0.00003610701,0.0012027869,0.0009784002,0.0008665343,0.00040245184],"domain_scores_gemma":[0.9933183,0.000120952616,0.0019542244,0.0006911539,0.0038073903,0.000107958884],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006798929,0.00046108133,0.0005866154,0.00029847014,0.0001954306,0.0002289899,0.0030358105,0.00029679955,0.000041337855],"category_scores_gemma":[0.0006474223,0.00038864554,0.00025125517,0.0006634233,0.0006157025,0.00042517277,0.001145951,0.00066478073,0.000026362284],"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":[0.00009461639,0.0004973527,0.000024591614,0.00031990607,0.00006606221,6.9588674e-7,0.0027543588,0.17893034,0.023270011,0.30642298,0.00010000995,0.48751906],"study_design_scores_gemma":[0.000024004787,0.000076748336,0.0000043388286,0.00031889835,0.00001196775,4.779589e-7,0.00006958513,0.51965564,0.38120463,0.09843856,0.0000043684995,0.00019073903],"about_ca_topic_score_codex":0.000028377744,"about_ca_topic_score_gemma":0.0000028069971,"teacher_disagreement_score":0.57031566,"about_ca_system_score_codex":0.00013317862,"about_ca_system_score_gemma":0.00055856287,"threshold_uncertainty_score":0.99985653},"labels":[],"label_agreement":null},{"id":"W2759142102","doi":"","title":"EM-driven multi-objective design of impedance transformers by pareto ranking bisection algorithm","year":2017,"lang":"en","type":"article","venue":"2017 International Applied Computational Electromagnetics Society Symposium (ACES)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Bisection; Transformer; Multi-objective optimization; Bisection method; Pareto principle; Mathematical optimization; Pareto optimal; Algorithm; Impedance matching; Mathematics; Electrical impedance; Ranking (information retrieval); Matching (statistics); Computer science; Engineering; Artificial intelligence; Statistics; Voltage","score_opus":0.013364426937418355,"score_gpt":0.26748255774098545,"score_spread":0.2541181308035671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2759142102","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":[0.0009999477,0.00010921081,0.99509615,0.0005939798,0.0007414845,0.00088289805,0.000107852415,0.00019449215,0.001273996],"genre_scores_gemma":[0.30915388,0.0002457511,0.68964744,0.00019832718,0.00013170597,0.000167163,0.0001315865,0.000047888592,0.0002762259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964454,0.000080324346,0.00071221625,0.0010170352,0.0011791482,0.00056584534],"domain_scores_gemma":[0.9968591,0.00030270024,0.0010594055,0.0005523665,0.0010621223,0.0001643573],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039542947,0.00048702708,0.0004473968,0.00015896026,0.0009494048,0.0004999309,0.0020871807,0.00022496641,0.00001937132],"category_scores_gemma":[0.000051925075,0.00055045774,0.00029421825,0.00028070575,0.00032711448,0.0010986532,0.00029559387,0.00043616374,0.000020984717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012731581,0.0007075167,0.00015824605,0.0000439733,0.0006290356,0.000006664722,0.003949279,0.86128813,0.075276114,0.013530953,0.0017050082,0.04257775],"study_design_scores_gemma":[0.002813893,0.00028188367,0.0010580368,0.000028856837,0.000038035138,0.000035435412,0.00014100371,0.9805491,0.009132339,0.005194105,0.00017322904,0.0005540966],"about_ca_topic_score_codex":0.00003786484,"about_ca_topic_score_gemma":0.000007085166,"teacher_disagreement_score":0.30815396,"about_ca_system_score_codex":0.0004882211,"about_ca_system_score_gemma":0.0003432982,"threshold_uncertainty_score":0.9996947},"labels":[],"label_agreement":null},{"id":"W2759726304","doi":"10.1007/s00158-017-1815-0","title":"Employing partial metamodels for optimization with scarce samples","year":2017,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Simon Fraser University","funders":"","keywords":"Metamodeling; Mathematical optimization; Radial basis function; Computer science; Optimization problem; Engineering design process; Algorithm; Mathematics; Engineering; Artificial intelligence","score_opus":0.031152022518268246,"score_gpt":0.3044022860194053,"score_spread":0.273250263501137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2759726304","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":[0.0046768463,0.000061038074,0.9931047,0.00060488255,0.0003530675,0.0008574804,0.00003241035,0.00020566591,0.0001039358],"genre_scores_gemma":[0.24085496,0.000047625992,0.7586626,0.000023926863,0.00010455046,0.00008658779,0.000084772386,0.000029351137,0.000105609615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982613,0.00004740334,0.00030620006,0.00076755066,0.00024186002,0.00037569948],"domain_scores_gemma":[0.9982742,0.000088071145,0.0004076477,0.00067941594,0.00039859436,0.00015203844],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00015025101,0.00031606294,0.00029402078,0.00011970214,0.0025535468,0.0006223336,0.00058766635,0.000097499964,0.000011919363],"category_scores_gemma":[0.00012231765,0.0002529246,0.00006281894,0.00013802254,0.00021559904,0.0031492088,0.00041540756,0.000103571554,6.098576e-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":[0.00007859515,0.000012932366,0.0006455257,0.000025970012,0.00002813241,0.0000023359717,0.00050118176,0.9886605,0.00007844655,0.0029354258,0.0000023459443,0.0070286165],"study_design_scores_gemma":[0.0016572513,0.00016473893,0.002258631,0.00003126703,0.000049027185,0.000033328113,0.000079848396,0.9936836,0.0006300025,0.0010089497,0.000012650273,0.00039071983],"about_ca_topic_score_codex":0.000018809409,"about_ca_topic_score_gemma":0.000009381516,"teacher_disagreement_score":0.23617812,"about_ca_system_score_codex":0.000053836357,"about_ca_system_score_gemma":0.000059937935,"threshold_uncertainty_score":0.9999923},"labels":[],"label_agreement":null},{"id":"W2761472785","doi":"10.1007/s10898-017-0574-1","title":"Order-based error for managing ensembles of surrogates in mesh adaptive direct search","year":2017,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Benchmark (surveying); Surrogate model; Mathematical optimization; Global optimization; Computer science; Mathematics; Machine learning; Algorithm","score_opus":0.03303527240385088,"score_gpt":0.32753132144494923,"score_spread":0.29449604904109833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2761472785","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":[0.0010639909,0.000086607826,0.99656236,0.0007123133,0.00029182353,0.00027556473,0.000013978122,0.000017192122,0.0009761573],"genre_scores_gemma":[0.36761507,0.000033607073,0.63227314,0.000024568344,0.000026249476,0.000003462722,0.0000017361137,0.0000075713,0.000014573196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985812,0.000098544304,0.00051187543,0.00021964558,0.00036155744,0.00022717948],"domain_scores_gemma":[0.99703676,0.00015950022,0.0009407116,0.00032801495,0.0014560819,0.00007895009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006174704,0.00014346356,0.00033486105,0.00017412966,0.00017499067,0.00013363472,0.0007648171,0.00006517163,0.000004546671],"category_scores_gemma":[0.00065444026,0.00013523294,0.00010253257,0.0003983856,0.00008178558,0.0012795259,0.00010667736,0.00009846432,5.1548324e-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":[0.00013086162,0.00010266864,0.0023769285,0.000015135856,0.000023951476,0.000008913058,0.00008626233,0.99010164,0.000028977283,0.0019385177,0.000018675495,0.0051674633],"study_design_scores_gemma":[0.0018388756,0.0002614592,0.002765779,0.00012707998,0.000012720347,0.000010315097,0.000077578254,0.99303395,0.0010908855,0.00064392143,0.00001203618,0.00012539784],"about_ca_topic_score_codex":0.000033978857,"about_ca_topic_score_gemma":0.00003936321,"teacher_disagreement_score":0.3665511,"about_ca_system_score_codex":0.00026207807,"about_ca_system_score_gemma":0.00027057622,"threshold_uncertainty_score":0.5514639},"labels":[],"label_agreement":null},{"id":"W2762742239","doi":"10.1007/s00158-017-1826-x","title":"Surrogate-based optimization with clustering-based space exploration for expensive multimodal problems","year":2017,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Mathematical optimization; Kriging; Benchmark (surveying); Local optimum; Cluster analysis; Surrogate model; Computer science; Optimization problem; Global optimization; Local search (optimization); Algorithm; Mathematics; Machine learning","score_opus":0.0231776931946312,"score_gpt":0.28264271371293054,"score_spread":0.25946502051829934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762742239","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":[0.0017394752,0.000032076263,0.9936563,0.0018216034,0.00045615615,0.0019132156,0.000037062946,0.00028313563,0.000061026596],"genre_scores_gemma":[0.2859077,0.000013444993,0.7133526,0.000045429744,0.00007130984,0.0002347491,0.00026367535,0.000042168125,0.00006890517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785244,0.00007594104,0.00037686166,0.00094381825,0.00032207632,0.00042884008],"domain_scores_gemma":[0.9974109,0.00012577366,0.0006519058,0.0007926108,0.0008359907,0.00018280328],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00016316316,0.0004405207,0.00033934892,0.000201463,0.0021909357,0.0006599736,0.00054039125,0.00015274968,0.000009892661],"category_scores_gemma":[0.0001534389,0.0003729293,0.00007427089,0.0002001305,0.00025109915,0.0034138216,0.00023889371,0.00013736273,0.0000010926644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021969667,0.000036268397,0.0003613364,0.00007889032,0.000016017519,0.000004513319,0.00055950356,0.99633074,0.00011511606,0.00031284068,0.000004091831,0.0019610035],"study_design_scores_gemma":[0.0047867945,0.0004341587,0.0009659197,0.00009256533,0.00003497544,0.000010234045,0.0001389941,0.9917206,0.0010534754,0.0002089708,0.0000094102725,0.0005438674],"about_ca_topic_score_codex":0.000029806755,"about_ca_topic_score_gemma":0.00009095296,"teacher_disagreement_score":0.2841682,"about_ca_system_score_codex":0.000118508964,"about_ca_system_score_gemma":0.00013795766,"threshold_uncertainty_score":0.99987227},"labels":[],"label_agreement":null},{"id":"W2765909831","doi":"10.1115/detc2017-67843","title":"A Reference Error Formulation for Multi-Fidelity Design Optimization","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Fidelity; Computer science; Constraint (computer-aided design); Mathematical optimization; Key (lock); Process (computing); High fidelity; Engineering design process; Optimization problem; Algorithm; Mathematics; Engineering","score_opus":0.1804359847249212,"score_gpt":0.3819200661392065,"score_spread":0.20148408141428528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765909831","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":[0.000013864876,0.000007829993,0.9974729,0.00028179341,0.00022595304,0.0008890617,0.000005206148,0.0002656911,0.0008377054],"genre_scores_gemma":[0.05703338,0.000007873381,0.9416916,0.00013469225,0.000029653935,0.0001245807,0.000012861178,0.000014558801,0.000950805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987581,0.00004566408,0.00026744982,0.00049636915,0.00017544268,0.00025697448],"domain_scores_gemma":[0.99789494,0.000107112915,0.00032603266,0.0009793538,0.00060441816,0.00008812162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037474604,0.00015875399,0.00015365114,0.00007886198,0.00080863945,0.00037787855,0.00090594223,0.000088004985,0.000019209943],"category_scores_gemma":[0.0007385952,0.00014772515,0.000049465452,0.000091672904,0.00003070009,0.002533689,0.00020230032,0.000071589275,0.000016263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020635429,0.00007188383,0.000079285644,0.0000070514225,0.0000090535605,7.222074e-7,0.00012579511,0.94717455,0.00015840064,0.031870563,0.00007596465,0.020406077],"study_design_scores_gemma":[0.0010950712,0.000056689525,0.0015544383,0.000007811929,0.0000042822326,0.000002099907,0.000009620983,0.9938645,0.0015806055,0.0014520686,0.00017173938,0.00020108631],"about_ca_topic_score_codex":0.000025298628,"about_ca_topic_score_gemma":0.000015185642,"teacher_disagreement_score":0.057019513,"about_ca_system_score_codex":0.000093385126,"about_ca_system_score_gemma":0.000076172895,"threshold_uncertainty_score":0.62194824},"labels":[],"label_agreement":null},{"id":"W2766299525","doi":"10.1115/detc2017-67601","title":"Dimension Reduction and Decomposition Using Causal Graph and Qualitative Analysis for Aircraft Concept Design Optimization","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Dimensionality reduction; Curse of dimensionality; Multidisciplinary design optimization; Decomposition; Dimension (graph theory); Mathematical optimization; Computer science; Optimization problem; Graph; Decomposition method (queueing theory); Principal component analysis; Mathematics; Artificial intelligence; Theoretical computer science; Multidisciplinary approach","score_opus":0.055034606642099214,"score_gpt":0.3846011530735725,"score_spread":0.3295665464314733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766299525","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":[0.0030305246,0.000053515694,0.9960187,0.00022004913,0.0001307128,0.00044629764,0.000004867812,0.00007463945,0.000020666546],"genre_scores_gemma":[0.2443529,0.000024641293,0.7555117,0.000022825261,0.000018347488,0.000015512895,0.000012489852,0.000007524366,0.000034032957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988828,0.00015753017,0.0002002664,0.00047833836,0.00012759659,0.00015350882],"domain_scores_gemma":[0.99886155,0.00015448613,0.00026723064,0.00030993007,0.0003274086,0.0000793721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034067454,0.00014001926,0.00020115192,0.00024203447,0.0008756736,0.00030268982,0.00013603154,0.00006221803,0.0000033539104],"category_scores_gemma":[0.00010612454,0.00013665365,0.00004352079,0.0002475671,0.0001612569,0.0016666119,0.00010421686,0.000045909394,2.0465394e-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":[0.00002215042,0.0000231617,0.000052566727,0.0000037098416,0.000097089134,6.165947e-7,0.0029615145,0.9902313,0.0018624132,0.0025001285,0.0000069687762,0.0022383968],"study_design_scores_gemma":[0.00066563353,0.00009298648,0.00047394194,0.000008998618,0.00010284061,0.000011316816,0.00045392444,0.9915001,0.004657949,0.0018531099,0.0000012849778,0.00017795633],"about_ca_topic_score_codex":0.00005891027,"about_ca_topic_score_gemma":0.0000043322907,"teacher_disagreement_score":0.24132238,"about_ca_system_score_codex":0.000053583768,"about_ca_system_score_gemma":0.0000230869,"threshold_uncertainty_score":0.6735062},"labels":[],"label_agreement":null},{"id":"W2768981582","doi":"10.1007/s10288-017-0362-2","title":"The inventory replenishment planning and staggering problem: a bi-objective approach","year":2017,"lang":"en","type":"article","venue":"4OR","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Université Laval","funders":"Université Laval","keywords":"Mathematical optimization; Pareto principle; Multi-objective optimization; Computer science; Decision maker; Population; Pareto optimal; Order (exchange); Space (punctuation); Operations research; Mathematics; Economics","score_opus":0.02579070386469375,"score_gpt":0.287191586310707,"score_spread":0.2614008824460133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768981582","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":[0.0014451144,0.00040154145,0.98737866,0.00036045205,0.00024346325,0.00041673356,0.0000011757212,0.00013130628,0.009621551],"genre_scores_gemma":[0.26229665,0.000082553095,0.7348196,0.00015591354,0.00013242086,0.0002231513,0.0000019137362,0.000029866069,0.0022579336],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893504,0.000043627773,0.0001493339,0.0004211134,0.0001949865,0.00025591286],"domain_scores_gemma":[0.9987906,0.00005768759,0.00021470412,0.00077360775,0.00008456537,0.00007884179],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00033211664,0.00012623469,0.00011628262,0.000043214815,0.0013093074,0.0005636212,0.0006963252,0.000034579472,8.4237087e-7],"category_scores_gemma":[0.00014362212,0.00009467088,0.000027311859,0.00006937128,0.00014896457,0.0006979502,0.00060415623,0.00014256236,0.0000039209535],"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":[0.00016802762,0.0006093354,0.036292218,0.00024506755,0.00050695654,0.00016378364,0.054358974,0.087232664,0.00199097,0.14998007,0.0022233974,0.66622853],"study_design_scores_gemma":[0.0012079936,0.0001286748,0.017239986,0.00008226297,0.000010487808,0.000049010345,0.00078342407,0.962984,0.0015605779,0.005260721,0.010213141,0.0004796828],"about_ca_topic_score_codex":0.000020307969,"about_ca_topic_score_gemma":0.0000026702216,"teacher_disagreement_score":0.8757514,"about_ca_system_score_codex":0.000074396026,"about_ca_system_score_gemma":0.000047160567,"threshold_uncertainty_score":0.9999909},"labels":[],"label_agreement":null},{"id":"W2771061817","doi":"10.1109/smc.2017.8122576","title":"Estimation distribution algorithms with differential mutation for multi-objective optimization problems","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"EDAS; Estimation of distribution algorithm; Mathematical optimization; Evolutionary algorithm; Benchmark (surveying); Differential evolution; Multi-objective optimization; Computer science; Evolutionary computation; Mutation; Selection (genetic algorithm); Convergence (economics); Population; Optimization problem; Algorithm; Premature convergence; Mathematics; Genetic algorithm; Machine learning","score_opus":0.027562202066772092,"score_gpt":0.29923728392282634,"score_spread":0.27167508185605427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771061817","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":[0.00020047532,0.000006810724,0.99697804,0.00026106494,0.00037219477,0.001604038,0.00005085089,0.00037025826,0.00015626081],"genre_scores_gemma":[0.1469441,0.000006204543,0.8518149,0.00002386158,0.000058900874,0.0003854131,0.00038997422,0.000026749974,0.0003498754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982769,0.000046846206,0.00030903277,0.00070013164,0.0003241696,0.00034293617],"domain_scores_gemma":[0.9977943,0.00007294234,0.0005418175,0.00073194935,0.00075001747,0.000108994296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016682401,0.00027470812,0.0002298264,0.00009750681,0.001185709,0.0007359436,0.00063578907,0.00010892491,0.000011807862],"category_scores_gemma":[0.0003044081,0.00023405564,0.00006827653,0.00017828427,0.00012450064,0.003018006,0.00014529582,0.00011015121,0.00000846632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031540654,0.00017101341,0.000070683105,0.000018806413,0.00003501406,0.000002341202,0.00028967677,0.91726196,0.00005030284,0.006216679,0.000019138444,0.07583285],"study_design_scores_gemma":[0.0028770908,0.00023213716,0.002735717,0.000032354572,0.000022361224,0.000017715223,0.000033857395,0.9902734,0.002843202,0.00056591805,0.000025937728,0.0003402815],"about_ca_topic_score_codex":0.00004469467,"about_ca_topic_score_gemma":0.000029716546,"teacher_disagreement_score":0.14674363,"about_ca_system_score_codex":0.00021968946,"about_ca_system_score_gemma":0.00008823121,"threshold_uncertainty_score":0.9544512},"labels":[],"label_agreement":null},{"id":"W2774188651","doi":"10.1088/1367-2630/aae72a","title":"Statistical physics of design","year":2018,"lang":"en","type":"article","venue":"New Journal of Physics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Office of Naval Research","keywords":"Systems engineering; Component (thermodynamics); Range (aeronautics); Physics; Key (lock); Management science; Aerospace engineering; Computer science; Engineering","score_opus":0.03617238896819869,"score_gpt":0.30401104573436727,"score_spread":0.2678386567661686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774188651","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":[0.00007526405,0.000022730836,0.99908,0.00007095537,0.0003508184,0.00005316047,0.0000022356867,0.000013024557,0.00033183693],"genre_scores_gemma":[0.058009103,0.000011132464,0.94100565,0.00008123832,0.0008225556,2.5617825e-7,2.9421918e-7,0.000010412218,0.00005936887],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989874,0.00006865316,0.0003105437,0.00012125838,0.0003636302,0.00014853566],"domain_scores_gemma":[0.9982814,0.00019001382,0.0004490626,0.00022009389,0.00073905126,0.00012036488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015554024,0.000100503035,0.0002185332,0.000033403358,0.000045151482,0.00002955649,0.00047738763,0.000026596377,0.000013808739],"category_scores_gemma":[0.00009975447,0.00008885454,0.00006141734,0.00034927868,0.00011260688,0.00062608207,0.000073498595,0.00014126992,0.000017343697],"study_design_candidate":"design_other","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":[0.000060222632,0.00034054753,0.0000903688,0.0000116397305,0.00009095826,0.000022700102,0.001998237,0.06348716,0.006439107,0.1726829,0.0037731458,0.751003],"study_design_scores_gemma":[0.002187285,0.001615253,0.0006480931,0.00008103614,0.000042648568,0.000067716144,0.000039416816,0.49159554,0.18874879,0.31347716,0.0011718702,0.0003251834],"about_ca_topic_score_codex":0.0000025266459,"about_ca_topic_score_gemma":1.640397e-7,"teacher_disagreement_score":0.7506778,"about_ca_system_score_codex":0.000041647483,"about_ca_system_score_gemma":0.0002817848,"threshold_uncertainty_score":0.3623383},"labels":[],"label_agreement":null},{"id":"W2786385181","doi":"10.1007/s00158-018-1906-6","title":"Ensemble of surrogate based global optimization methods using hierarchical design space reduction","year":2018,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Northwestern Polytechnical University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Surrogate model; Mathematical optimization; Benchmark (surveying); Global optimization; Robustness (evolution); Computer science; Reduction (mathematics); Computation; Multi-objective optimization; Engineering design process; Optimization problem; Engineering optimization; Mathematics; Algorithm; Engineering","score_opus":0.03347490557827443,"score_gpt":0.3517479651707263,"score_spread":0.3182730595924519,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786385181","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":[0.0041353526,0.00006512181,0.9939993,0.00024529613,0.00070522295,0.0005605819,0.000011561965,0.0001737911,0.00010379064],"genre_scores_gemma":[0.12313041,0.000020905301,0.8766382,0.000014003204,0.00011030233,0.000009707026,0.00003262234,0.000019818552,0.000024045657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977564,0.00046572217,0.0004620039,0.00069645955,0.000281053,0.00033832004],"domain_scores_gemma":[0.99833536,0.0001068864,0.00036840988,0.0003948845,0.00063821085,0.00015621983],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037586462,0.0003017204,0.00031390617,0.00019096867,0.00057231274,0.00010021496,0.00030023814,0.00015516354,0.00002561706],"category_scores_gemma":[0.0001377828,0.00028043706,0.00006591549,0.0010727985,0.00036912944,0.0013026054,0.00027418614,0.00013385057,8.346677e-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":[0.00010872887,0.0000269471,0.00008456197,0.00002202297,0.0000158626,0.0000016212986,0.00038561848,0.98595667,0.0033901283,0.0013395187,0.0000028103336,0.008665524],"study_design_scores_gemma":[0.0009660962,0.0002642065,0.00039703638,0.000033671236,0.000037589045,0.000074232674,0.00007280915,0.98469126,0.011277993,0.0018797535,0.0000018603976,0.00030348808],"about_ca_topic_score_codex":0.000023730576,"about_ca_topic_score_gemma":0.0000015158505,"teacher_disagreement_score":0.118995056,"about_ca_system_score_codex":0.0001583181,"about_ca_system_score_gemma":0.00015904542,"threshold_uncertainty_score":0.9999648},"labels":[],"label_agreement":null},{"id":"W2788137402","doi":"10.1287/ijoc.2021.1066","title":"Network Models for Multiobjective Discrete Optimization","year":2021,"lang":"en","type":"preprint","venue":"INFORMS journal on computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematical optimization; Pareto principle; Multi-objective optimization; Computer science; Shortest path problem; Set (abstract data type); Path (computing); Network planning and design; Optimization problem; Identification (biology); Mathematics; Theoretical computer science","score_opus":0.029121039473464873,"score_gpt":0.2954834439798765,"score_spread":0.26636240450641163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2788137402","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":[0.00013127956,0.00021236353,0.9926483,0.00026362445,0.004468768,0.0008977384,0.000011105837,0.00027890346,0.0010879122],"genre_scores_gemma":[0.03772902,0.000100152734,0.95963246,0.0009477239,0.0013412954,0.0000329743,0.00007181598,0.00006642688,0.00007812307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964607,0.000119112956,0.0011525872,0.00080850744,0.00067734707,0.0007817824],"domain_scores_gemma":[0.995031,0.00050982705,0.0016906367,0.0007312292,0.00176723,0.00027007115],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009416698,0.0005874805,0.0006993177,0.00034593718,0.00090287434,0.001866539,0.0013995804,0.00036555226,0.0000066590615],"category_scores_gemma":[0.00036404686,0.0005400197,0.00044245596,0.000535867,0.000051355582,0.0019095322,0.001678829,0.0016724565,0.0000034397663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028234614,0.000040588922,0.000007919503,0.000032294934,0.000104422244,0.000022211962,0.0012520314,0.9633854,6.221597e-7,0.00267179,0.00009626898,0.0323582],"study_design_scores_gemma":[0.0010832099,0.00014003247,0.000027277412,0.000675057,0.000021159669,0.0001371528,0.00013087875,0.98610026,0.00003886746,0.010919281,0.000102033984,0.0006248212],"about_ca_topic_score_codex":0.0000048191046,"about_ca_topic_score_gemma":0.0000013429443,"teacher_disagreement_score":0.037597742,"about_ca_system_score_codex":0.0006709432,"about_ca_system_score_gemma":0.000611641,"threshold_uncertainty_score":0.99970514},"labels":[],"label_agreement":null},{"id":"W2788310267","doi":"10.1093/biomet/asx022","title":"OUP accepted manuscript","year":2017,"lang":"en","type":"article","venue":"Biometrika","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Mathematics","score_opus":0.039372507185360214,"score_gpt":0.31131264218589033,"score_spread":0.2719401350005301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2788310267","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":[0.00061355246,0.000111913236,0.9835042,0.0006738086,0.0012554413,0.00015730903,0.000005467002,0.00025322638,0.01342512],"genre_scores_gemma":[0.3068594,0.000042240226,0.68845546,0.00027770462,0.00014016517,0.000018142104,0.0000041939925,0.000018019755,0.0041847075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988359,0.000023770139,0.00016125815,0.0004254038,0.00027549025,0.00027812697],"domain_scores_gemma":[0.9980481,0.000041742373,0.00020184735,0.0014160683,0.00016828939,0.00012390918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017120985,0.0001357513,0.0001450587,0.0005521321,0.0004946912,0.000565594,0.0019280623,0.00006533924,0.00009832681],"category_scores_gemma":[0.0005196538,0.00012879557,0.00005975163,0.00082153245,0.000095596944,0.0012642263,0.0006108956,0.00008600558,0.0003642946],"study_design_candidate":"design_other","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":[0.000008176196,0.00017105804,0.0017309631,0.000009419935,0.000043286247,0.00006148476,0.00020143166,0.00014280928,0.0042022723,0.01102795,0.0038071168,0.978594],"study_design_scores_gemma":[0.0050903773,0.00035846597,0.29471743,0.00005059648,0.00002242016,0.0000774061,0.000074255586,0.3330472,0.078193314,0.004757987,0.2817819,0.0018286296],"about_ca_topic_score_codex":0.000019396695,"about_ca_topic_score_gemma":0.0000014122965,"teacher_disagreement_score":0.9767654,"about_ca_system_score_codex":0.00007230432,"about_ca_system_score_gemma":0.000033243483,"threshold_uncertainty_score":0.5454036},"labels":[],"label_agreement":null},{"id":"W2791987457","doi":"10.1016/j.applthermaleng.2018.03.037","title":"Sensitivity analysis of heat exchanger design to uncertainties of correlations","year":2018,"lang":"en","type":"article","venue":"Applied Thermal Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heat exchanger; Variance (accounting); Sensitivity (control systems); Variable (mathematics); Heat transfer coefficient; Mathematics; Heat transfer; Statistics; Thermodynamics; Engineering; Physics; Economics; Mathematical analysis","score_opus":0.014615545345146017,"score_gpt":0.23331913941720706,"score_spread":0.21870359407206102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791987457","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":[0.010590225,0.00000837513,0.9887449,0.000017300621,0.00008536871,0.00016196806,0.0000039740466,0.00008053969,0.00030730787],"genre_scores_gemma":[0.6619746,6.64552e-7,0.3379574,0.000017442884,0.000018620485,0.000010346535,0.0000012572714,0.0000070111146,0.000012614741],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993207,0.000017989558,0.00017491469,0.00019360102,0.0001443799,0.00014842092],"domain_scores_gemma":[0.9992782,0.00016945711,0.000049036473,0.00030406768,0.00014972966,0.000049495182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018422524,0.000102860504,0.00022053366,0.0003447435,0.000034051096,0.000009432887,0.00015558417,0.00003419155,0.000013712756],"category_scores_gemma":[0.000036580805,0.0001058058,0.000051055733,0.0014572359,0.000035740042,0.00011380846,0.000098700846,0.000047594003,0.0000062350437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005415278,0.000013841566,0.000022610722,0.0000028944871,0.00011431368,4.5883945e-7,0.0011639883,0.91944724,0.072628915,0.00408288,0.0000018093567,0.0025156524],"study_design_scores_gemma":[0.00008809704,0.0000242449,0.0032306148,0.0000056901536,0.00003942586,4.404731e-7,0.00002611946,0.92079246,0.07566526,0.0000149889775,0.000015199092,0.00009748581],"about_ca_topic_score_codex":0.00002395379,"about_ca_topic_score_gemma":0.0000033646688,"teacher_disagreement_score":0.6513844,"about_ca_system_score_codex":0.00003359277,"about_ca_system_score_gemma":0.000015102949,"threshold_uncertainty_score":0.4314635},"labels":[],"label_agreement":null},{"id":"W2793283161","doi":"10.1016/j.asoc.2018.01.041","title":"SCGOSR: Surrogate-based constrained global optimization using space reduction","year":2018,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Kriging; Mathematical optimization; Global optimization; Surrogate model; Computer science; Local optimum; Reduction (mathematics); Optimization problem; Local search (optimization); Constraint (computer-aided design); Constrained optimization; Penalty method; Algorithm; Mathematics; Machine learning","score_opus":0.016568139401737837,"score_gpt":0.2762662904990873,"score_spread":0.25969815109734945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793283161","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":[0.0037828034,0.000013714024,0.99178004,0.00014533526,0.0007374891,0.00039818883,0.000003222247,0.00079671125,0.0023425056],"genre_scores_gemma":[0.44756982,3.5926652e-7,0.55211806,0.000116104515,0.00016412832,0.000002614531,0.000008167111,0.000014656643,0.0000060974735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979444,0.00007454513,0.00038033197,0.00076163793,0.00035008526,0.0004889782],"domain_scores_gemma":[0.9984927,0.00009791187,0.00033861346,0.00048492305,0.00044581,0.00014002594],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034699313,0.00027668042,0.0002478105,0.00012927077,0.00062859216,0.00022120895,0.00048736218,0.00011753771,0.000017076803],"category_scores_gemma":[0.00008503774,0.00031925764,0.000058898364,0.0013350667,0.0002802391,0.00036409294,0.00023454915,0.00014692775,0.000033036773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012826923,0.000047956306,0.00008792748,0.0000068132717,0.000013342124,0.0000026877376,0.00019257348,0.9724853,0.0010493312,0.011227967,0.00001806278,0.014855235],"study_design_scores_gemma":[0.00095117564,0.000041660893,0.00006402004,0.000028897035,0.000009774984,0.000037242185,0.00008830415,0.99458647,0.0031956148,0.0006174925,0.000050751187,0.00032859453],"about_ca_topic_score_codex":0.000020091291,"about_ca_topic_score_gemma":0.0000015803645,"teacher_disagreement_score":0.443787,"about_ca_system_score_codex":0.00035875582,"about_ca_system_score_gemma":0.00024347652,"threshold_uncertainty_score":0.999926},"labels":[],"label_agreement":null},{"id":"W2793486732","doi":"10.5267/j.dsl.2017.11.001","title":"A many-objective Jaya algorithm for many-objective optimization problems","year":2018,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Benchmark (surveying); Sorting; Mathematical optimization; Traverse; Algorithm; Computer science; Selection (genetic algorithm); Multi-objective optimization; Measure (data warehouse); Tournament selection; Mathematics; Genetic algorithm; Data mining; Artificial intelligence","score_opus":0.01690443067706444,"score_gpt":0.2949201915517192,"score_spread":0.2780157608746548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793486732","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":[0.0008487231,0.000023752576,0.99304837,0.0012306698,0.0020948686,0.0016620872,0.000022930699,0.00040096432,0.00066762796],"genre_scores_gemma":[0.012899377,0.000014268886,0.9826496,0.0035958937,0.0003467132,0.00027751183,0.000007520573,0.000048593338,0.00016047001],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99430984,0.00010145229,0.00069135317,0.0021437667,0.0016315134,0.0011220729],"domain_scores_gemma":[0.9953875,0.00059972284,0.00047064212,0.0013044706,0.001877717,0.00035995664],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0017182059,0.00047561087,0.00043154537,0.0011980194,0.0014798692,0.0008718347,0.0027464353,0.00013194872,0.000041469557],"category_scores_gemma":[0.0009478327,0.00044080848,0.00018032231,0.004705,0.0013899246,0.0044730795,0.00078492204,0.0002661172,0.00015542452],"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":[0.00006339474,0.00024850637,0.00010386409,0.000009293028,0.00004237924,0.000023216406,0.004239719,0.29201287,0.008155717,0.0050093867,0.001988914,0.6881027],"study_design_scores_gemma":[0.0013660827,0.00032423495,0.0007561669,0.000056694767,0.000010161652,0.00005218149,0.00015323538,0.98488235,0.007853462,0.002640558,0.0013196706,0.0005852024],"about_ca_topic_score_codex":0.000018927076,"about_ca_topic_score_gemma":0.0000040486025,"teacher_disagreement_score":0.6928695,"about_ca_system_score_codex":0.0006851384,"about_ca_system_score_gemma":0.00028606073,"threshold_uncertainty_score":0.99982005},"labels":[],"label_agreement":null},{"id":"W2793895061","doi":"10.1080/19475683.2018.1424736","title":"A comparison of three heuristic optimization algorithms for solving the multi-objective land allocation (MOLA) problem","year":2018,"lang":"en","type":"article","venue":"Annals of GIS","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Canada Foundation for Innovation","keywords":"Mathematical optimization; Simulated annealing; Particle swarm optimization; Computer science; Genetic algorithm; Optimization problem; Heuristic; Location-allocation; Penalty method; Algorithm; Mathematics","score_opus":0.10211118586663939,"score_gpt":0.3801868520672466,"score_spread":0.2780756662006072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793895061","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":[0.00061418,0.00016533479,0.99732906,0.00061221066,0.00017396985,0.0008344881,0.00001964376,0.0000653399,0.00018580222],"genre_scores_gemma":[0.3001567,0.000023089551,0.69952947,0.000085269196,0.00006649259,0.00006501204,0.000011489496,0.000016449734,0.00004600929],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985289,0.00006231674,0.000493097,0.00037564707,0.00028247418,0.00025757437],"domain_scores_gemma":[0.9967121,0.00033064236,0.00059388723,0.00047319406,0.0018368298,0.000053367137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048298066,0.00016758053,0.00030315627,0.00013647793,0.00022682972,0.000047865873,0.0005975245,0.00007452819,0.0000070560427],"category_scores_gemma":[0.0004112746,0.00013589702,0.00009150555,0.0005562423,0.00022215142,0.00049148145,0.0001496068,0.000090994836,0.0000031870952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103923965,0.0007268563,0.004553003,0.00010910893,0.00019180853,6.1310817e-7,0.008117652,0.9156655,0.0011152363,0.0043008453,0.00057715,0.0645383],"study_design_scores_gemma":[0.0005654689,0.00039232394,0.0019767669,0.000053035372,0.000014374395,0.0000017412518,0.00010506699,0.9756278,0.019767513,0.0012326689,0.00011545053,0.00014778625],"about_ca_topic_score_codex":0.00008767032,"about_ca_topic_score_gemma":0.00008254352,"teacher_disagreement_score":0.29954252,"about_ca_system_score_codex":0.000024572604,"about_ca_system_score_gemma":0.00008194507,"threshold_uncertainty_score":0.5541719},"labels":[],"label_agreement":null},{"id":"W2800275207","doi":"10.4018/978-1-5225-2255-3.ch189","title":"A Nature-Inspired Metaheuristic Approach for Generating Alternatives","year":2017,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"York University","funders":"","keywords":"Computer science; Firefly algorithm; Mathematical optimization; Metaheuristic; Artificial intelligence; Mathematics; Machine learning","score_opus":0.02727722670554076,"score_gpt":0.29583463326119436,"score_spread":0.2685574065556536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800275207","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":[3.271346e-7,0.00034319892,0.539674,0.000016646647,0.0005629924,0.00063341483,0.000116072915,0.0001870584,0.45846632],"genre_scores_gemma":[0.0025630381,0.0000062242066,0.8975725,0.0003850671,0.00059000356,0.00014892564,0.000022016091,0.00007361065,0.09863859],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972094,0.000032035234,0.00047144052,0.0012997517,0.00051373243,0.00047363044],"domain_scores_gemma":[0.9969551,0.0000915448,0.00084439403,0.0013976792,0.0005188278,0.00019244685],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019700319,0.0006587321,0.00069615967,0.00011374365,0.000524419,0.00051565556,0.0019883523,0.0005419645,0.000002973826],"category_scores_gemma":[0.00022982094,0.000648495,0.0003456158,0.00001660983,0.00016491594,0.00028284517,0.00057018275,0.00051142223,0.000017254775],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011792291,0.000015139122,7.858609e-7,0.000036350943,0.00017143557,0.000028369082,0.00006731013,0.0021912917,0.000022146734,0.9778701,0.00023860407,0.019346694],"study_design_scores_gemma":[0.001469368,0.00015959048,0.0000063124603,0.00014133954,0.000118017226,0.00009198222,0.000005211789,0.61545724,0.0003357985,0.36188388,0.018963993,0.0013672523],"about_ca_topic_score_codex":0.000010520997,"about_ca_topic_score_gemma":0.000008254747,"teacher_disagreement_score":0.6159862,"about_ca_system_score_codex":0.00033179362,"about_ca_system_score_gemma":0.00026427858,"threshold_uncertainty_score":0.99959666},"labels":[],"label_agreement":null},{"id":"W2800842922","doi":"10.1007/978-3-319-91641-5_25","title":"Robust Design with Surrogate-Assisted Evolutionary Algorithm: Does It Work?","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais","keywords":"Robustness (evolution); Computer science; Surrogate model; Evolutionary algorithm; Artificial intelligence; Mathematical optimization; Machine learning; Algorithm; Mathematics","score_opus":0.028826136284710386,"score_gpt":0.24283001707534085,"score_spread":0.21400388079063046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800842922","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":[0.0000021385797,0.0002078489,0.9949356,0.00070318143,0.0019107404,0.0008520004,0.000010047733,0.00041906003,0.00095942465],"genre_scores_gemma":[0.0006517889,0.000046082307,0.99569386,0.00093459716,0.00047268067,0.00003496753,0.00001352123,0.00008027047,0.0020722044],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939056,0.00011533354,0.00067359844,0.0026873327,0.0015833554,0.0010347734],"domain_scores_gemma":[0.99523205,0.00079651724,0.00055147713,0.0018686333,0.0012272376,0.0003241167],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008620429,0.00093019207,0.000735463,0.0010954434,0.0005818767,0.00061119447,0.0036904074,0.0004439173,0.00010602925],"category_scores_gemma":[0.00012406586,0.0006962957,0.00013260772,0.0018595643,0.0018982432,0.0015856362,0.0013571194,0.00093453896,0.00010460206],"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":[0.000027894015,0.00007683804,0.0000300372,0.000016234828,0.000035860798,0.0002350674,0.0007314282,0.49515855,0.000009762726,0.0006633664,0.00011393543,0.502901],"study_design_scores_gemma":[0.00068910513,0.00036998815,0.00020300112,0.0005805787,0.000016556565,0.00020334385,8.9765274e-7,0.97495615,0.00038244392,0.020187847,0.0012123013,0.0011977886],"about_ca_topic_score_codex":0.000012875694,"about_ca_topic_score_gemma":0.000055063527,"teacher_disagreement_score":0.5017032,"about_ca_system_score_codex":0.0008822862,"about_ca_system_score_gemma":0.0011200531,"threshold_uncertainty_score":0.9995488},"labels":[],"label_agreement":null},{"id":"W2806266970","doi":"10.1115/1.4040485","title":"An Adaptive Aggregation-Based Approach for Expensively Constrained Black-Box Optimization Problems","year":2018,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"CMC Microsystems","keywords":"Computer science; Constraint (computer-aided design); Mathematical optimization; Adaptive sampling; Mathematics","score_opus":0.04697842901350394,"score_gpt":0.2853379376940037,"score_spread":0.23835950868049974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806266970","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":[0.000010233781,0.00001723587,0.99856186,0.00017544381,0.00026087865,0.0008098913,0.00000452301,0.00006849337,0.00009144507],"genre_scores_gemma":[0.09764232,0.0000054137345,0.9017131,0.00033356404,0.00023170935,0.000027295533,0.000004398458,0.000024972556,0.000017240924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979,0.00032899188,0.00064204255,0.0003836667,0.0004596156,0.00028569193],"domain_scores_gemma":[0.99557674,0.00043186196,0.00082719355,0.00031262785,0.0026085908,0.00024301258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010777677,0.00021077198,0.0003399979,0.00022563779,0.0001785662,0.00013298354,0.0007210291,0.00013832582,0.000015921403],"category_scores_gemma":[0.000603507,0.00018329163,0.00012950874,0.0004500873,0.00015068846,0.0010808093,0.000034498877,0.00018222818,0.0000032712903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022155684,0.0002604288,3.552457e-7,0.000006287838,0.000032799268,0.0000035835867,0.00021845337,0.9825791,0.0024972775,0.008063129,0.0001359703,0.0059810425],"study_design_scores_gemma":[0.002106331,0.0031366935,0.0000013095259,0.00004165632,0.000021774202,0.000040770607,0.00009980938,0.9650971,0.025327526,0.0038890955,0.000026241189,0.00021167331],"about_ca_topic_score_codex":9.131508e-7,"about_ca_topic_score_gemma":2.739504e-7,"teacher_disagreement_score":0.09763209,"about_ca_system_score_codex":0.00013899452,"about_ca_system_score_gemma":0.00039381775,"threshold_uncertainty_score":0.74744153},"labels":[],"label_agreement":null},{"id":"W2807347316","doi":"10.1139/cjce-2017-0354","title":"Adaptive multi-objective traffic signal control using NLRMNSGA-II algorithm","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sorting; Genetic algorithm; Algorithm; Computer science; Nonlinear system; Adaptive control; Mathematical optimization; Adaptive algorithm; SIGNAL (programming language); Control theory (sociology); Control (management); Mathematics; Artificial intelligence; Machine learning","score_opus":0.013822251970727287,"score_gpt":0.2219988137569557,"score_spread":0.20817656178622843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807347316","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":[0.0010050724,0.00036597022,0.9969943,0.000054844724,0.0012144706,0.00016972318,0.000017876593,0.000053456686,0.00012429348],"genre_scores_gemma":[0.6047256,0.0000024778624,0.39474398,0.00007173092,0.00039631978,0.0000021774192,3.6029365e-7,0.000029487392,0.00002785156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982786,0.000055701603,0.0004698448,0.00029766042,0.00028103765,0.0006171378],"domain_scores_gemma":[0.99756455,0.000095885516,0.0002913908,0.00024278848,0.0009908358,0.00081452716],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034514195,0.00028123512,0.00037581217,0.0007717487,0.00033062053,0.000118919466,0.00068400666,0.000106249936,0.00006791381],"category_scores_gemma":[0.0001633819,0.00029612545,0.00014552176,0.00072861207,0.0001182037,0.0010681497,0.00004068382,0.00041029238,0.000007746043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057066836,0.000020660875,0.000018931074,0.0000032952814,0.000104215,0.00019658651,0.0015920771,0.98257256,0.00037246954,0.00019229772,0.00004995126,0.0148712685],"study_design_scores_gemma":[0.0013455345,0.00031523005,0.00030515064,0.00009113111,0.000022693126,0.000454762,0.00009235428,0.9959416,0.00047310002,0.000037412232,0.0006021406,0.00031889402],"about_ca_topic_score_codex":0.00022498224,"about_ca_topic_score_gemma":0.007347978,"teacher_disagreement_score":0.60372055,"about_ca_system_score_codex":0.0006823751,"about_ca_system_score_gemma":0.0012077875,"threshold_uncertainty_score":0.9999491},"labels":[],"label_agreement":null},{"id":"W2808165282","doi":"10.1109/icdcs.2018.00031","title":"PEA: Parallel Evolutionary Algorithm by Separating Convergence and Diversity for Large-Scale Multi-Objective Optimization","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Evolutionary algorithm; Computer science; Convergence (economics); Selection (genetic algorithm); Process (computing); Scale (ratio); Operator (biology); Evolutionary computation; Speedup; Mathematical optimization; Parallel algorithm; Algorithm; Machine learning; Mathematics; Parallel computing","score_opus":0.014315566916415367,"score_gpt":0.26900559377486916,"score_spread":0.25469002685845377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808165282","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":[0.000117390475,0.00014362363,0.9978221,0.00014045724,0.00038309052,0.00068663777,0.000083082254,0.00028753438,0.00033606624],"genre_scores_gemma":[0.008491113,0.000050563252,0.9895385,0.00031529638,0.00008961004,0.000058620368,0.000043659344,0.000017134695,0.0013954947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981999,0.00007571251,0.00025389038,0.0007936424,0.00025141865,0.00042546995],"domain_scores_gemma":[0.99846613,0.00013843479,0.00016798914,0.00031295553,0.0007546271,0.00015986564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023876934,0.00023596427,0.00021549384,0.00009881394,0.0012429594,0.00007785809,0.000421595,0.000107867636,0.0000499766],"category_scores_gemma":[0.000102511585,0.00024315645,0.00005848464,0.00040901793,0.00015771324,0.0014348429,0.00085914927,0.00010254721,0.000017276161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038597244,0.0035656546,0.03212132,0.00015859237,0.0006682426,0.000027145992,0.04330251,0.77168554,0.0013278597,0.020306688,0.044502873,0.08194762],"study_design_scores_gemma":[0.0016325583,0.00016387133,0.0010840211,0.000007632613,0.00000991619,0.0000100384805,0.00036645852,0.99526405,0.0003814189,0.00027894496,0.00048075063,0.000320362],"about_ca_topic_score_codex":0.000040060608,"about_ca_topic_score_gemma":0.000019803867,"teacher_disagreement_score":0.2235785,"about_ca_system_score_codex":0.00013482202,"about_ca_system_score_gemma":0.00005905258,"threshold_uncertainty_score":0.99156326},"labels":[],"label_agreement":null},{"id":"W2808305484","doi":"10.3233/jifs-169688","title":"A Kriging-based sequential optimization method with dual transformation for black-box models","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Science Foundation of Heilongjiang Province","keywords":"Black box; Kriging; Transformation (genetics); Dual (grammatical number); Computer science; Mathematical optimization; Mathematics; Artificial intelligence; Machine learning; Chemistry","score_opus":0.03366633057904297,"score_gpt":0.3109798941913449,"score_spread":0.27731356361230197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808305484","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":[0.0001278561,0.000088081906,0.9969316,0.00024771018,0.0011198074,0.00079292234,0.0000101905125,0.00006897281,0.0006128265],"genre_scores_gemma":[0.13931268,0.000021563097,0.85994875,0.00010324926,0.00046093267,0.000038637547,0.000007190699,0.00003102769,0.00007594109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975854,0.00020716111,0.00096829707,0.0002852657,0.0006440392,0.00030981854],"domain_scores_gemma":[0.99584574,0.00017601147,0.0009939257,0.00028966062,0.002528284,0.0001664013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001147535,0.000243022,0.0003998076,0.00045650968,0.00017523851,0.0002825491,0.00047285506,0.00009996135,0.000009472074],"category_scores_gemma":[0.000059514372,0.00018946374,0.0001664504,0.00054971484,0.00008267917,0.00184148,0.000023747663,0.00016998994,0.0000062555455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019242743,0.000086378706,0.0000029008677,0.00005780251,0.000080123624,0.0000064984247,0.0017593295,0.987919,0.00012014398,0.0069052125,0.000107652544,0.002762583],"study_design_scores_gemma":[0.0011655057,0.0010228537,7.0708904e-7,0.00020054787,0.00004248733,0.00019787079,0.00039049928,0.98583645,0.009617165,0.0005277164,0.0007738404,0.00022433246],"about_ca_topic_score_codex":0.000013631856,"about_ca_topic_score_gemma":0.000004648605,"teacher_disagreement_score":0.13918483,"about_ca_system_score_codex":0.00025625178,"about_ca_system_score_gemma":0.0003306375,"threshold_uncertainty_score":0.7726107},"labels":[],"label_agreement":null},{"id":"W2808787830","doi":"10.1109/tcyb.2018.2842158","title":"Multidirectional Prediction Approach for Dynamic Multiobjective Optimization Problems","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":241,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Multi-objective optimization; Particle swarm optimization; Mathematical optimization; Evolutionary algorithm; Population; Computer science; Set (abstract data type); Optimization problem; Pareto principle; Multi-swarm optimization; Mathematics","score_opus":0.015072468403118287,"score_gpt":0.25554914295242037,"score_spread":0.24047667454930208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808787830","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":[0.000071390525,0.000015281252,0.9953471,0.00006692156,0.0015089867,0.001387039,0.00012707658,0.0006393195,0.0008368828],"genre_scores_gemma":[0.17764455,0.000042549316,0.8204163,0.000085714375,0.000114823124,0.00051729433,0.00003362632,0.00004935336,0.0010957943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979234,0.00008604055,0.000398231,0.0008257783,0.00038072327,0.0003858594],"domain_scores_gemma":[0.99816614,0.00016604904,0.0001755268,0.0004908582,0.0008670032,0.00013442228],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019649438,0.0003049944,0.00021613564,0.00033883532,0.0005970408,0.00011690149,0.00038196734,0.00019086337,0.000026409312],"category_scores_gemma":[0.000029127461,0.0003268805,0.00014111122,0.0007685686,0.0001920936,0.0006714681,0.0000051677016,0.00024575184,0.000024564853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003998083,0.00045909686,0.0000032026635,0.000012203768,0.000052902506,2.5003177e-7,0.00067927554,0.9766106,0.00038499327,0.00022623793,0.000032922522,0.021498319],"study_design_scores_gemma":[0.0014462619,0.0005018659,0.00008194282,0.000018391678,0.00003016795,0.000021190852,0.00005293632,0.9894587,0.0075742076,0.00021884165,0.00028711086,0.00030839528],"about_ca_topic_score_codex":0.000014514205,"about_ca_topic_score_gemma":0.00002243759,"teacher_disagreement_score":0.17757316,"about_ca_system_score_codex":0.00037652673,"about_ca_system_score_gemma":0.00010212495,"threshold_uncertainty_score":0.99991834},"labels":[],"label_agreement":null},{"id":"W2809759906","doi":"10.1016/j.advengsoft.2018.06.001","title":"Multi-surrogate-based Differential Evolution with multi-start exploration (MDEME) for computationally expensive optimization","year":2018,"lang":"en","type":"article","venue":"Advances in Engineering Software","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Kriging; Surrogate model; Mathematical optimization; Global optimization; Optimization problem; Computer science; Differential evolution; Meta-optimization; Radial basis function; Algorithm; Mathematics; Artificial intelligence; Artificial neural network; Machine learning","score_opus":0.01604897834074174,"score_gpt":0.2640538693778125,"score_spread":0.24800489103707077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809759906","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":[0.00019897058,0.00014045746,0.99754184,0.00005489661,0.0006705784,0.00080547336,0.000019808635,0.0005659018,0.000002070381],"genre_scores_gemma":[0.1381971,0.0000128121455,0.86118704,0.00005028582,0.00008977022,0.00028794992,0.00011690365,0.000043904467,0.000014250214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833655,0.00003750824,0.00035182302,0.0006214545,0.00028486468,0.00036778385],"domain_scores_gemma":[0.9984691,0.00025936976,0.0001863912,0.00031302846,0.0006882143,0.00008385279],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000100255296,0.0002964229,0.00024113186,0.00031287488,0.00017468192,0.000079596735,0.00033894947,0.00008832889,0.000005507164],"category_scores_gemma":[0.00034849538,0.00030392848,0.00005025578,0.00062287186,0.000082350234,0.0025194406,0.00006032656,0.00012657882,0.000005316508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040190855,0.00010892155,0.0004118704,0.00003969119,0.00000968569,0.000002743608,0.00030654483,0.99647737,0.00011164904,0.0007546587,0.0000026425448,0.0017340229],"study_design_scores_gemma":[0.0029232483,0.00020529622,0.00087029435,0.000116633055,0.0000067916876,0.0000029793785,0.00004963685,0.9944301,0.00077339757,0.00013765188,0.000108411,0.0003755592],"about_ca_topic_score_codex":0.0000040268424,"about_ca_topic_score_gemma":0.000052482636,"teacher_disagreement_score":0.13799812,"about_ca_system_score_codex":0.00039133083,"about_ca_system_score_gemma":0.00009617301,"threshold_uncertainty_score":0.9999413},"labels":[],"label_agreement":null},{"id":"W2810313902","doi":"10.2514/6.2018-2931","title":"A Relative Adequacy Framework for Multimodel Management in Multidisciplinary Design Optimization","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Multidisciplinary approach; Computer science; Multidisciplinary design optimization; Systems engineering; Engineering","score_opus":0.043438364299642346,"score_gpt":0.3353358526690527,"score_spread":0.2918974883694103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810313902","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":[0.000011316085,0.000018303115,0.9955941,0.0003825926,0.00026130636,0.0016283253,0.000002159859,0.00026257138,0.0018393248],"genre_scores_gemma":[0.011032729,0.000020189515,0.98761326,0.00017516747,0.000056458368,0.00035500817,0.000005668272,0.000027322913,0.00071417337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982942,0.00009727517,0.0003406021,0.0006927218,0.00020405087,0.0003711994],"domain_scores_gemma":[0.9985938,0.00040463882,0.0001357832,0.000509075,0.00027275845,0.00008392491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003319782,0.00021068452,0.00018170093,0.00029662868,0.00021194512,0.000073264586,0.00055589253,0.000114921575,0.00003150036],"category_scores_gemma":[0.00017384003,0.00020371331,0.000054668748,0.000859343,0.00008524839,0.0013326441,0.00033252162,0.00013025926,0.000039723567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044033128,0.00013120173,0.00002187343,0.000005844597,0.00001842885,0.000004369336,0.001328584,0.8480316,0.000004540966,0.13692391,0.000028978358,0.013456661],"study_design_scores_gemma":[0.0011972004,0.00015834587,0.00019387584,0.00003940341,0.00000549821,0.0000017117732,0.00013412602,0.9375038,0.0002916378,0.060192965,0.000028832439,0.00025257847],"about_ca_topic_score_codex":0.0000047617564,"about_ca_topic_score_gemma":0.000002985632,"teacher_disagreement_score":0.08947225,"about_ca_system_score_codex":0.0001837901,"about_ca_system_score_gemma":0.000037524613,"threshold_uncertainty_score":0.83071876},"labels":[],"label_agreement":null},{"id":"W2832550360","doi":"10.1080/10095020.2018.1489576","title":"An improved knowledge-informed NSGA-II for multi-objective land allocation (MOLA)","year":2018,"lang":"en","type":"article","venue":"Geo-spatial Information Science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Sorting; Multi-objective optimization; Computer science; Mathematical optimization; Context (archaeology); Pareto principle; Urban sprawl; Compromise; Grid; Operations research; Land use; Geography; Mathematics; Engineering; Algorithm; Civil engineering; Law","score_opus":0.024535211872120988,"score_gpt":0.33641209484612533,"score_spread":0.31187688297400434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2832550360","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":[0.0027093643,0.000006458833,0.9925149,0.00014187294,0.001306742,0.001194697,0.000026871392,0.00038337524,0.0017156895],"genre_scores_gemma":[0.50617,0.0000060305333,0.4928225,0.00045272545,0.00017983023,0.000188136,0.000049804526,0.000010503836,0.00012047423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99761015,0.000046857214,0.00064770685,0.00054032577,0.0004864585,0.00066848093],"domain_scores_gemma":[0.9945189,0.000120904784,0.0005020841,0.0008067287,0.0037465917,0.00030479833],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012091857,0.00027735656,0.00022140001,0.00067748415,0.0017295339,0.0006792205,0.0016061441,0.00011892316,0.000016861539],"category_scores_gemma":[0.0012377708,0.0002614432,0.00006481312,0.0019512123,0.00059943943,0.019510835,0.0004110712,0.00014782778,0.00015867245],"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":[0.00018073442,0.00046207762,0.00043372004,0.00006324115,0.000031874664,3.3803065e-7,0.060210325,0.016267106,0.008083048,0.013407062,0.00023260228,0.90062785],"study_design_scores_gemma":[0.0015299759,0.00063186715,0.004549707,0.000014558607,0.0000047672943,0.0000074229506,0.00027835136,0.9724334,0.01609292,0.00025794946,0.0038345265,0.00036454093],"about_ca_topic_score_codex":0.00024650223,"about_ca_topic_score_gemma":0.00052062713,"teacher_disagreement_score":0.9561663,"about_ca_system_score_codex":0.0004062274,"about_ca_system_score_gemma":0.0011330134,"threshold_uncertainty_score":0.9999838},"labels":[],"label_agreement":null},{"id":"W2860358344","doi":"10.1162/evco_a_00301","title":"Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multiobjective Evolutionary Algorithm","year":2021,"lang":"en","type":"preprint","venue":"Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Science Foundation","keywords":"Leverage (statistics); Artificial intelligence; Evolutionary robotics; Robotics; Computer science; Reinforcement learning; Evolutionary algorithm; Task (project management); Selection (genetic algorithm); Machine learning; Process (computing); Robot; Mathematical optimization; Mathematics","score_opus":0.010928380496214942,"score_gpt":0.2596300563978829,"score_spread":0.24870167590166795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2860358344","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":[0.002705961,0.0015966052,0.9861048,0.00062830024,0.0052446206,0.0026160553,0.000075395605,0.0008778846,0.00015040458],"genre_scores_gemma":[0.33615997,0.000028262417,0.6614496,0.00010216862,0.00061402755,0.0007290889,0.0007161223,0.000089423535,0.00011131444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99342066,0.00093261653,0.0009200064,0.0021962228,0.0017071925,0.0008233042],"domain_scores_gemma":[0.9937002,0.0006659213,0.0010940366,0.0012345859,0.0030655349,0.00023968771],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004575893,0.000996033,0.0007966701,0.0005471338,0.0015362601,0.0005983968,0.0015369029,0.0005379081,0.000033294935],"category_scores_gemma":[0.00013125283,0.0009135124,0.00035122508,0.0013093773,0.0004320683,0.0021696954,0.0024298267,0.0017256665,0.000035851186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042305,0.00073261466,0.0006035536,0.00005320754,0.00024619504,0.00016447912,0.002234121,0.95834833,0.00011976867,0.0006602832,0.00022623032,0.036568925],"study_design_scores_gemma":[0.0017239546,0.00015246974,0.11543206,0.00020035567,0.00011919436,0.00033133503,0.00050157605,0.87878513,0.00005307042,0.0016657605,0.00004995812,0.0009851501],"about_ca_topic_score_codex":0.00037579107,"about_ca_topic_score_gemma":0.000015841224,"teacher_disagreement_score":0.333454,"about_ca_system_score_codex":0.0021633452,"about_ca_system_score_gemma":0.0011395604,"threshold_uncertainty_score":0.9997636},"labels":[],"label_agreement":null},{"id":"W2888663540","doi":"10.1007/978-3-319-99253-2_2","title":"Design of a Surrogate Model Assisted (1 + 1)-ES","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Surrogate model; Context (archaeology); Adaptation (eye); Machine learning; Function (biology); Artificial intelligence; Mathematical optimization; Mathematics","score_opus":0.03626305347064781,"score_gpt":0.27298382236821955,"score_spread":0.23672076889757174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888663540","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":[0.000007956893,0.00015052521,0.9973357,0.00012058583,0.00083509594,0.0005336735,0.000007487788,0.00018548971,0.00082351296],"genre_scores_gemma":[0.008692456,0.00003712383,0.9902159,0.00036532382,0.00012816791,0.000010597846,0.0000031519753,0.000043557062,0.0005037271],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99601716,0.00006220399,0.00066223135,0.0016037086,0.0010508397,0.00060383906],"domain_scores_gemma":[0.9962126,0.0005382426,0.0005925497,0.0015462732,0.0009448679,0.00016547614],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00087913655,0.00056477915,0.00066201354,0.0009770507,0.00019362886,0.00021709454,0.0032861123,0.0003188577,0.000026949217],"category_scores_gemma":[0.00018108902,0.0005310357,0.0001206723,0.0008922508,0.0012436897,0.0008398335,0.0012860722,0.0005031734,0.00003360895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007831393,0.000031376956,0.0000022875274,0.00001634195,0.000011065815,0.000020019073,0.0006565354,0.8581112,0.00034085158,0.0034754223,0.000008694898,0.13731834],"study_design_scores_gemma":[0.00031726286,0.0001590006,0.000010866066,0.00021105257,0.00000676878,0.000036227248,1.0714699e-7,0.9124037,0.0032628016,0.083068594,0.000038037713,0.00048556607],"about_ca_topic_score_codex":0.0000043934156,"about_ca_topic_score_gemma":0.000009595068,"teacher_disagreement_score":0.13683277,"about_ca_system_score_codex":0.00031515356,"about_ca_system_score_gemma":0.00087172654,"threshold_uncertainty_score":0.99971414},"labels":[],"label_agreement":null},{"id":"W2889272453","doi":"10.1039/c8sc02239a","title":"Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories","year":2018,"lang":"en","type":"article","venue":"Chemical Science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":163,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto; Canadian Institute for Advanced Research","funders":"Tata Sons; FAS Division of Science, Harvard University; Harvard University","keywords":"Chimera (genetics); Computer science; Interpretability; A priori and a posteriori; Multi-objective optimization; Mathematical optimization; Artificial intelligence; Machine learning; Mathematics","score_opus":0.013391263366100775,"score_gpt":0.2846620514620578,"score_spread":0.27127078809595706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889272453","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":[0.0017574142,0.000026427651,0.996126,0.00019190602,0.00051117217,0.00045785168,0.0000055944165,0.00052048685,0.0004031325],"genre_scores_gemma":[0.19688188,0.0000039933857,0.8025752,0.0002471721,0.00017526542,0.00007083868,0.0000037442344,0.000016165553,0.00002576545],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99752873,0.00003355005,0.0002786318,0.0010180398,0.00049815257,0.00064288266],"domain_scores_gemma":[0.99715036,0.0002621935,0.00016960091,0.0004593294,0.0016978169,0.0002606927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005523143,0.00022738497,0.0001997133,0.00022169937,0.00075861183,0.00034149396,0.001225204,0.00008401699,0.000017658253],"category_scores_gemma":[0.0016876567,0.00021658522,0.000055223485,0.0031610196,0.00080471206,0.0018167256,0.00035578568,0.00014525042,0.000010386993],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071158174,0.0010483824,0.0012919198,0.00007730538,0.00005143416,0.000008405434,0.00576337,0.11016722,0.82619685,0.023884501,0.00015415288,0.031285275],"study_design_scores_gemma":[0.00047957836,0.000047372047,0.000049564067,0.000015000466,0.000003252915,0.0000018166803,0.000024790375,0.6665193,0.3321093,0.00015907893,0.00038512403,0.00020584094],"about_ca_topic_score_codex":0.0000030652627,"about_ca_topic_score_gemma":0.000001318691,"teacher_disagreement_score":0.5563521,"about_ca_system_score_codex":0.00036745289,"about_ca_system_score_gemma":0.00055337214,"threshold_uncertainty_score":0.8832089},"labels":[],"label_agreement":null},{"id":"W2894147786","doi":"10.1111/anzs.12247","title":"On the existence and constructions of orthogonal designs","year":2018,"lang":"en","type":"article","venue":"Australian & New Zealand Journal of Statistics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Orthogonal array; Latin hypercube sampling; Mathematics; Hypercube; Computer experiment; Class (philosophy); Orthogonal transformation; Compiler; Orthogonal basis; Empirical orthogonal functions; Algorithm; Combinatorics; Computer science; Statistics; Artificial intelligence; Programming language; Monte Carlo method","score_opus":0.04215345534616777,"score_gpt":0.2938439509565809,"score_spread":0.2516904956104131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894147786","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":[0.0056911036,0.000007818778,0.99259865,0.0011923328,0.00028020443,0.000065124026,0.0000386315,0.0000061464425,0.00011997352],"genre_scores_gemma":[0.11496677,0.000045495726,0.8832819,0.000114500086,0.000120510544,4.3204943e-7,7.7942343e-7,0.000006329,0.001463307],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991182,0.00005881705,0.00033275204,0.00011005258,0.00024326003,0.0001369128],"domain_scores_gemma":[0.99847776,0.0003946475,0.00040832846,0.00015702962,0.00043421972,0.00012801848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018668952,0.000092973794,0.00015008796,0.00008044596,0.000104815,0.00005018155,0.00027317417,0.00002983836,0.00005795934],"category_scores_gemma":[0.00021320948,0.000063504,0.00002551137,0.00019660828,0.00031793411,0.00018897984,0.000033547414,0.00015939707,0.000004180436],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000107742795,0.0001588333,0.0034599928,0.0000149954785,0.00015213026,0.00009448418,0.0025319955,0.0017959435,0.00075326656,0.8694148,0.05822564,0.06329019],"study_design_scores_gemma":[0.0066340207,0.0084843645,0.09602091,0.00059869315,0.00021933638,0.005036032,0.0011722103,0.03941304,0.011381239,0.8099155,0.020058334,0.0010663233],"about_ca_topic_score_codex":0.000008789125,"about_ca_topic_score_gemma":0.0000067838537,"teacher_disagreement_score":0.10931679,"about_ca_system_score_codex":0.00001900917,"about_ca_system_score_gemma":0.00015606877,"threshold_uncertainty_score":0.25896177},"labels":[],"label_agreement":null},{"id":"W2895404167","doi":"10.1007/978-981-13-1610-4_1","title":"Optimization of Constrained Engineering Design Problems Using Cohort Intelligence Method","year":2018,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 Windsor","funders":"","keywords":"Simulated annealing; Particle swarm optimization; Swarm intelligence; Mathematical optimization; Computer science; Robustness (evolution); Evolutionary algorithm; Standard deviation; Engineering optimization; Optimization problem; Computational intelligence; Constraint (computer-aided design); Metaheuristic; Artificial intelligence; Machine learning; Algorithm; Mathematics; Statistics","score_opus":0.03241023265900115,"score_gpt":0.29385694691647796,"score_spread":0.2614467142574768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895404167","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":[0.0000041578287,0.0053834924,0.98986083,0.0000033096167,0.0010317386,0.001185767,0.00000539135,0.0001257725,0.0023995368],"genre_scores_gemma":[0.0037590633,0.0016713251,0.99355096,0.000010057373,0.0001330087,0.000014509222,0.000006881591,0.0000658103,0.0007884115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968575,0.00010175812,0.001335431,0.00092926715,0.00039546646,0.00038053415],"domain_scores_gemma":[0.997233,0.0006279405,0.0010538809,0.00048245455,0.00049879577,0.00010392596],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010670158,0.00052018557,0.00085302186,0.00052749494,0.00010651704,0.000112535345,0.0005899021,0.00025026817,0.000015427597],"category_scores_gemma":[0.000100299105,0.0005470663,0.00008787975,0.00027365514,0.00015878114,0.00058112777,0.0003485059,0.00033454047,0.0000031089864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004217107,0.00001353887,0.000019717303,0.000242094,0.00004581212,0.0000075705384,0.0006171061,0.94649947,0.000019919124,0.039192595,8.265877e-7,0.013337124],"study_design_scores_gemma":[0.00010433783,0.00009936945,6.421801e-7,0.0021941916,0.000021215501,0.000096444346,0.00011024853,0.9942977,0.00039161934,0.0016121939,0.0005694826,0.0005025822],"about_ca_topic_score_codex":0.000030704974,"about_ca_topic_score_gemma":0.0000018147292,"teacher_disagreement_score":0.04779819,"about_ca_system_score_codex":0.00022601102,"about_ca_system_score_gemma":0.000096399104,"threshold_uncertainty_score":0.9996981},"labels":[],"label_agreement":null},{"id":"W2898550336","doi":"10.1109/nemo.2018.8503121","title":"Multi-Objective Design of Compact Microwave Components with Data-Driven Surrogates and Pareto Front Decomposition","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Narodowym Centrum Nauki","keywords":"Multi-objective optimization; Pareto principle; Kriging; Computer science; Mathematical optimization; Set (abstract data type); Volume (thermodynamics); Data set; Pareto analysis; Algorithm; Mathematics; Machine learning; Artificial intelligence","score_opus":0.0488784994277955,"score_gpt":0.30441028926718583,"score_spread":0.25553178983939034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898550336","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":[0.008954876,0.000037423815,0.9899593,0.0000647353,0.00009499779,0.00052102155,0.00002909173,0.00012893365,0.0002096375],"genre_scores_gemma":[0.45831317,0.000008651258,0.54155415,0.000040618743,0.000014663166,0.0000033698154,0.000030235175,0.000010722248,0.000024445588],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983709,0.00017600707,0.000272513,0.0006766117,0.0002380348,0.00026596407],"domain_scores_gemma":[0.9981952,0.00021673736,0.00022690902,0.0007559237,0.00047912705,0.00012614476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000197375,0.00022975235,0.0003097086,0.00013904618,0.00016403115,0.00008137611,0.0007048433,0.000053660428,0.000014214528],"category_scores_gemma":[0.000035404813,0.00018634205,0.000019848761,0.00023767543,0.0003350698,0.0011223945,0.00036582164,0.00009420858,0.000022141483],"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":[0.0023588329,0.005596799,0.089069374,0.00021185247,0.0022486213,0.00014657032,0.031168234,0.31124148,0.50637937,0.0044657188,0.0021968689,0.04491626],"study_design_scores_gemma":[0.001529104,0.0003837857,0.026121134,0.000047341236,0.00001626138,0.000036763216,0.00011724732,0.9399448,0.031435195,0.000107193315,0.000014442817,0.00024671506],"about_ca_topic_score_codex":0.00012336735,"about_ca_topic_score_gemma":0.00016187552,"teacher_disagreement_score":0.6287033,"about_ca_system_score_codex":0.000072749266,"about_ca_system_score_gemma":0.000059308353,"threshold_uncertainty_score":0.7598808},"labels":[],"label_agreement":null},{"id":"W2898957569","doi":"10.1115/detc2018-85379","title":"Advanced Primal-Dual Interior-Point Method for the Method of Moving Asymptotes","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; General Motors of Canada","keywords":"Asymptote; Topology optimization; Mathematical optimization; Dual (grammatical number); Point (geometry); Computer science; Interior point method; Topology (electrical circuits); Optimization problem; Convergence (economics); Mathematics; Finite element method; Engineering","score_opus":0.018881176060303997,"score_gpt":0.34420913700444733,"score_spread":0.32532796094414335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898957569","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":[0.000023583296,0.000048397764,0.99608016,0.00074955204,0.0004562026,0.00059633947,0.0000052165465,0.00016430767,0.0018762625],"genre_scores_gemma":[0.0034043442,0.000008403833,0.99514925,0.00053587713,0.00009876681,0.000080008,0.0000010443672,0.000020941923,0.0007013626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984694,0.00014824807,0.00037621293,0.00049419596,0.00021560295,0.0002963256],"domain_scores_gemma":[0.99665046,0.0017005681,0.0002506596,0.0006730017,0.00066272094,0.00006262018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097574294,0.00018406489,0.00026330334,0.00010046273,0.00019562345,0.0000660198,0.0007670366,0.000053471034,0.00006026445],"category_scores_gemma":[0.00073906133,0.0001247128,0.000117770905,0.0004397795,0.00011618503,0.00065593905,0.00047231294,0.00009769287,0.000011741649],"study_design_candidate":"design_other","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":[0.00004854552,0.00004758626,0.000012700834,0.000019008303,0.000074477255,6.8102435e-7,0.0020689953,0.014117464,0.03241253,0.055322517,0.00010935304,0.89576614],"study_design_scores_gemma":[0.00053481036,0.00020856962,0.00010103244,0.000015724594,0.000009971812,0.000014620172,0.00022678314,0.7923719,0.20155466,0.003258038,0.0015536273,0.0001502503],"about_ca_topic_score_codex":0.0000199433,"about_ca_topic_score_gemma":0.000011842831,"teacher_disagreement_score":0.8956159,"about_ca_system_score_codex":0.000060877202,"about_ca_system_score_gemma":0.00007163656,"threshold_uncertainty_score":0.508564},"labels":[],"label_agreement":null},{"id":"W2899199563","doi":"10.1155/2018/8465020","title":"CFD-Based Optimization of Fluid Flow Product Aided by Artificial Intelligence and Design Space Validation","year":2018,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Solver; Computational fluid dynamics; Computer science; Metamodeling; MATLAB; Python (programming language); Engineering design process; Control engineering; Mathematical optimization; Engineering; Mechanical engineering; Mathematics; Aerospace engineering","score_opus":0.02729831506858458,"score_gpt":0.2484841063735433,"score_spread":0.22118579130495872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899199563","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":[0.00012961759,0.000037505837,0.998962,0.000116664785,0.000079543344,0.000507985,0.0000012856964,0.00012379045,0.000041615964],"genre_scores_gemma":[0.22765546,0.0000044449707,0.77225286,0.0000068862287,0.000019606872,0.00003636856,0.0000024809701,0.000015730498,0.0000061832616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987924,0.000043738495,0.00041251877,0.00033605425,0.00020712624,0.00020816292],"domain_scores_gemma":[0.99922603,0.00020231759,0.000092827366,0.00027719428,0.00014542686,0.000056209257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004969905,0.00015401817,0.00019914542,0.00016901588,0.00003686867,0.00005551648,0.00022403788,0.000052579213,0.000014089982],"category_scores_gemma":[0.0006339849,0.00015434272,0.000018754383,0.00057551236,0.00007147189,0.000387556,0.00007281761,0.00009507141,0.0000053858853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031175207,0.00007363428,0.0000021798444,0.00010933606,0.0000037009722,4.2618106e-7,0.00035594715,0.98613954,0.005050609,0.0047822646,0.0000051210013,0.0034741296],"study_design_scores_gemma":[0.000094203904,0.0000652311,0.0000010839487,0.00011238067,0.000002733223,0.0000025737857,0.0000062829295,0.8487443,0.14509459,0.005739844,0.0000032715911,0.0001335484],"about_ca_topic_score_codex":0.0000015646727,"about_ca_topic_score_gemma":2.0177426e-7,"teacher_disagreement_score":0.22752583,"about_ca_system_score_codex":0.000056100813,"about_ca_system_score_gemma":0.000025665253,"threshold_uncertainty_score":0.6293914},"labels":[],"label_agreement":null},{"id":"W2900393606","doi":"","title":"Locally weighted regression models for surrogate-assisted design optimization","year":2016,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; McGill University; Group for Research in Decision Analysis","funders":"","keywords":"Mathematical optimization; Metric (unit); Surrogate model; Optimization problem; Smoothing; Computer science; Mathematics; Smoothness; Statistics; Engineering","score_opus":0.020558377825519426,"score_gpt":0.25144888877840144,"score_spread":0.23089051095288202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900393606","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":[0.00006234608,0.00024337751,0.9924994,0.0033729821,0.00022456866,0.0017652673,0.000033413857,0.0016684763,0.0001301557],"genre_scores_gemma":[0.034165487,0.00023142419,0.96255106,0.0007355266,0.00006684517,0.0010787578,0.000021352638,0.000091962946,0.0010575772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99682426,0.00029434037,0.0006232365,0.00094963907,0.00047155117,0.0008369713],"domain_scores_gemma":[0.99665004,0.0005705278,0.00044543282,0.0012205956,0.0007486196,0.00036481273],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00080739276,0.00046485465,0.00042388684,0.0005390726,0.00040419985,0.0002301752,0.0011667325,0.00034387052,0.000017721983],"category_scores_gemma":[0.00038505404,0.00034990394,0.00018148441,0.0009433806,0.000104175386,0.0021621722,0.00032039365,0.00017606672,0.000010861416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017697128,0.00023244473,0.00009637688,0.000014624604,0.000038851915,0.000021321253,0.00011411379,0.8218489,0.005327025,0.036342368,0.0009480666,0.1348389],"study_design_scores_gemma":[0.001727522,0.00019377324,0.00017635447,0.00012759028,0.0000149661155,0.000051328618,0.000010039794,0.96518296,0.01981048,0.011956702,0.00025059926,0.0004976619],"about_ca_topic_score_codex":0.0001504199,"about_ca_topic_score_gemma":0.0000609177,"teacher_disagreement_score":0.14333403,"about_ca_system_score_codex":0.0007711508,"about_ca_system_score_gemma":0.00036302194,"threshold_uncertainty_score":0.9998953},"labels":[],"label_agreement":null},{"id":"W2903221689","doi":"","title":"Review of measures of the quality of approximated Pareto fronts in multiobjective optimization","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Pareto principle; Multi-objective optimization; Mathematical optimization; Computer science; Quality (philosophy); Pareto analysis; Pareto optimal; Mathematics; Physics","score_opus":0.03290294344312134,"score_gpt":0.2891658533052156,"score_spread":0.25626290986209427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903221689","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":[0.0011131918,0.004727318,0.98866445,0.00068394793,0.0001862103,0.0011085732,0.00007633634,0.000061883526,0.003378073],"genre_scores_gemma":[0.13963006,0.006629375,0.85324633,0.000073142335,0.0000067918704,0.00009676615,0.00010238445,0.000030736617,0.00018443097],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98956394,0.0075539243,0.0013237145,0.0006833165,0.00066498603,0.00021012624],"domain_scores_gemma":[0.98618406,0.00094326836,0.0025348843,0.002726647,0.0075495816,0.000061582505],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0066692294,0.00027540015,0.000726424,0.00021403957,0.00008873144,0.000036209498,0.002278918,0.000204404,0.00002207953],"category_scores_gemma":[0.006784345,0.00024559107,0.00023585753,0.0011141239,0.00044568777,0.00023595388,0.0018907104,0.00036271758,0.0000011257627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022709678,0.011666622,0.029075684,0.04209919,0.0010276571,0.0000029942828,0.08613735,0.5139403,0.011956323,0.10688087,0.0009003754,0.19608556],"study_design_scores_gemma":[0.0012442857,0.0000015161064,0.015444861,0.036422387,0.000045897483,0.000002083015,0.00010463741,0.8103901,0.13273738,0.0029456262,0.00013406927,0.0005271497],"about_ca_topic_score_codex":0.0009499821,"about_ca_topic_score_gemma":0.00048016783,"teacher_disagreement_score":0.2964498,"about_ca_system_score_codex":0.00016773223,"about_ca_system_score_gemma":0.00043424362,"threshold_uncertainty_score":0.99999964},"labels":[],"label_agreement":null},{"id":"W2904647428","doi":"10.1016/j.apm.2018.12.011","title":"A time-space Kriging-based sequential metamodeling approach for multi-objective crashworthiness optimization","year":2018,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Beijing Science and Technology Planning Project; Beijing Institute of Technology Research Fund Program for Young Scholars; National Natural Science Foundation of China","keywords":"Crashworthiness; Metamodeling; Kriging; Crash; Domain (mathematical analysis); Mathematical optimization; Computer science; Optimization problem; Design of experiments; Engineering; Algorithm; Mathematics; Machine learning","score_opus":0.04877985823875721,"score_gpt":0.29175173540359756,"score_spread":0.24297187716484034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904647428","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":[0.000046802295,0.000016839069,0.99502844,0.000058379137,0.00010328764,0.0017473808,0.000009298566,0.0006725357,0.0023170337],"genre_scores_gemma":[0.044967975,0.0000021501376,0.95386493,0.00017527193,0.00017128924,0.0005360724,0.00004117669,0.00009226364,0.00014885471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970839,0.00005840186,0.00062489766,0.0011081735,0.00047579725,0.00064880407],"domain_scores_gemma":[0.9977366,0.0003283662,0.0003060065,0.0007351533,0.0006941515,0.00019972962],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00066507293,0.0004582333,0.00056023034,0.00025553792,0.0005047207,0.00024152682,0.00075208757,0.00019937917,0.000035999765],"category_scores_gemma":[0.00011493288,0.0004382178,0.000172409,0.0007118224,0.0002285415,0.00057564717,0.0001915348,0.0002329553,0.000058603575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034186625,0.00026457966,1.5194477e-7,0.000067852336,0.000043305576,6.3951234e-7,0.00071625883,0.9439991,0.00032931502,0.053991757,0.000008138368,0.0005447111],"study_design_scores_gemma":[0.0014535161,0.000049793965,4.50072e-8,0.00003338593,0.00005356465,0.00000447443,0.000067996356,0.9792206,0.0044244127,0.014204163,0.000013872259,0.00047414104],"about_ca_topic_score_codex":0.0000024722985,"about_ca_topic_score_gemma":2.7363805e-7,"teacher_disagreement_score":0.04492117,"about_ca_system_score_codex":0.00013431547,"about_ca_system_score_gemma":0.00013969967,"threshold_uncertainty_score":0.99980694},"labels":[],"label_agreement":null},{"id":"W2905319669","doi":"10.1002/pamm.201800273","title":"Pareto Front Interpolation Based on Parametric Sensitivity Analysis in a Bi‐Objective Setting","year":2018,"lang":"en","type":"article","venue":"PAMM","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Berger (Canada)","funders":"","keywords":"Interpolation (computer graphics); Parametric statistics; Sensitivity (control systems); Multi-objective optimization; Mathematical optimization; Pareto principle; Mathematics; Front (military); Exploit; Pareto optimal; Polynomial; Applied mathematics; Algorithm; Computer science; Statistics; Engineering; Mathematical analysis; Artificial intelligence; Electronic engineering","score_opus":0.010444534878331047,"score_gpt":0.27203669594645496,"score_spread":0.26159216106812394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905319669","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":[0.010561218,0.000006672326,0.9873846,0.00016167178,0.00016761715,0.0002170284,0.00000417295,0.00013079467,0.0013662069],"genre_scores_gemma":[0.7391092,7.320799e-7,0.26052454,0.00027051868,0.000043538865,0.000014883117,0.000006607842,0.00000790362,0.000022083605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982531,0.00033756762,0.00024522835,0.0005995105,0.00028461785,0.00027999602],"domain_scores_gemma":[0.998466,0.00055770454,0.00017064741,0.00048471044,0.0002476717,0.00007324003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060187397,0.00016558763,0.00024048738,0.0012060298,0.0001108664,0.00008303305,0.00019217991,0.00006481771,0.000021664062],"category_scores_gemma":[0.00077739224,0.0001645548,0.00009211954,0.0035334367,0.00005690445,0.00039556753,0.00009995351,0.00016445937,0.000075612916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008300076,0.00036218474,0.10113537,0.0000081537855,0.00015413274,0.000059587164,0.0035873458,0.79935336,0.0003822099,0.00054063613,0.000050513005,0.09428352],"study_design_scores_gemma":[0.0003369564,0.000086869426,0.1001925,0.000014829154,0.000017432065,0.0000010294962,0.00005836949,0.89793134,0.000990427,0.0001854088,0.000018550423,0.00016627961],"about_ca_topic_score_codex":0.00016704292,"about_ca_topic_score_gemma":0.00050570816,"teacher_disagreement_score":0.728548,"about_ca_system_score_codex":0.00028479673,"about_ca_system_score_gemma":0.000058067744,"threshold_uncertainty_score":0.67103505},"labels":[],"label_agreement":null},{"id":"W2907009959","doi":"10.1002/mcda.1658","title":"Investigating trade‐offs between optimal mobile photo enforcement programme plans","year":2019,"lang":"en","type":"article","venue":"Journal of Multi-Criteria Decision Analysis","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Silhouette; Computer science; Medoid; Set (abstract data type); Partition (number theory); Cluster analysis; Metric (unit); Jaccard index; Multi-objective optimization; Data mining; Index (typography); Operations research; Mathematical optimization; Mathematics; Operations management; Artificial intelligence; Engineering; Machine learning","score_opus":0.03445674400745234,"score_gpt":0.3314569130756071,"score_spread":0.29700016906815474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907009959","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":[0.1375499,0.00010546582,0.8613437,0.000091909205,0.00039575013,0.00038050645,0.00002047722,0.000056195586,0.000056083747],"genre_scores_gemma":[0.41929856,0.00003182238,0.58031946,0.00013347989,0.000087923465,0.000011140351,0.000011691533,0.000018920664,0.00008697534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99583393,0.00022627361,0.0016168704,0.0005896018,0.001203401,0.0005298913],"domain_scores_gemma":[0.99633175,0.00054984813,0.0013273815,0.00075597136,0.0005282498,0.0005068246],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014439612,0.00036198497,0.0010066242,0.001309366,0.0001568889,0.00045860835,0.0014126315,0.00014560061,0.00035100264],"category_scores_gemma":[0.00040145978,0.00030344364,0.0007010834,0.0025478846,0.00006835404,0.0013564635,0.0003182735,0.00042597298,0.00005463543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064878324,0.00062781386,0.030161945,0.000026460291,0.0020744891,0.00009803038,0.0037173352,0.69620746,0.005662564,0.00011696994,0.00023724147,0.2610048],"study_design_scores_gemma":[0.0028482222,0.00045007095,0.008689789,0.00008450637,0.00038426174,0.00004829575,0.00052490144,0.98090494,0.0024264418,0.000084203646,0.0031351089,0.00041924158],"about_ca_topic_score_codex":0.000019229734,"about_ca_topic_score_gemma":0.0000074592676,"teacher_disagreement_score":0.28469747,"about_ca_system_score_codex":0.0002630525,"about_ca_system_score_gemma":0.00014460101,"threshold_uncertainty_score":0.99994177},"labels":[],"label_agreement":null},{"id":"W2911999864","doi":"10.1007/978-3-030-12598-1_9","title":"GDE4: The Generalized Differential Evolution with Ordered Mutation","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Ontario Tech University","funders":"","keywords":"Differential (mechanical device); Mutation; Mathematics; Genetics; Biology; Physics; Thermodynamics; Gene","score_opus":0.00984153339223182,"score_gpt":0.23364580304659055,"score_spread":0.22380426965435873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911999864","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":[0.000073892414,0.00014802814,0.9956047,0.00059992605,0.0016269363,0.0008522831,0.0000051847887,0.00017900468,0.00091003446],"genre_scores_gemma":[0.103664644,0.000023450793,0.893874,0.00070179865,0.0003960445,0.000024646584,0.0000199336,0.0000585626,0.0012369059],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99643636,0.00006953209,0.00040882797,0.0014337302,0.0011121605,0.0005393942],"domain_scores_gemma":[0.99722856,0.00032986674,0.0004212753,0.0013859908,0.00053527404,0.00009904377],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032688226,0.00052462734,0.00042814744,0.0005314004,0.00037959107,0.00050170376,0.002333007,0.00022484001,0.000030675554],"category_scores_gemma":[0.00006664505,0.000354585,0.000093943454,0.00078063045,0.0006407164,0.00080474536,0.0007177985,0.0006503084,0.00005166973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021426218,0.000028974626,0.000025037065,0.000015654708,0.000021353531,0.000030360228,0.0005887756,0.7674169,0.00013151484,0.040963676,0.0000071639865,0.19074917],"study_design_scores_gemma":[0.0007669003,0.00016426836,0.00032691107,0.00011736857,0.000011708238,0.00009397035,3.3313052e-7,0.9631791,0.00039563247,0.03420818,0.00021367669,0.0005219447],"about_ca_topic_score_codex":0.00002866088,"about_ca_topic_score_gemma":0.00010065482,"teacher_disagreement_score":0.1957622,"about_ca_system_score_codex":0.0005383552,"about_ca_system_score_gemma":0.00079372706,"threshold_uncertainty_score":0.9998906},"labels":[],"label_agreement":null},{"id":"W2912321885","doi":"10.5555/3320516.3320763","title":"Green simulation optimization using likelihood ratio estimators","year":2018,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Estimator; Context (archaeology); Computer science; Mathematical optimization; Maximum likelihood; Computer simulation; Reuse; Mathematics; Statistics; Simulation; Engineering","score_opus":0.042296933293259745,"score_gpt":0.32587115029866803,"score_spread":0.2835742170054083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912321885","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":[0.001568918,0.000008404428,0.9959807,0.00014973214,0.0006441098,0.00042383888,0.0000059024323,0.0004339289,0.00078451016],"genre_scores_gemma":[0.60331786,7.772839e-7,0.3961931,0.00018130339,0.00015963575,0.000006312581,0.000013736013,0.000019563435,0.00010774496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978708,0.00013419753,0.0005164249,0.00069941854,0.0004139868,0.00036519603],"domain_scores_gemma":[0.99712974,0.00020854025,0.00033887255,0.0006403489,0.0015321703,0.00015035104],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020383419,0.00029040396,0.00023438665,0.00029511206,0.00033874647,0.00034044535,0.00056646357,0.00013088534,0.00024295837],"category_scores_gemma":[0.00023566518,0.00030947838,0.000068487614,0.00075133843,0.00012997497,0.0025415826,0.00023209587,0.00014022867,0.000108819244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014239896,0.000037469534,0.00041278155,0.0000050950757,0.000013551982,0.000001743593,0.00083366496,0.9846057,0.00013319589,0.0012738238,0.0000040486516,0.012664685],"study_design_scores_gemma":[0.0006117424,0.000092320035,0.0004738313,0.00004381122,0.000011860325,0.0000039955876,0.00003024913,0.99649626,0.0006130319,0.0011237351,0.00015045298,0.0003486918],"about_ca_topic_score_codex":0.000025430338,"about_ca_topic_score_gemma":0.000011388494,"teacher_disagreement_score":0.60174894,"about_ca_system_score_codex":0.00017406781,"about_ca_system_score_gemma":0.00016130003,"threshold_uncertainty_score":0.99993575},"labels":[],"label_agreement":null},{"id":"W2913101259","doi":"10.1109/ssci.2018.8628739","title":"Enhanced Correlation Matrix Based Visualization for Multi- and Many-objective optimization","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Ontario Tech University","funders":"","keywords":"Visualization; Computer science; Plot (graphics); Set (abstract data type); Benchmark (surveying); Mathematical optimization; Convergence (economics); Parallel coordinates; Data visualization; Optimization problem; Data mining; Focus (optics); Variable (mathematics); Mathematics; Algorithm; Statistics","score_opus":0.019082517776213424,"score_gpt":0.3249452592664193,"score_spread":0.30586274149020587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913101259","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":[0.00010128892,0.000017353135,0.9975687,0.00008007184,0.00040554992,0.00094574055,0.000005730111,0.00038594488,0.0004896059],"genre_scores_gemma":[0.09314835,0.000008271611,0.9055833,0.00024543755,0.000078043304,0.000101183796,0.0000406664,0.000025573141,0.00076914777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986522,0.00006777203,0.00027830253,0.00058151025,0.00018228039,0.00023791488],"domain_scores_gemma":[0.99841595,0.0001438447,0.00019755137,0.0002800358,0.0008772392,0.00008538853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019921678,0.00018961717,0.0001616071,0.00022878558,0.00031529032,0.00013930976,0.00020272114,0.00010629871,0.00003988428],"category_scores_gemma":[0.00026362165,0.00019084655,0.000039483224,0.00057757774,0.000092351824,0.0011165604,0.000077664816,0.00005157035,0.00001633901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007464848,0.00017592375,0.0001444124,0.00002377468,0.0000231547,5.3392085e-7,0.0010551981,0.9239749,0.0015753855,0.05810421,0.000079351244,0.014768511],"study_design_scores_gemma":[0.0018083475,0.0002085063,0.00041415455,0.000013929456,0.000008955064,0.0000020987018,0.00005039012,0.9808797,0.016028916,0.0002748249,0.00006982208,0.00024035663],"about_ca_topic_score_codex":0.000007122259,"about_ca_topic_score_gemma":0.000008648677,"teacher_disagreement_score":0.09304706,"about_ca_system_score_codex":0.00010773032,"about_ca_system_score_gemma":0.00006571431,"threshold_uncertainty_score":0.7782497},"labels":[],"label_agreement":null},{"id":"W2914029036","doi":"10.1002/9781118014967.ch12","title":"Randomized Optimization","year":2011,"lang":"en","type":"other","venue":"Wiley series in probability and statistics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Stochastic optimization; Mathematical optimization; Randomness; Randomized algorithm; Stochastic approximation; Computer science; Monte Carlo method; Simulated annealing; Global optimization; Mathematics; Algorithm; Key (lock)","score_opus":0.015890567518318344,"score_gpt":0.2435982088983946,"score_spread":0.22770764138007624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914029036","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":[8.7812445e-8,0.0005902105,0.9398778,0.00003502349,0.00040803055,0.0010190525,0.00015213872,0.00021786104,0.057699807],"genre_scores_gemma":[0.000003367653,0.003030978,0.9486514,0.000048804246,0.000031336396,0.00012334525,0.00006668478,0.000095609015,0.047948506],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826646,0.00031584094,0.0004228812,0.0005901204,0.00017483486,0.00022989191],"domain_scores_gemma":[0.9987868,0.00021662649,0.00028644313,0.00052699697,0.00010722931,0.00007586764],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041195727,0.0002902684,0.0006374654,0.00018636229,0.000053358228,0.00007467253,0.00034216838,0.00022271633,0.00043969837],"category_scores_gemma":[0.00078015303,0.00027575233,0.00003428171,0.0002483701,0.00055353035,0.00027626043,0.00022363632,0.00020476127,0.000009770496],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002062732,0.00023220734,0.0000584125,0.00044178733,0.000079742385,0.000033008364,0.0016924894,0.023788914,1.4462341e-7,0.92740184,0.015321638,0.028887091],"study_design_scores_gemma":[0.042166397,0.000114762566,0.000015275273,0.00042392846,0.000040810253,0.000024461184,0.000028547196,0.6430233,0.000005346541,0.28918368,0.024000537,0.0009729312],"about_ca_topic_score_codex":0.000097550634,"about_ca_topic_score_gemma":0.00020916051,"teacher_disagreement_score":0.63821816,"about_ca_system_score_codex":0.000058157446,"about_ca_system_score_gemma":0.00008988084,"threshold_uncertainty_score":0.9999695},"labels":[],"label_agreement":null},{"id":"W2914865307","doi":"10.1080/13669877.2019.1569093","title":"Reproducibility investigation of elicitation techniques in risk assessment for hydraulic turbines","year":2019,"lang":"en","type":"article","venue":"Journal of Risk Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer 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":"École de Technologie Supérieure","funders":"Mitacs","keywords":"Expert elicitation; Metric (unit); Computer science; Variable (mathematics); Expert opinion; Risk analysis (engineering); Management science; Operations research; Engineering; Mathematics; Statistics; Operations management","score_opus":0.04726016173597463,"score_gpt":0.4065754968099397,"score_spread":0.35931533507396507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914865307","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":[0.35702717,0.00008127014,0.6415671,0.0005300112,0.000112391426,0.00057928666,0.000004292796,0.000013186005,0.00008525076],"genre_scores_gemma":[0.48370808,0.00022072684,0.5159696,0.00001158287,0.000047577087,0.000014609108,0.0000010295691,0.0000064270653,0.000020371564],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971227,0.0008167666,0.0006493859,0.0004477527,0.0007598022,0.00020361548],"domain_scores_gemma":[0.99440193,0.0012960401,0.0007673349,0.0007576853,0.0027088604,0.00006813972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014076705,0.00008255524,0.00024540635,0.00072136783,0.00007120852,0.000053276108,0.00051418954,0.0000636079,0.000003917215],"category_scores_gemma":[0.006267781,0.000069592774,0.00007640256,0.0010572106,0.000086327855,0.0009924175,0.000120680685,0.0005549667,0.0000018315172],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022832117,0.00041212316,0.5918417,0.00016652356,0.00005289747,0.000005535404,0.0038749136,0.053563207,0.022695031,0.0022203734,0.00039608616,0.3245433],"study_design_scores_gemma":[0.0013942339,0.0010751241,0.50592303,0.00017205624,0.0000077275545,0.000010341696,0.0003338513,0.2976336,0.10737617,0.08553251,0.00038607244,0.00015528317],"about_ca_topic_score_codex":0.000088820256,"about_ca_topic_score_gemma":0.000011025671,"teacher_disagreement_score":0.324388,"about_ca_system_score_codex":0.000277326,"about_ca_system_score_gemma":0.00032152142,"threshold_uncertainty_score":0.7503572},"labels":[],"label_agreement":null},{"id":"W2914898599","doi":"10.1007/978-3-030-12598-1_49","title":"IRA-EMO: Interactive Method Using Reservation and Aspiration Levels for Evolutionary Multiobjective Optimization","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Reservation; Computer science; Multi-objective optimization; Mathematical optimization; Pareto principle; Decision maker; Evolutionary algorithm; Interval (graph theory); Optimization problem; Multiobjective programming; Artificial intelligence; Operations research; Algorithm; Mathematics; Machine learning","score_opus":0.04482112435557944,"score_gpt":0.3283234854295944,"score_spread":0.28350236107401494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914898599","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":[0.000012949623,0.00015134603,0.99600667,0.0002210264,0.0013722067,0.0017533065,0.000033888897,0.00013253669,0.00031604356],"genre_scores_gemma":[0.0038935156,0.000013729322,0.99501526,0.0004841381,0.0002467899,0.00003073691,0.000032124175,0.00005284057,0.0002308708],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962862,0.000105879066,0.0006008404,0.0018511412,0.00068203744,0.00047386807],"domain_scores_gemma":[0.99574053,0.001251942,0.00068340264,0.0007689538,0.0014444066,0.00011075611],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008746242,0.0005377019,0.0005298707,0.0011173969,0.00042796019,0.00039614775,0.00096246804,0.0003696839,0.000009404832],"category_scores_gemma":[0.0005465109,0.0005620943,0.00010387623,0.0007148116,0.00030578478,0.0030022338,0.0007689884,0.00052915764,0.0000042375473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019387075,0.000019667279,0.000015459404,0.000026450758,0.000014902288,0.0000026440334,0.0006295614,0.8447291,0.0002291271,0.0038632802,0.0000015244779,0.15044886],"study_design_scores_gemma":[0.0006862748,0.00023734859,0.00017608667,0.0002510733,0.0000151236245,0.000041865314,0.0000014066875,0.9660978,0.0009246114,0.030915748,0.00007526915,0.0005774116],"about_ca_topic_score_codex":0.000025543488,"about_ca_topic_score_gemma":0.000019600078,"teacher_disagreement_score":0.14987145,"about_ca_system_score_codex":0.0011841797,"about_ca_system_score_gemma":0.00076679693,"threshold_uncertainty_score":0.9996831},"labels":[],"label_agreement":null},{"id":"W2914910073","doi":"10.1007/s10479-018-3122-6","title":"Improving the computational efficiency of stochastic programs using automated algorithm configuration: an application to decentralized energy systems","year":2019,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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; University of British Columbia Hospital","funders":"Compute Canada","keywords":"Theory of computation; Computer science; Mathematical optimization; Computational complexity theory; Energy (signal processing); Integer (computer science); Scale (ratio); Stack (abstract data type); Photovoltaic system; Algorithm; Mathematics","score_opus":0.08685261222821998,"score_gpt":0.41343236826343277,"score_spread":0.3265797560352128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914910073","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":[0.00999476,0.00010083275,0.9879788,0.00026780154,0.000101991216,0.0014067224,0.000014177553,0.000103383165,0.0000315218],"genre_scores_gemma":[0.7514115,0.0000046547843,0.24825461,0.00004130074,0.000025221898,0.00014977578,0.000054619828,0.000013377216,0.000044968587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99738055,0.0004215997,0.0005161591,0.00042883927,0.0009116307,0.00034122204],"domain_scores_gemma":[0.99506724,0.00020909769,0.00011957002,0.0006121081,0.0038686802,0.00012330119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011065342,0.00012595112,0.00020896406,0.00034471438,0.00035742877,0.00025209234,0.00076247565,0.00005899542,0.000008529873],"category_scores_gemma":[0.00015783543,0.00010438288,0.00004186088,0.0018198187,0.00014053484,0.0007301152,0.00016062161,0.00012425739,0.0000125270535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070860556,0.0001765072,0.0000064427913,0.000012159984,0.000013880939,2.929335e-7,0.0005288048,0.946901,0.005778976,0.018975725,0.000006768818,0.02759231],"study_design_scores_gemma":[0.00028027594,0.00024056541,0.00008577162,0.000031403666,0.00000222388,0.0000070624546,0.0002700533,0.99603426,0.0028101613,0.00009837019,0.00003125515,0.00010860877],"about_ca_topic_score_codex":0.0008564018,"about_ca_topic_score_gemma":0.00001818024,"teacher_disagreement_score":0.7414167,"about_ca_system_score_codex":0.000058135953,"about_ca_system_score_gemma":0.00043762068,"threshold_uncertainty_score":0.42566103},"labels":[],"label_agreement":null},{"id":"W2917647106","doi":"10.1002/qre.1040","title":"Discussion (3): Jones–Johnson Paper","year":2009,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science","score_opus":0.011034141712087997,"score_gpt":0.28277432417025505,"score_spread":0.27174018245816706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917647106","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":[0.0034212444,0.000033895652,0.98447037,0.010413891,0.00066152913,0.00010153312,0.0000050360904,0.00023992668,0.0006525941],"genre_scores_gemma":[0.5754537,0.00008304198,0.42308676,0.000711292,0.00016060144,0.000011119482,0.00001486983,0.000009102434,0.00046950835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988758,0.00003839891,0.0002808269,0.0003723603,0.00027903586,0.00015358778],"domain_scores_gemma":[0.9993433,0.000087346205,0.00006000908,0.0002944025,0.00012721443,0.0000877738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036946122,0.00012925544,0.00013094295,0.00007732773,0.000065475615,0.0000882897,0.00032786737,0.00006668803,0.000025587251],"category_scores_gemma":[0.00037891706,0.00009550356,0.00005189916,0.00014723152,0.00002264369,0.00077030336,0.000093195194,0.00015861729,0.000009286021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051662177,0.000662609,0.0015847798,0.00006291278,0.00004705768,0.000013018581,0.001569903,0.460995,0.0063232593,0.36830756,0.00039377756,0.15998846],"study_design_scores_gemma":[0.00056677504,0.000061020906,0.13912019,0.00003525893,0.0000026782802,0.000015568032,0.000017039103,0.83173,0.000642212,0.008512168,0.018965818,0.00033128803],"about_ca_topic_score_codex":0.000007900887,"about_ca_topic_score_gemma":4.1420873e-7,"teacher_disagreement_score":0.57203245,"about_ca_system_score_codex":0.00008941808,"about_ca_system_score_gemma":0.000016027727,"threshold_uncertainty_score":0.38945222},"labels":[],"label_agreement":null},{"id":"W2922377918","doi":"10.1007/s12293-019-00282-5","title":"Project portfolio selection and scheduling under a fuzzy environment","year":2019,"lang":"en","type":"article","venue":"Memetic Computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for International Governance Innovation; Balsillie School of International Affairs; University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Project portfolio management; Mathematical optimization; Portfolio; Flexibility (engineering); Pareto principle; Fuzzy logic; Scheduling (production processes); Evolutionary algorithm; Operations research; Project management; Machine learning; Artificial intelligence; Mathematics; Engineering; Systems engineering","score_opus":0.012046640714551271,"score_gpt":0.25060554894108106,"score_spread":0.23855890822652978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922377918","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":[0.068590306,0.00009099681,0.92941517,0.0000825212,0.0002138524,0.00036108604,2.042666e-7,0.00017099104,0.0010748844],"genre_scores_gemma":[0.4575178,0.0000104848,0.5421874,0.00008258213,0.00003835731,0.0000034164418,8.4742067e-7,0.00001087719,0.00014826316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986704,0.00006068533,0.0002226483,0.00052875024,0.00023541275,0.00028210084],"domain_scores_gemma":[0.99943054,0.00008997593,0.00013176544,0.0002460503,0.00004672805,0.000054954056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029204378,0.00015493769,0.00016654939,0.00016344803,0.0001481267,0.00010903286,0.0002061617,0.000044299737,0.000020776855],"category_scores_gemma":[0.00002808178,0.00015800857,0.00003332649,0.0003808542,0.000022291619,0.00026000803,0.00024519092,0.00015299802,0.000046265814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046443256,0.00006484722,0.0032113374,0.000028692179,0.00003710111,0.0000045258735,0.00076148583,0.8960419,0.0052838423,0.008309464,0.000008300891,0.08624384],"study_design_scores_gemma":[0.0004499425,0.00006429641,0.0024759609,0.000024028353,0.0000053375625,0.000035107547,0.00009968165,0.9946404,0.0008744139,0.0008829633,0.0002491528,0.0001986948],"about_ca_topic_score_codex":0.00001266523,"about_ca_topic_score_gemma":5.575787e-7,"teacher_disagreement_score":0.3889275,"about_ca_system_score_codex":0.00011011327,"about_ca_system_score_gemma":0.000046466073,"threshold_uncertainty_score":0.6443403},"labels":[],"label_agreement":null},{"id":"W2925680367","doi":"10.48550/arxiv.1904.03615","title":"Topology of Pareto sets of strongly convex problems","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Precursory Research for Embryonic Science and Technology; Japan Society for the Promotion of Science","keywords":"Simplex; Mathematics; Pareto principle; Regular polygon; Mathematical optimization; Dimension (graph theory); Topology (electrical circuits); Multi-objective optimization; Convex analysis; Combinatorics; Convex optimization; Geometry","score_opus":0.057694667986832454,"score_gpt":0.20861682393397038,"score_spread":0.15092215594713793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2925680367","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":[0.037708092,0.00005008172,0.9591896,0.00003596754,0.0005774427,0.0004904228,0.000042666048,0.00008544326,0.0018202978],"genre_scores_gemma":[0.9654375,0.00008412467,0.033690352,0.00001688773,0.000016116335,9.995092e-7,0.00001675724,0.00001644622,0.0007208656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828845,0.00014167486,0.00031812006,0.0008730272,0.00010581563,0.00027293098],"domain_scores_gemma":[0.99729276,0.00013216546,0.00072314066,0.0012358187,0.00052641035,0.00008968542],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015070541,0.00026139474,0.00055498263,0.00028847528,0.00003698258,0.000013528298,0.0015107126,0.0002787038,0.000038388665],"category_scores_gemma":[0.000037599253,0.0003082887,0.00016681635,0.00047091086,0.00025678743,0.00029467093,0.0016908009,0.0003548733,0.000022271073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015188909,0.00009381307,0.003960977,0.00013412687,0.00008203738,0.000019654683,0.0002780782,0.94395363,0.000069047645,0.051056124,0.000028196948,0.0003091329],"study_design_scores_gemma":[0.0008501173,0.00014497194,0.0019241141,0.00009886462,0.000038688893,0.0000034707189,0.00011727578,0.98139,0.0010680207,0.01390097,0.0001140802,0.00034941194],"about_ca_topic_score_codex":0.00010044649,"about_ca_topic_score_gemma":0.000014164908,"teacher_disagreement_score":0.92772937,"about_ca_system_score_codex":0.00013702389,"about_ca_system_score_gemma":0.00028465615,"threshold_uncertainty_score":0.99993694},"labels":[],"label_agreement":null},{"id":"W2928561833","doi":"10.1162/978-0-262-31709-2-ch139","title":"A hybrid genetic/immune strategy to tackle the multiobjective quadratic assignment problem","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Knapsack problem; Mathematical optimization; Benchmark (surveying); Multi-objective optimization; Quadratic assignment problem; Genetic algorithm; Computer science; Optimization problem; Quadratic equation; Mathematics","score_opus":0.00949706403982447,"score_gpt":0.23855539712973522,"score_spread":0.22905833308991075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2928561833","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":[0.0020717133,0.00005602808,0.98815155,0.0013385705,0.00015929366,0.00200069,0.0000016174927,0.0002377535,0.005982786],"genre_scores_gemma":[0.3653181,0.000006161188,0.6301916,0.0006944159,0.00003783749,0.0006495817,0.000001502998,0.000019112478,0.0030817064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980806,0.00014358886,0.00037085704,0.00057861843,0.0003794678,0.00044686018],"domain_scores_gemma":[0.9984006,0.00014645193,0.00012493048,0.0007989841,0.00036069643,0.00016831743],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016206171,0.0002434915,0.00019628574,0.000094690644,0.00026340428,0.00038942177,0.0009468856,0.00003390444,0.00026470333],"category_scores_gemma":[0.000059479604,0.00016369719,0.00006585799,0.00043900157,0.00006306367,0.0007115268,0.000378302,0.00015944264,0.0010701121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007712807,0.00041787798,0.00009946729,0.000012552205,0.000096412245,0.000016443868,0.0028140217,0.70816463,0.007478044,0.017466936,0.002451782,0.2609741],"study_design_scores_gemma":[0.0006534375,0.00038487054,0.006856698,0.000015130167,0.0000070808496,0.000032692955,0.00045990464,0.966905,0.014941459,0.008728021,0.0005669618,0.00044876357],"about_ca_topic_score_codex":0.00029503496,"about_ca_topic_score_gemma":0.000018244358,"teacher_disagreement_score":0.36324635,"about_ca_system_score_codex":0.00017123473,"about_ca_system_score_gemma":0.000107101296,"threshold_uncertainty_score":0.9997077},"labels":[],"label_agreement":null},{"id":"W2932891632","doi":"","title":"Optimizing DICOM data management with NSGA-G","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"DICOM; Computer science; Database; Operating system","score_opus":0.020721088838804413,"score_gpt":0.2473986961190756,"score_spread":0.2266776072802712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2932891632","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":[0.00022948504,0.00055634056,0.9479646,0.0039148056,0.00038248207,0.00093589735,0.00007261116,0.00059158663,0.045352206],"genre_scores_gemma":[0.029971665,0.0006710905,0.9591217,0.00017824977,0.000021487273,0.00008776495,0.0008483891,0.00007686546,0.009022754],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9933447,0.0026449494,0.00056267355,0.0021101169,0.0008017234,0.00053580396],"domain_scores_gemma":[0.98678344,0.00086556986,0.0007330179,0.009625919,0.0017724765,0.00021955429],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0036590833,0.00053167884,0.0004974768,0.00033484533,0.0003677208,0.0010187981,0.007434413,0.00022239542,0.000048560447],"category_scores_gemma":[0.00035557797,0.0005303496,0.00011075878,0.00072712894,0.0002001477,0.0008794334,0.013400691,0.00079921517,0.000102931255],"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":[0.000048675876,0.0022646566,0.0011667002,0.0009229226,0.0009824123,0.00014842027,0.01900331,0.18721184,0.00021218677,0.39399496,0.00292742,0.3911165],"study_design_scores_gemma":[0.0010772925,6.8973134e-7,0.00066952046,0.0017411857,0.00005796411,0.00002098924,0.000110948866,0.9792307,0.0021540006,0.0017978052,0.012295215,0.0008437104],"about_ca_topic_score_codex":0.00016478395,"about_ca_topic_score_gemma":0.00017685378,"teacher_disagreement_score":0.79201883,"about_ca_system_score_codex":0.00021680357,"about_ca_system_score_gemma":0.00028608227,"threshold_uncertainty_score":0.9997148},"labels":[],"label_agreement":null},{"id":"W2942517280","doi":"10.1109/access.2019.2914697","title":"Multi-Objective Optimization of Manufacturing Process in Carbon Fiber Industry Using Artificial Intelligence Techniques","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Deakin University","keywords":"Computer science; TOPSIS; Energy consumption; Ideal solution; Multi-objective optimization; Profitability index; Mathematical optimization; Process (computing); Efficient energy use; Process engineering; Industrial engineering; Engineering; Mathematics; Operations research; Machine learning","score_opus":0.04490728971614212,"score_gpt":0.34563329134259546,"score_spread":0.30072600162645335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942517280","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":[0.22085397,0.000009790284,0.7779253,0.000014121801,0.00024028054,0.00054855173,0.0000020699185,0.0001121573,0.00029375695],"genre_scores_gemma":[0.6752402,0.0000041085823,0.32463464,0.000023964973,0.000031557393,0.000025514706,0.0000012510959,0.000019129673,0.000019589725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830455,0.00007679743,0.0004703582,0.0005683109,0.00028920124,0.00029080364],"domain_scores_gemma":[0.9988371,0.00007401561,0.00034313186,0.00042084855,0.0002708066,0.00005408871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020879615,0.00021303211,0.00027748483,0.0004362123,0.000040944702,0.00010756268,0.00092151144,0.00023999407,0.000027722474],"category_scores_gemma":[0.000056501158,0.00022459553,0.00003913548,0.00089993124,0.00006006498,0.0017378129,0.00021235552,0.0004116493,0.0000033478705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012755858,0.00010454256,0.00094990025,0.000033023225,0.000007123377,0.0000048036213,0.0004943372,0.9864891,0.0009345422,0.000041276882,1.1438359e-7,0.010928449],"study_design_scores_gemma":[0.000072733455,0.000018612165,0.00027653927,0.00007081088,0.0000023130838,0.000003515229,0.00007618868,0.563669,0.43522498,0.0004306731,4.927507e-7,0.00015412894],"about_ca_topic_score_codex":0.00015289812,"about_ca_topic_score_gemma":0.000014715538,"teacher_disagreement_score":0.45438626,"about_ca_system_score_codex":0.00019351461,"about_ca_system_score_gemma":0.00012225147,"threshold_uncertainty_score":0.915874},"labels":[],"label_agreement":null},{"id":"W2943729245","doi":"10.1007/978-3-319-31204-0_1","title":"Enhanced Multiobjective Population-Based Incremental Learning with Applications in Risk Treaty Optimization","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Speedup; Computer science; Pareto principle; Context (archaeology); Population; Mathematical optimization; Multi-objective optimization; Dual (grammatical number); Parallel computing; Machine learning; Mathematics","score_opus":0.0071618120789763325,"score_gpt":0.2418116852113466,"score_spread":0.23464987313237026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943729245","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":[0.00009173663,0.0000479369,0.99707466,0.00006662067,0.00019715325,0.0012388699,0.000014344405,0.00024172595,0.0010269317],"genre_scores_gemma":[0.22251017,0.000024129538,0.77693605,0.000102855825,0.000100863035,0.00010763031,0.00003828117,0.00004748336,0.00013250927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99616545,0.000104769046,0.0005529859,0.0017891185,0.00083616533,0.00055151625],"domain_scores_gemma":[0.99712116,0.0007436715,0.0006696431,0.0008614281,0.00046467557,0.00013943188],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045525472,0.00058641843,0.0004996806,0.0012831439,0.0004457853,0.00023921148,0.0012386164,0.00025677242,0.000023307768],"category_scores_gemma":[0.00015786818,0.00048197986,0.00008097527,0.0011935689,0.0003658491,0.0010127813,0.00034384715,0.0007455581,0.000012354984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013619178,0.000040288585,0.0016693539,0.00000715547,0.0000069923317,0.000005252601,0.00025571173,0.8692856,0.000030859672,0.0012542602,5.0307822e-8,0.12743086],"study_design_scores_gemma":[0.0011481963,0.0001702125,0.0017411349,0.00026700643,0.00000893056,0.0000052754312,8.3195056e-7,0.9900424,0.0013840062,0.004564211,0.000016004607,0.0006518119],"about_ca_topic_score_codex":0.00008274651,"about_ca_topic_score_gemma":0.0002723031,"teacher_disagreement_score":0.22241844,"about_ca_system_score_codex":0.0014072441,"about_ca_system_score_gemma":0.00046809096,"threshold_uncertainty_score":0.9997632},"labels":[],"label_agreement":null},{"id":"W2944554922","doi":"10.1007/s10898-019-00782-1","title":"Preference-based evolutionary multi-objective optimization for portfolio selection: a new credibilistic model under investor preferences","year":2019,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Concordia University","funders":"Ministerio de Economía y Competitividad; Concordia University","keywords":"Selection (genetic algorithm); Preference; Mathematics; Portfolio; Mathematical optimization; Multi-objective optimization; Portfolio optimization; Econometrics; Machine learning; Computer science; Statistics; Business; Finance","score_opus":0.04571095833041305,"score_gpt":0.28495084844442164,"score_spread":0.2392398901140086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944554922","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":[0.00072958076,0.00017084331,0.99645543,0.00032968965,0.0008442341,0.0009987913,0.000030804196,0.000114995346,0.0003256043],"genre_scores_gemma":[0.0892136,0.0000395397,0.9099233,0.00030128603,0.00015705609,0.00001794006,0.000031312065,0.00002801156,0.0002879517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972521,0.00013887329,0.00092261855,0.0005984903,0.0006948336,0.00039305212],"domain_scores_gemma":[0.9952457,0.00021212082,0.001305302,0.0003475057,0.0025845414,0.00030482243],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032543295,0.00036554315,0.00047857128,0.00030440398,0.0001951618,0.0002006369,0.000706983,0.00023345146,0.00009270479],"category_scores_gemma":[0.00063176226,0.0003488804,0.00021451814,0.001336199,0.0000647502,0.002040876,0.000091712725,0.00023505848,0.000006043527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018773027,0.00023854269,0.00093910785,0.000021281385,0.00006293065,0.0000010413331,0.0000825839,0.9936068,0.000030872907,0.0036738128,0.00044640774,0.0007088959],"study_design_scores_gemma":[0.0033834053,0.00060501,0.0010097327,0.00009066122,0.00006293608,0.000051651925,0.000058080357,0.99084854,0.00006846532,0.003430931,0.00002874481,0.00036185054],"about_ca_topic_score_codex":0.000017774566,"about_ca_topic_score_gemma":0.000008690998,"teacher_disagreement_score":0.08848402,"about_ca_system_score_codex":0.001238397,"about_ca_system_score_gemma":0.0024931147,"threshold_uncertainty_score":0.99989635},"labels":[],"label_agreement":null},{"id":"W2949103490","doi":"10.48550/arxiv.1003.0804","title":"Branch and Bound Algorithms for Maximizing Expected Improvement Functions","year":2010,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Algorithm; Function (biology); Set (abstract data type); Mathematical optimization; Source code; Process (computing); Genetic algorithm; Code (set theory); Gaussian process; Gaussian; Computer experiment; Simulation; Mathematics; Machine learning","score_opus":0.059012045044887294,"score_gpt":0.21414433072215253,"score_spread":0.15513228567726522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949103490","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":[0.020242989,0.000058616963,0.97627777,0.00012170531,0.0015390796,0.0009758103,0.0000626725,0.00038323793,0.00033813826],"genre_scores_gemma":[0.7013569,0.00013596786,0.2952723,0.0001351922,0.00020736887,0.000028818382,0.00007165359,0.000054891425,0.0027369487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976504,0.00004697983,0.0002474553,0.0015184265,0.000099743134,0.0004370069],"domain_scores_gemma":[0.9977988,0.00015373068,0.0003154237,0.0010418267,0.00046957866,0.00022061376],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017097277,0.00039740122,0.0003548354,0.00033389806,0.00046482618,0.00025411232,0.0008849904,0.00034942105,0.000018687258],"category_scores_gemma":[0.000074330295,0.0004809679,0.0001669059,0.000473654,0.00015963212,0.0006174631,0.0015027405,0.0006530981,0.000011594148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016895548,0.0007546777,0.0007939776,0.00032367185,0.0007098414,0.00015676295,0.0017064549,0.7683599,0.0048820563,0.10386053,0.0004522035,0.117830984],"study_design_scores_gemma":[0.0014501552,0.00010805545,0.00033624566,0.000038489685,0.00006494651,0.0000072540597,0.00011684596,0.97769153,0.00076953345,0.017773002,0.0010163771,0.000627542],"about_ca_topic_score_codex":0.00006536144,"about_ca_topic_score_gemma":0.000052083957,"teacher_disagreement_score":0.6811139,"about_ca_system_score_codex":0.00023706241,"about_ca_system_score_gemma":0.00019195872,"threshold_uncertainty_score":0.9997642},"labels":[],"label_agreement":null},{"id":"W2949920955","doi":"10.48550/arxiv.1403.4890","title":"Modeling an Augmented Lagrangian for Blackbox Constrained Optimization","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Augmented Lagrangian method; Computer science; Mathematical optimization; Heuristics; Bottleneck; Benchmark (surveying); Context (archaeology); Lagrangian relaxation; Sensitivity (control systems); Optimization problem; Simple (philosophy); Algorithm; Mathematics","score_opus":0.061969418260469145,"score_gpt":0.21566876393236215,"score_spread":0.153699345671893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949920955","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":[0.002605793,0.000010914022,0.9948863,0.00006108807,0.0005192781,0.0008952371,0.000043344942,0.00057497126,0.00040311387],"genre_scores_gemma":[0.5614481,0.000031980133,0.43786332,0.00009971881,0.00007458993,0.000004924987,0.00016478682,0.0000337831,0.00027881813],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99760723,0.00015479367,0.00029024782,0.0014273621,0.000105856314,0.0004145343],"domain_scores_gemma":[0.99757385,0.000090514535,0.00028344244,0.0011139761,0.00069524074,0.00024296598],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028220165,0.0003993773,0.0003890594,0.00035939517,0.00026165493,0.00017145579,0.0013417281,0.0003270106,0.000018395485],"category_scores_gemma":[0.00008790816,0.0004998557,0.00019805433,0.00047239623,0.000104445106,0.00081183494,0.0006450332,0.00032306748,0.000011324598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029908837,0.00008166744,0.00001106991,0.00003549599,0.000051845567,0.000012381226,0.0001603728,0.9678286,0.000015002999,0.031203274,0.000008398346,0.0005620106],"study_design_scores_gemma":[0.0013262857,0.00008042311,0.0000030509073,0.00005217023,0.00005652838,0.0000028846596,0.00008247923,0.9916048,0.000054213975,0.0061690337,0.000029904684,0.0005381967],"about_ca_topic_score_codex":0.000033001845,"about_ca_topic_score_gemma":0.000015667787,"teacher_disagreement_score":0.5588423,"about_ca_system_score_codex":0.0002715615,"about_ca_system_score_gemma":0.00020429185,"threshold_uncertainty_score":0.9997453},"labels":[],"label_agreement":null},{"id":"W2952346195","doi":"10.1007/s42114-019-00107-6","title":"A multi-objective Gaussian process approach for optimization and prediction of carbonization process in carbon fiber production under uncertainty","year":2019,"lang":"en","type":"article","venue":"Advanced Composites and Hybrid Materials","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Carbonization; Polyacrylonitrile; Process engineering; Fiber; Process (computing); Energy consumption; Materials science; Computer science; Ultimate tensile strength; Mechanical engineering; Composite material; Polymer; Engineering","score_opus":0.009018392970465708,"score_gpt":0.2472421186603419,"score_spread":0.2382237256898762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952346195","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":[0.43721065,0.00008443748,0.5607831,0.000034977395,0.00016705255,0.0015849405,0.000032253036,0.000071312046,0.000031273],"genre_scores_gemma":[0.74316734,0.00004890589,0.25636315,0.000014884843,0.000026553342,0.00021349678,0.000105389256,0.000022499464,0.000037799255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984079,0.00006913627,0.0004161637,0.00071164576,0.00016463296,0.00023051149],"domain_scores_gemma":[0.99895614,0.000047485006,0.00033082505,0.00023350654,0.0003803419,0.000051689793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001901082,0.00022874745,0.0003704972,0.00020327147,0.00007487181,0.0000749178,0.00014025542,0.0000621046,0.000002646841],"category_scores_gemma":[0.000052899333,0.00022365894,0.000019223027,0.0003420907,0.00006469356,0.00084152975,0.00006391554,0.000063265674,1.6362085e-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":[0.000114550465,0.000116142066,0.00065166387,0.00029257947,0.00001633386,2.8107436e-7,0.00064724224,0.9496758,0.047770265,0.00014841616,1.7678016e-7,0.0005665221],"study_design_scores_gemma":[0.0016979292,0.00017063852,0.0016898409,0.00009846716,0.000015525127,0.000015985705,0.00021812964,0.8712633,0.1240965,0.000518643,0.0000013442011,0.00021367698],"about_ca_topic_score_codex":0.0000223364,"about_ca_topic_score_gemma":0.000002238117,"teacher_disagreement_score":0.3059567,"about_ca_system_score_codex":0.00007579168,"about_ca_system_score_gemma":0.000047198682,"threshold_uncertainty_score":0.91205466},"labels":[],"label_agreement":null},{"id":"W2953667433","doi":"10.1115/1.4044109","title":"A Relative Adequacy Framework for Multi-Model Management in Design Optimization","year":2019,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Mathematical optimization; Fidelity; Computer science; Airfoil; Transonic; Engineering design process; Key (lock); Process (computing); Constraint (computer-aided design); Optimization problem; Mathematics; Engineering; Aerodynamics; Aerospace engineering","score_opus":0.07694864023157873,"score_gpt":0.3331516760135233,"score_spread":0.2562030357819446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953667433","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":[0.00000528606,0.000061799954,0.9979944,0.00028460272,0.00037599864,0.0012091992,9.260358e-7,0.000035058063,0.00003271407],"genre_scores_gemma":[0.0048284708,0.0000887359,0.99454963,0.0002623313,0.000031663803,0.000038705475,4.765356e-7,0.000027328095,0.00017263519],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791145,0.0003171391,0.00069873605,0.00035596156,0.00040969485,0.00030699268],"domain_scores_gemma":[0.99770004,0.0008814444,0.0005876711,0.0003168704,0.00038411812,0.00012986237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00161979,0.00019535216,0.000364941,0.00033588518,0.000056808844,0.00007467144,0.0007438906,0.00016443821,0.000015469384],"category_scores_gemma":[0.000600801,0.00017516801,0.00013668695,0.00056603976,0.000014586167,0.0012295401,0.0001186283,0.00037810166,0.000013756132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015408352,0.00018260247,0.0000011118522,0.000009019867,0.000041066116,0.00001637467,0.00018927344,0.8930999,0.000102768434,0.10100198,0.000026740985,0.0051750555],"study_design_scores_gemma":[0.0023284943,0.00042399197,0.000007048913,0.00012277908,0.000017767257,0.000015570784,0.000035019348,0.8971782,0.00078152015,0.09889911,0.000011689974,0.00017883255],"about_ca_topic_score_codex":2.8838394e-7,"about_ca_topic_score_gemma":8.7774424e-8,"teacher_disagreement_score":0.004996223,"about_ca_system_score_codex":0.00026703122,"about_ca_system_score_gemma":0.00011504359,"threshold_uncertainty_score":0.71431446},"labels":[],"label_agreement":null},{"id":"W2953979082","doi":"10.1145/3321707.3321728","title":"A surrogate model assisted (1+1)-ES with increased exploitation of the model","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Surrogate model; Context (archaeology); Computer science; Gaussian process; Mathematical optimization; Black box; Process (computing); Function (biology); Gaussian; Mathematics; Artificial intelligence; Physics","score_opus":0.017569413325023982,"score_gpt":0.22306256983906494,"score_spread":0.20549315651404096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953979082","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":[0.3999418,0.00003122936,0.59917194,0.00019956328,0.00003751539,0.0002972102,0.000004769229,0.000023747565,0.00029223508],"genre_scores_gemma":[0.6908634,0.000009884261,0.30897024,0.00003715681,0.000004048285,0.000012947688,8.906593e-7,0.00000589201,0.00009559269],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989159,0.000019296891,0.00026502786,0.00029516514,0.000374513,0.00013006298],"domain_scores_gemma":[0.99828106,0.000056147634,0.000409379,0.00014504764,0.0010686071,0.000039786497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010463202,0.00013424327,0.00016142722,0.000069806156,0.00013079817,0.000034326273,0.00048043163,0.000042400967,0.000001829198],"category_scores_gemma":[0.000047714384,0.000087854554,0.0000427838,0.00042173467,0.00017169706,0.00042715739,0.0002469101,0.00008617301,9.090064e-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":[0.000026667363,0.000051283707,0.0075637526,0.000056349567,0.000016838998,2.8620383e-8,0.0008451675,0.9675442,0.0052239546,0.016951665,0.000030462652,0.0016896175],"study_design_scores_gemma":[0.0004904347,0.00004755248,0.07221471,0.0000827839,0.000013087207,0.0000089507985,0.0001278704,0.90906656,0.0011449619,0.016700383,0.0000010564374,0.00010165813],"about_ca_topic_score_codex":0.000009358997,"about_ca_topic_score_gemma":0.0000012182185,"teacher_disagreement_score":0.29092154,"about_ca_system_score_codex":0.000037562375,"about_ca_system_score_gemma":0.00021254063,"threshold_uncertainty_score":0.35826045},"labels":[],"label_agreement":null},{"id":"W2954664346","doi":"10.22158/asir.v3n3p92","title":"A Stochastic Simulation-Optimization Method for Generating Waste Management Alternatives Using Population-Based Algorithms","year":2019,"lang":"en","type":"article","venue":"Applied Science and Innovative Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Mathematical optimization; Computer science; Process (computing); Population; Stochastic optimization; Stochastic simulation; Stochastic process; Algorithm; Mathematics","score_opus":0.06961703694971053,"score_gpt":0.4230346416882911,"score_spread":0.3534176047385805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954664346","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":[0.004185122,0.000012006081,0.99310386,0.000089789326,0.00012690171,0.0018805203,0.000005447736,0.00006166304,0.00053471595],"genre_scores_gemma":[0.3180498,0.0000012009301,0.6815819,0.00012493168,0.000042338554,0.0001228456,0.000009287302,0.000014379219,0.000053312593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965552,0.000108371896,0.00036825938,0.0010187881,0.0013429234,0.00060646504],"domain_scores_gemma":[0.99534357,0.00079874496,0.00019416166,0.0004283525,0.0031332239,0.0001019714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037023125,0.00019400743,0.00021536421,0.0012040358,0.0010502238,0.0004583175,0.0006879204,0.000050852857,0.000009463688],"category_scores_gemma":[0.00037443195,0.00018486872,0.000020631169,0.0065385215,0.00025535,0.00118079,0.00041140412,0.00020794608,0.00000576132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012314585,0.00003190667,0.000052327723,0.000022566515,0.000010166894,3.8888606e-7,0.00029512885,0.92386657,0.005710429,0.037479233,0.0000016577486,0.032517318],"study_design_scores_gemma":[0.00081683486,0.000059449216,0.000137607,0.00003022051,0.0000018555352,7.561169e-7,0.00044067556,0.9940621,0.0026083603,0.001610687,0.000012370142,0.0002190755],"about_ca_topic_score_codex":0.000022146996,"about_ca_topic_score_gemma":6.3296164e-7,"teacher_disagreement_score":0.31386468,"about_ca_system_score_codex":0.00039810946,"about_ca_system_score_gemma":0.0003158932,"threshold_uncertainty_score":0.80775785},"labels":[],"label_agreement":null},{"id":"W2954785872","doi":"10.1016/j.asoc.2019.105588","title":"A Two-Engine interaction driven many-objective evolutionary algorithm with feasibility-aware adaptation","year":2019,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"National Key Research and Development Program of China; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China; McGill University","keywords":"Computer science; Benchmark (surveying); Adaptation (eye); Evolutionary algorithm; Multi-objective optimization; Selection (genetic algorithm); Mathematical optimization; Population; Space (punctuation); Pareto principle; Artificial intelligence; Machine learning; Mathematics","score_opus":0.01164351555665493,"score_gpt":0.25376320152564247,"score_spread":0.24211968596898753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954785872","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":[0.0070859413,0.000021407905,0.9888345,0.00007010061,0.0004676638,0.001051645,0.0000046183113,0.0006914623,0.0017726618],"genre_scores_gemma":[0.49622935,0.0000010604011,0.50346726,0.000102128535,0.00007474969,0.000020836065,0.000025929947,0.000025671046,0.00005304206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975552,0.00008981469,0.00038728816,0.0010266462,0.00048426847,0.00045681064],"domain_scores_gemma":[0.99813354,0.00036736747,0.0003449865,0.0006208345,0.00041174187,0.000121503435],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023652191,0.00034295404,0.00033248716,0.00024835274,0.00030526184,0.00013237208,0.000507062,0.0000853895,0.00001919412],"category_scores_gemma":[0.000027828575,0.00034167853,0.000071611255,0.00088882755,0.000062404055,0.0008683163,0.00034128368,0.00043478093,0.00017031547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038928738,0.00012760372,0.0006226274,0.000016470934,0.000050385115,0.000008257577,0.0017310516,0.864181,0.00019868246,0.0045216624,0.000013658031,0.1284897],"study_design_scores_gemma":[0.0017333555,0.00013525871,0.0029202881,0.000058953443,0.0000108405375,0.00005573056,0.0011770712,0.9923413,0.00025882063,0.0008261824,0.00006500221,0.00041723633],"about_ca_topic_score_codex":0.000030726816,"about_ca_topic_score_gemma":0.000005347616,"teacher_disagreement_score":0.4891434,"about_ca_system_score_codex":0.0005505166,"about_ca_system_score_gemma":0.00014187282,"threshold_uncertainty_score":0.9999035},"labels":[],"label_agreement":null},{"id":"W2963265176","doi":"10.5555/3157096.3157258","title":"Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian","year":2016,"lang":"en","type":"other","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Advanced Scientific Computing Research; U.S. Department of Energy; Office of Science; National Science Foundation","keywords":"Augmented Lagrangian method; Lagrangian; Variable (mathematics); Computer science; Bayesian probability; Mathematical optimization; Bayesian optimization; Lagrangian relaxation; Mathematics; Applied mathematics; Artificial intelligence; Mathematical analysis","score_opus":0.0062627147488541444,"score_gpt":0.2231939715461903,"score_spread":0.21693125679733616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963265176","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":[4.180125e-7,0.00031544405,0.91645056,0.0011043046,0.00030461224,0.001573826,0.00015884722,0.002895119,0.077196866],"genre_scores_gemma":[0.00029955842,0.00018983927,0.8434652,0.0011374207,0.0001616014,0.00049701607,0.00010959345,0.0006079519,0.15353183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99615276,0.00025142703,0.0005550214,0.0012912917,0.00068027596,0.0010692363],"domain_scores_gemma":[0.9965704,0.00011966533,0.0007754916,0.0017606686,0.00028751796,0.00048623772],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00033809812,0.0008720209,0.0007350655,0.0011828163,0.00023574464,0.00035800214,0.0014019628,0.0007431807,0.0011876503],"category_scores_gemma":[0.0000898639,0.0007442325,0.00014867158,0.0011804457,0.0003616846,0.0006459397,0.00037002433,0.00048195553,0.00008616873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018223618,0.0009887454,0.00046214476,0.00020819916,0.0010570214,0.00037289754,0.00029990362,0.59529525,0.00073118525,0.22004378,0.12559907,0.054759566],"study_design_scores_gemma":[0.0029608514,0.00020785228,0.00010302775,0.0007675628,0.000087207176,0.00027929363,0.0000614056,0.96264166,0.00071135286,0.0013473401,0.02905464,0.001777786],"about_ca_topic_score_codex":0.0011511194,"about_ca_topic_score_gemma":0.0010762301,"teacher_disagreement_score":0.36734644,"about_ca_system_score_codex":0.000804071,"about_ca_system_score_gemma":0.0007061375,"threshold_uncertainty_score":0.9997254},"labels":[],"label_agreement":null},{"id":"W2964033023","doi":"10.1115/1.4044321","title":"Multi-Fidelity Modeling and Adaptive Co-Kriging-Based Optimization for All-Electric Geostationary Orbit Satellite Systems","year":2019,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Aeronautical Science Foundation of China; International Cooperation and Exchange Programme; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Kriging; Multidisciplinary design optimization; Geostationary orbit; Computer science; Metamodeling; Satellite; Optimization problem; Mathematical optimization; Aerospace engineering; Engineering; Algorithm; Mathematics; Multidisciplinary approach; Machine learning","score_opus":0.049927553643281254,"score_gpt":0.2998007894296624,"score_spread":0.24987323578638113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964033023","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":[0.00024987815,0.00059333805,0.99776345,0.00012760292,0.00034791825,0.00085604936,0.0000048031457,0.000048160506,0.000008823541],"genre_scores_gemma":[0.14748886,0.00013435128,0.8520719,0.00019593438,0.000048541708,0.000016481921,0.0000039236593,0.000021938302,0.000018107376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790066,0.00029936197,0.00071719283,0.00036539565,0.0004364264,0.00028096768],"domain_scores_gemma":[0.9971453,0.00074900355,0.000603575,0.00021880117,0.0011065429,0.0001767907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013971691,0.00019936456,0.00037723727,0.00026932204,0.00010206307,0.00013336579,0.00037538444,0.00011986558,0.000006071044],"category_scores_gemma":[0.00028906844,0.00017865245,0.000102505415,0.00033099292,0.00001238541,0.0008995187,0.000037149744,0.00023812437,0.0000044202775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018259196,0.0000906766,0.0000036854674,0.000021705528,0.000041746334,0.0000073656006,0.0000631754,0.99276525,0.0019015213,0.0019146452,0.000013052591,0.0029945679],"study_design_scores_gemma":[0.0020567307,0.00064592005,0.0000069066223,0.000069510854,0.00002604371,0.0000511509,0.000051040533,0.99482465,0.001116333,0.0009213104,0.00002690969,0.00020348604],"about_ca_topic_score_codex":0.0000042850484,"about_ca_topic_score_gemma":2.1253585e-7,"teacher_disagreement_score":0.14723898,"about_ca_system_score_codex":0.00017557145,"about_ca_system_score_gemma":0.00025059012,"threshold_uncertainty_score":0.72852355},"labels":[],"label_agreement":null},{"id":"W2964235255","doi":"10.48550/arxiv.1706.06926","title":"Trade-off preservation in inverse multi-objective convex optimization","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Mathematical optimization; Inverse; Linear programming; Mathematics; Computer science; Convex optimization; Pareto principle; Measure (data warehouse); Regular polygon","score_opus":0.07990169809114857,"score_gpt":0.229607976579074,"score_spread":0.14970627848792545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964235255","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":[0.004002564,0.000051140054,0.9923834,0.00015999324,0.00083277497,0.0010230433,0.00002754609,0.00033647395,0.0011830458],"genre_scores_gemma":[0.716365,0.0006863703,0.28045294,0.00016915138,0.00008825001,0.000014236357,0.000116310875,0.00006280832,0.002044989],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969231,0.00031435076,0.00037094584,0.0017498903,0.00017197205,0.00046974097],"domain_scores_gemma":[0.99671376,0.00013667844,0.00078212743,0.0018259218,0.0003495071,0.00019199633],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003410389,0.0005004287,0.0005239053,0.000657274,0.0003137763,0.00024727415,0.002322553,0.0005256025,0.000026478583],"category_scores_gemma":[0.00034243282,0.00064329203,0.00018040182,0.0007220337,0.00021943855,0.0027966145,0.0018726678,0.0008749166,0.000032885757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031420892,0.00018851788,0.0022860474,0.000033135257,0.000046708534,0.00016347715,0.00088891346,0.9901316,0.000008089661,0.005272764,0.00004411909,0.00090518896],"study_design_scores_gemma":[0.0019680671,0.000039171544,0.0049031796,0.000112021786,0.0000280834,0.000003336678,0.00012446199,0.98884505,0.000094678275,0.003125455,0.00010792273,0.0006485896],"about_ca_topic_score_codex":0.00023044666,"about_ca_topic_score_gemma":0.00021574748,"teacher_disagreement_score":0.7123624,"about_ca_system_score_codex":0.00083341973,"about_ca_system_score_gemma":0.00040341498,"threshold_uncertainty_score":0.99960184},"labels":[],"label_agreement":null},{"id":"W2964281761","doi":"10.1016/j.artint.2013.10.003","title":"Algorithm runtime prediction: Methods &amp; evaluation","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":384,"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":"Computer science; Parameterized complexity; Generalization; Range (aeronautics); Algorithm; Variety (cybernetics); Machine learning; Artificial intelligence; Mathematics","score_opus":0.08348968459179722,"score_gpt":0.38890349694185905,"score_spread":0.3054138123500618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964281761","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":[0.00009763319,0.000112767135,0.99480695,0.000794411,0.001333629,0.00076899666,0.0000044850312,0.00037679385,0.0017043574],"genre_scores_gemma":[0.006005083,0.000022282815,0.99265516,0.00030967878,0.00020836909,0.00026279571,0.000014071668,0.000018551014,0.0005040326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974608,0.00036613818,0.00054432574,0.0006720205,0.0005770108,0.00037971753],"domain_scores_gemma":[0.9973858,0.00025823124,0.00017265267,0.00075934635,0.0012651859,0.00015877488],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0011849045,0.00021277311,0.00018782553,0.0001866782,0.00027356236,0.0003213013,0.00073795655,0.00010039703,0.0010560116],"category_scores_gemma":[0.00063307525,0.00021253122,0.0000803121,0.0009723053,0.000119455355,0.0016632709,0.00022619627,0.00020175402,0.0028019785],"study_design_candidate":"design_other","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":[0.0000010183608,0.00007014555,0.00000800626,0.0000015031678,0.000012484373,6.297952e-7,0.00048291136,0.019033125,0.0013205993,0.009583506,0.000145095,0.969341],"study_design_scores_gemma":[0.000027161821,0.0000367587,0.00017732756,0.0000069558455,0.000008521979,0.000010786099,0.00007823665,0.8379694,0.02046714,0.13945466,0.0015741856,0.00018885547],"about_ca_topic_score_codex":0.000084161875,"about_ca_topic_score_gemma":0.000009044423,"teacher_disagreement_score":0.9691521,"about_ca_system_score_codex":0.00017840255,"about_ca_system_score_gemma":0.000116424104,"threshold_uncertainty_score":0.9998572},"labels":[],"label_agreement":null},{"id":"W2966894080","doi":"10.1109/cec.2019.8790351","title":"CGDE3: An Efficient Center-based Algorithm for Solving Large-scale Multi-objective Optimization Problems","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Differential evolution; Mathematical optimization; Initialization; Computer science; Population; Evolutionary algorithm; Optimization problem; Algorithm; Meta-optimization; Sampling (signal processing); Continuous optimization; Population-based incremental learning; Evolutionary computation; Multi-objective optimization; Mathematics; Multi-swarm optimization; Genetic algorithm","score_opus":0.01355382209919603,"score_gpt":0.26493944183685386,"score_spread":0.25138561973765783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966894080","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":[0.00021702092,0.00003361869,0.9952644,0.00008283042,0.00081646355,0.0024466247,0.000060922648,0.00068637193,0.00039174096],"genre_scores_gemma":[0.008201355,0.0000045060287,0.99023443,0.00038243402,0.00005787377,0.00023708462,0.00011804405,0.00006290124,0.00070134597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99712706,0.00008820012,0.00044765658,0.0011924063,0.00041189548,0.0007328079],"domain_scores_gemma":[0.9977529,0.00015138292,0.0002623493,0.00082845334,0.00078256294,0.000222331],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045065273,0.0003685506,0.000343983,0.00029479188,0.0003165568,0.0002455812,0.0007699003,0.00014214468,0.00007866952],"category_scores_gemma":[0.0000487621,0.000354238,0.00014878818,0.0006773465,0.00004456257,0.0010588892,0.00021665283,0.00017163476,0.000055487224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008423675,0.0009021364,0.00018630642,0.000018836763,0.000015314907,0.0000010181819,0.00078818173,0.9794744,0.00014622085,0.00032072532,0.000013035196,0.018125366],"study_design_scores_gemma":[0.0046111685,0.00031008234,0.00016842395,0.00003618058,0.000007308271,0.00000505744,0.00024276838,0.99197066,0.0018602606,0.000030593503,0.00027378352,0.0004837192],"about_ca_topic_score_codex":0.00001422996,"about_ca_topic_score_gemma":0.000022541099,"teacher_disagreement_score":0.017641649,"about_ca_system_score_codex":0.00027024324,"about_ca_system_score_gemma":0.00014668064,"threshold_uncertainty_score":0.999891},"labels":[],"label_agreement":null},{"id":"W2967473668","doi":"10.22725/icasp13.093","title":"Bayesian optimization in effective dimensions via kernel-based sensitivity indices","year":2019,"lang":"en","type":"article","venue":"Seoul National University Open Repository (Seoul National University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Safran Electronics (Canada)","funders":"","keywords":"Mathematical optimization; Mathematics; Bayesian optimization; Reproducing kernel Hilbert space; Feature selection; Dimension (graph theory); Optimization problem; Sensitivity (control systems); Computer science; Machine learning; Hilbert space","score_opus":0.0065704039077710856,"score_gpt":0.22049316798354882,"score_spread":0.21392276407577773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967473668","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":[0.0055995416,0.000008748652,0.9346497,0.00050330814,0.00050262065,0.0015407689,0.00006158244,0.00019443825,0.05693928],"genre_scores_gemma":[0.85954446,0.000007288557,0.13462445,0.0002723124,0.00006295115,0.0000019911336,0.00016419207,0.00002585274,0.0052965214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99619704,0.00080775557,0.0002852935,0.0011237162,0.0012071035,0.0003791152],"domain_scores_gemma":[0.9960653,0.0009912052,0.0004295014,0.00036580683,0.0019314453,0.000216751],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006967146,0.00034825565,0.00037303337,0.0017107956,0.0007350373,0.0001960425,0.0012871443,0.00025497994,0.00005611792],"category_scores_gemma":[0.00014356305,0.0004622796,0.00014985322,0.0027845094,0.0001643831,0.0039691697,0.00083258457,0.00042710858,0.000066649256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018182966,0.00037167929,0.0045783455,0.000013320823,0.00007829221,0.00026575837,0.00015310968,0.95003396,0.000534346,0.043494835,0.00008312317,0.00021138451],"study_design_scores_gemma":[0.0036390503,0.00010951306,0.011260587,0.00006285313,0.0000215197,0.00005279006,0.0002270035,0.97993046,0.0007624438,0.0005272586,0.0028130398,0.00059350836],"about_ca_topic_score_codex":0.00022295771,"about_ca_topic_score_gemma":0.00010307868,"teacher_disagreement_score":0.8539449,"about_ca_system_score_codex":0.0030067465,"about_ca_system_score_gemma":0.0012949033,"threshold_uncertainty_score":0.9997829},"labels":[],"label_agreement":null},{"id":"W2967673584","doi":"10.48550/arxiv.1908.05357","title":"Sequential Computer Experimental Design for Estimating an Extreme Probability or Quantile","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Quantile; Computer science; Monte Carlo method; Code (set theory); Computer experiment; Source code; Probability distribution; Quality (philosophy); Algorithm; Statistics; Mathematics; Set (abstract data type); Simulation","score_opus":0.24125816844910142,"score_gpt":0.2674207027034251,"score_spread":0.02616253425432369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967673584","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":[0.011129633,0.000012234571,0.9845399,0.000012819015,0.0016717914,0.002077171,0.00002790672,0.00046164775,0.00006689512],"genre_scores_gemma":[0.32076067,0.0000020595176,0.6787372,0.00004185809,0.00012838288,0.0000102957365,0.000030578696,0.000029723371,0.0002592202],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99689263,0.00031657878,0.00031964146,0.0018789037,0.00013074577,0.00046148655],"domain_scores_gemma":[0.9973841,0.000257202,0.00041123602,0.0013861073,0.0003672637,0.0001941055],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003956131,0.00045139054,0.00046026497,0.00018974423,0.00025353688,0.00022643192,0.0016711738,0.00026815437,0.000039449365],"category_scores_gemma":[0.00005573909,0.0004847899,0.00020153864,0.00034603378,0.00012980142,0.0011361004,0.0016577161,0.00034295538,0.000028623677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012316213,0.00029235004,0.000060443228,0.000066594854,0.00004557211,0.000042670978,0.00046346494,0.99005413,0.0001070213,0.0076851277,0.000045658275,0.0010137956],"study_design_scores_gemma":[0.001123787,0.0003336655,0.000027743909,0.00006188839,0.000021464888,0.000008094373,0.0000620209,0.9893302,0.001226008,0.0071980227,0.000025857706,0.0005812795],"about_ca_topic_score_codex":0.00003398372,"about_ca_topic_score_gemma":0.000006939026,"teacher_disagreement_score":0.30963102,"about_ca_system_score_codex":0.000530874,"about_ca_system_score_gemma":0.00045074068,"threshold_uncertainty_score":0.9997604},"labels":[],"label_agreement":null},{"id":"W2972091841","doi":"10.1115/detc2018-85325","title":"Knowledge Assisted Optimization for Large-Scale Problems: A Review and Proposition","year":2018,"lang":"en","type":"review","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Knowledge engineering; Engineering optimization; Scale (ratio); Domain knowledge; Knowledge-based systems; Optimization problem; Industrial engineering; Management science; Data science; Artificial intelligence; Engineering","score_opus":0.04661742812093883,"score_gpt":0.35356159847491947,"score_spread":0.30694417035398064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972091841","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[7.5524795e-11,0.4965362,0.49958774,0.00003811779,0.000112782225,0.002962076,0.000023013834,0.00016727146,0.0005728026],"genre_scores_gemma":[1.6224748e-9,0.5290287,0.4687046,0.0000839268,0.000054700275,0.0008497629,0.00015116268,0.00003216612,0.0010949785],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99752146,0.00021608843,0.0007325193,0.0010186549,0.00015423448,0.0003570491],"domain_scores_gemma":[0.99773264,0.00013539052,0.00057158497,0.00064575137,0.0007835829,0.0001310777],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00054449245,0.00046939153,0.001308423,0.00022226073,0.00024459712,0.00016522029,0.0005581713,0.00024597996,0.00004402755],"category_scores_gemma":[0.00014132456,0.00036855778,0.00024460853,0.0009683209,0.00005275716,0.00068081723,0.00033187098,0.00015817309,0.000040916428],"study_design_candidate":"design_other","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":[6.651352e-7,0.00017053547,3.0321065e-8,0.07302399,0.000067425615,4.9950035e-7,0.00007254056,0.00010810811,2.7171636e-8,0.0013253293,0.0023250137,0.92290586],"study_design_scores_gemma":[0.0002757818,0.000098564946,6.978862e-8,0.020660687,0.00041643422,0.000057208952,0.0000017436387,0.20455389,4.0539277e-7,0.000053548843,0.7734582,0.00042349313],"about_ca_topic_score_codex":5.6365366e-7,"about_ca_topic_score_gemma":0.000004810186,"teacher_disagreement_score":0.9224824,"about_ca_system_score_codex":0.0001887226,"about_ca_system_score_gemma":0.00027080442,"threshold_uncertainty_score":0.9998766},"labels":[],"label_agreement":null},{"id":"W2973734321","doi":"10.1109/jsyst.2019.2939250","title":"Information-Based Hierarchical Planning for a Mobile Sensing Network in Environmental Mapping","year":2019,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Planner; Adaptive sampling; Motion planning; Distributed computing; Field (mathematics); Gaussian; Sampling (signal processing); Greedy algorithm; Mobile robot; Data mining; Real-time computing; Artificial intelligence; Algorithm; Computer vision; Mathematics","score_opus":0.010763115072522571,"score_gpt":0.2409295779204711,"score_spread":0.23016646284794853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973734321","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":[0.02731163,0.000114900846,0.9697664,0.000028625824,0.0019921376,0.00062769366,0.0000038374483,0.000041205705,0.00011359361],"genre_scores_gemma":[0.77424103,0.0000025089796,0.22526768,0.0001338131,0.00028143282,0.000020002466,0.0000043283067,0.00001119492,0.00003802212],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985967,0.00010797867,0.0005032479,0.00014871988,0.0002824607,0.0003608486],"domain_scores_gemma":[0.99912626,0.00022789439,0.00030839213,0.00018766914,0.00004772092,0.00010206231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005975019,0.00013434273,0.00021170765,0.00023131867,0.00017461675,0.0002520492,0.000265024,0.00006754051,0.000004388375],"category_scores_gemma":[0.000023018234,0.0001310688,0.00006635431,0.00022481635,0.0000188294,0.0010757248,0.000033558474,0.00029528045,0.000029232246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009966953,0.000010952089,0.0016868297,0.000016603311,0.000008250743,0.000008215094,0.0009393735,0.99121666,0.0002271232,0.000033343422,0.000057729736,0.005784927],"study_design_scores_gemma":[0.0012069104,0.00006539996,0.00056200806,0.00020034547,0.0000012528772,0.00019936,0.00027474196,0.9935965,0.000053572225,0.00008129449,0.003597547,0.00016105606],"about_ca_topic_score_codex":0.0000024913286,"about_ca_topic_score_gemma":2.3040502e-7,"teacher_disagreement_score":0.7469294,"about_ca_system_score_codex":0.00027542908,"about_ca_system_score_gemma":0.00009356478,"threshold_uncertainty_score":0.534483},"labels":[],"label_agreement":null},{"id":"W2983933840","doi":"10.1108/ec-04-2019-0146","title":"A sequential sampling method for adaptive metamodeling using data with highly nonlinear relation between input and output parameters","year":2019,"lang":"en","type":"article","venue":"Engineering Computations","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Metamodeling; Latin hypercube sampling; Kriging; Adaptive sampling; Sampling (signal processing); Nonlinear system; Relation (database); Input/output; Computer science; Mathematical optimization; Algorithm; Mathematics; Data mining; Monte Carlo method; Machine learning; Statistics","score_opus":0.08569164701266536,"score_gpt":0.3310697766606284,"score_spread":0.24537812964796307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983933840","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":[0.009267745,0.00003960251,0.98955446,0.000060053226,0.00020156415,0.0005782877,0.00006062653,0.00023251618,0.00000511268],"genre_scores_gemma":[0.104539126,0.0000013456678,0.8952087,0.000018944269,0.00006151061,0.00001239215,0.00011635936,0.000033088927,0.00000854531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986844,0.000039194267,0.00027089607,0.0005872836,0.00018340659,0.0002348219],"domain_scores_gemma":[0.998451,0.0006967475,0.00013353847,0.0004442011,0.00019225155,0.000082294515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031170167,0.00019108497,0.00024636893,0.00024230356,0.00014387314,0.00014122683,0.00035063335,0.00005705415,3.118493e-7],"category_scores_gemma":[0.000094944866,0.00019736163,0.000031698084,0.00042526858,0.000015897982,0.0010995108,0.00023757701,0.0001628238,0.000001951749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041221106,0.00000790307,0.00007854167,0.0000147884875,0.00007486891,5.1211384e-7,0.00029796365,0.9936367,0.00014138261,0.0012457212,5.2864027e-7,0.004496944],"study_design_scores_gemma":[0.00060715957,0.00006149579,0.00031455368,0.000053009015,0.000054189062,0.000010575692,0.000027035849,0.998203,0.00011973568,0.00023413455,0.00006866987,0.00024644955],"about_ca_topic_score_codex":0.000021656151,"about_ca_topic_score_gemma":0.0000011928001,"teacher_disagreement_score":0.09527138,"about_ca_system_score_codex":0.0000851081,"about_ca_system_score_gemma":0.00007757776,"threshold_uncertainty_score":0.8048174},"labels":[],"label_agreement":null},{"id":"W2988052337","doi":"10.1007/s40313-019-00526-2","title":"Applying Social Choice Theory to Solve Engineering Multi-objective Optimization Problems","year":2019,"lang":"en","type":"article","venue":"Journal of Control Automation and Electrical Systems","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Heuristics; Mathematical optimization; Computer science; Heuristic; Social choice theory; Domain (mathematical analysis); Voting; Optimization problem; Mathematics; Mathematical economics","score_opus":0.007045113322051408,"score_gpt":0.23227425077612227,"score_spread":0.22522913745407086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988052337","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":[0.0008097191,0.00028108378,0.9970438,0.00014813083,0.0004743737,0.0010692851,0.0000011896306,0.00010858429,0.00006385889],"genre_scores_gemma":[0.77544993,0.00001538741,0.223703,0.000256986,0.00030385153,0.00011962047,0.0000012498997,0.000028098073,0.000121859564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837273,0.00013751764,0.00059338764,0.00022960753,0.00039990025,0.000266859],"domain_scores_gemma":[0.99825966,0.0003770815,0.0005353006,0.000109882734,0.0005696806,0.00014840039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075551256,0.00017110012,0.00040182692,0.0003788201,0.00012258408,0.00024229877,0.00025972544,0.00009970222,0.000003938822],"category_scores_gemma":[0.0003670242,0.00014853505,0.000082283215,0.00065215665,0.000009360897,0.00089989044,0.00003459219,0.00023295074,0.000010448642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023496023,0.000050950428,0.0001752818,0.000024721903,0.00006316761,0.0000018100114,0.00048582695,0.9802766,0.0017180713,0.0053374693,0.00001803635,0.011824579],"study_design_scores_gemma":[0.0021440936,0.0001902319,0.0022405733,0.00005109952,0.000015529318,0.000061232124,0.000033503045,0.99447346,0.0000480382,0.000050647897,0.00051910884,0.00017248673],"about_ca_topic_score_codex":0.0000049937944,"about_ca_topic_score_gemma":2.9158952e-7,"teacher_disagreement_score":0.7746402,"about_ca_system_score_codex":0.00023450032,"about_ca_system_score_gemma":0.000072157185,"threshold_uncertainty_score":0.60570836},"labels":[],"label_agreement":null},{"id":"W2993305012","doi":"10.1016/j.finel.2020.103400","title":"Multi-fidelity bayesian optimization using model-order reduction for viscoplastic structures","year":2020,"lang":"en","type":"article","venue":"Finite Elements in Analysis and Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Safran Electronics (Canada)","funders":"","keywords":"Viscoplasticity; Reduction (mathematics); Fidelity; Model order reduction; Computer science; Mathematical optimization; Bayesian optimization; Computation; High fidelity; Finite element method; Decomposition; Bayesian probability; Algorithm; Mathematics; Artificial intelligence; Engineering; Constitutive equation; Structural engineering","score_opus":0.05786467632426533,"score_gpt":0.3163782550403253,"score_spread":0.25851357871605996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2993305012","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":[0.00012411755,0.00003387712,0.9992314,0.00007376134,0.000050195995,0.00041877135,0.000013502445,0.000048123016,0.0000062514755],"genre_scores_gemma":[0.10966278,0.00002612636,0.8900473,0.00015716486,0.00002344285,0.000024837622,0.00003185322,0.0000113973165,0.00001508317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849164,0.00011297079,0.00043235123,0.0005647794,0.00018248761,0.00021576305],"domain_scores_gemma":[0.9991958,0.00009305773,0.00020880532,0.00019464307,0.00021039834,0.00009727246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002508805,0.00016987209,0.00026646524,0.00033587325,0.00016019655,0.00012526207,0.00019115648,0.000058622532,0.000016968635],"category_scores_gemma":[0.00025443413,0.00017104136,0.00006274369,0.0017013251,0.000026999865,0.000531751,0.000079404395,0.00008116203,3.94699e-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":[0.000022748616,0.00003221097,0.00026889856,0.0000079631845,0.0000961269,7.009157e-7,0.00036683734,0.9962897,0.00027706154,0.00012064067,0.000005004917,0.0025121497],"study_design_scores_gemma":[0.0008035261,0.000046891928,0.00011097504,0.0000050062736,0.000115053816,4.7853183e-7,0.00004368635,0.9979595,0.000256166,0.00047515877,0.000004343523,0.00017924784],"about_ca_topic_score_codex":0.0000117961235,"about_ca_topic_score_gemma":0.0000053489052,"teacher_disagreement_score":0.10953866,"about_ca_system_score_codex":0.00006636189,"about_ca_system_score_gemma":0.000056166802,"threshold_uncertainty_score":0.69748646},"labels":[],"label_agreement":null},{"id":"W2996735224","doi":"10.3390/agriculture10010003","title":"Time–Cost–Quality Trade-Off in a Broiler Production Project Using Meta-Heuristic Algorithms: A Case Study","year":2019,"lang":"en","type":"article","venue":"Agriculture","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Particle swarm optimization; Algorithm; Production (economics); Quality (philosophy); Fuzzy logic; Mathematical optimization; Process (computing); Meta heuristic; Interval (graph theory); Computer science; Heuristic; Function (biology); Mathematics; Operations research; Economics; Artificial intelligence","score_opus":0.054499627479857544,"score_gpt":0.33105631575690614,"score_spread":0.2765566882770486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996735224","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":[0.737791,0.00097038434,0.2433881,0.001076612,0.0017425773,0.01354236,0.00004363429,0.00095049775,0.0004948149],"genre_scores_gemma":[0.7231783,0.000020440399,0.27346423,0.00016719941,0.00041526798,0.00036349564,0.000027062577,0.000051406565,0.0023125617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970312,0.0005222723,0.00049618864,0.0010855374,0.00046762935,0.00039714065],"domain_scores_gemma":[0.99865294,0.000098118675,0.00026871913,0.0006646379,0.0002344164,0.000081156024],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005824872,0.00035916318,0.00053690624,0.00018846743,0.00015072194,0.00013066662,0.00041509085,0.000119470686,0.000024437777],"category_scores_gemma":[0.00017288436,0.00024529625,0.00014031559,0.0018426271,0.000035748653,0.0011430497,0.00018748347,0.00038401893,0.00005171163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001319011,0.010273875,0.004799148,0.00033815549,0.0031250715,0.008722042,0.09775454,0.72652507,0.026955385,0.0006723005,0.0035109504,0.11719159],"study_design_scores_gemma":[0.0066712215,0.0009891274,0.013575398,0.00013210201,0.0009910872,0.0133548165,0.019626867,0.93301874,0.0037141177,0.00031577662,0.004261827,0.003348943],"about_ca_topic_score_codex":0.00036377908,"about_ca_topic_score_gemma":0.00010358613,"teacher_disagreement_score":0.20649368,"about_ca_system_score_codex":0.00025398948,"about_ca_system_score_gemma":0.00009939248,"threshold_uncertainty_score":0.99999994},"labels":[],"label_agreement":null},{"id":"W3001500280","doi":"10.2514/1.a34616","title":"Optimization of a Supersonic Rocket-Based Combined Cycle Inlet Using Differential Evolution","year":2020,"lang":"en","type":"article","venue":"Journal of Spacecraft and Rockets","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Mach number; Supersonic speed; Rocket (weapon); Aerospace engineering; Differential evolution; Mechanics; Inlet; Control theory (sociology); Physics; Mathematics; Engineering; Mathematical optimization; Computer science; Mechanical engineering","score_opus":0.015361678713844902,"score_gpt":0.2392464868236812,"score_spread":0.2238848081098363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3001500280","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":[0.12193239,0.00016750096,0.8766994,0.00089835824,0.00017304132,0.0000969734,0.000002263627,0.000020582453,0.000009492263],"genre_scores_gemma":[0.7416819,0.000027695292,0.25813127,0.00007331656,0.00006920867,5.190521e-7,0.0000011484204,0.000009928271,0.0000050639533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887913,0.00007208769,0.0003852433,0.00017932973,0.00032293226,0.00016126146],"domain_scores_gemma":[0.9987997,0.000050914547,0.00049390044,0.00011340544,0.00037090306,0.00017119743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009784885,0.00014168286,0.00030082706,0.00017928921,0.0001154288,0.00004630741,0.00023792502,0.00007052793,0.000014718607],"category_scores_gemma":[0.000104131694,0.00012549596,0.00009067706,0.00040948144,0.000043399934,0.0006564709,0.000096946154,0.00016687858,5.793806e-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":[0.00017122328,0.00012594048,0.0018699692,0.000043900425,0.000049535858,0.000015923699,0.001154784,0.9825566,0.011299919,0.00009534386,0.0000385897,0.0025782953],"study_design_scores_gemma":[0.002166178,0.00036838755,0.001024552,0.00006259402,0.000029544524,0.000027218539,0.000119520235,0.9906549,0.005318765,0.000083679144,0.000015787513,0.00012887614],"about_ca_topic_score_codex":0.0000042730053,"about_ca_topic_score_gemma":0.0000012307812,"teacher_disagreement_score":0.6197495,"about_ca_system_score_codex":0.00007873696,"about_ca_system_score_gemma":0.00014938884,"threshold_uncertainty_score":0.51175773},"labels":[],"label_agreement":null},{"id":"W3003586727","doi":"10.1016/j.asoc.2020.106143","title":"Adaptive repair method for constraint handling in multi-objective genetic algorithm based on relationship between constraints and variables","year":2020,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Institute for Computing, Information and Cognitive Systems","keywords":"Mathematical optimization; Constraint (computer-aided design); Feasible region; Computer science; Evolutionary algorithm; Genetic algorithm; Metric (unit); Multi-objective optimization; Convergence (economics); Variable (mathematics); Optimization problem; Algorithm; Mathematics; Engineering","score_opus":0.05105968804820702,"score_gpt":0.3041225723608524,"score_spread":0.25306288431264534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003586727","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":[0.0002683761,0.00002747895,0.99746454,0.00016792926,0.00007674478,0.0012884352,0.000030473959,0.00044385882,0.00023217617],"genre_scores_gemma":[0.391816,3.6170857e-7,0.6076077,0.00045124872,0.000052518826,0.00003926274,0.0000085666725,0.000022870778,0.0000014685909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997415,0.00020046768,0.00056613755,0.0011074464,0.00024408888,0.00046682602],"domain_scores_gemma":[0.99353325,0.0055207796,0.00029248427,0.00027283188,0.00017081239,0.00020986336],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007734982,0.00033448316,0.00047304653,0.00021098026,0.00035317437,0.000098086115,0.00032608892,0.00015511236,0.0000016881953],"category_scores_gemma":[0.00082191156,0.000376187,0.000085187006,0.0006835072,0.00019219297,0.00014512155,0.00019997866,0.0004214266,0.00000397357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000325723,0.00007542913,0.004944837,0.0000397885,0.000046518093,0.000013589457,0.002821442,0.7180817,0.00005504381,0.011600945,0.0000051248453,0.262283],"study_design_scores_gemma":[0.0027643885,0.00013667463,0.011380208,0.00006589302,0.0000186448,0.0000046030286,0.00042381187,0.98241067,0.00027105588,0.0021261917,0.000011255424,0.00038657687],"about_ca_topic_score_codex":0.000010618565,"about_ca_topic_score_gemma":0.00000142741,"teacher_disagreement_score":0.39154762,"about_ca_system_score_codex":0.00014291656,"about_ca_system_score_gemma":0.00019407876,"threshold_uncertainty_score":0.999869},"labels":[],"label_agreement":null},{"id":"W3003818284","doi":"","title":"Review of the quality of approximated Pareto fronts in multiobjective optimization, Journées de l'optimisation","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Multi-objective optimization; Pareto principle; Quality (philosophy); Computer science; Mathematical optimization; Pareto optimal; Mathematics; Physics","score_opus":0.022106832088032848,"score_gpt":0.28486614804166255,"score_spread":0.2627593159536297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003818284","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":[0.0017415119,0.0032894362,0.98858654,0.0018588875,0.00019378135,0.0010641791,0.00006530867,0.00009790379,0.0031024485],"genre_scores_gemma":[0.06226113,0.007617883,0.929348,0.00018098122,0.000011946531,0.00012143791,0.00011903065,0.000034708828,0.00030486382],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.988844,0.008312299,0.0012595855,0.00073425006,0.00056319754,0.00028668588],"domain_scores_gemma":[0.98783004,0.0010649665,0.002266183,0.0024808126,0.0062643774,0.00009361827],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0075883917,0.00031095778,0.0006190873,0.00022856086,0.00016813868,0.00009079975,0.002260856,0.00024232303,0.00003893901],"category_scores_gemma":[0.0072997003,0.00028667945,0.00022715497,0.0010501437,0.0003738016,0.0003399329,0.0017026128,0.00048356733,0.0000021780088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009887671,0.005597372,0.02125619,0.016432209,0.00050683203,0.0000034087143,0.07004991,0.7476819,0.003584582,0.059573144,0.0010849874,0.074130565],"study_design_scores_gemma":[0.0008411122,8.446884e-7,0.011130997,0.018872708,0.00003016097,0.000003992626,0.00009411121,0.9293901,0.035619598,0.0034849853,0.00011956308,0.00041184085],"about_ca_topic_score_codex":0.0007981865,"about_ca_topic_score_gemma":0.00042510888,"teacher_disagreement_score":0.18170817,"about_ca_system_score_codex":0.0003573214,"about_ca_system_score_gemma":0.00067336584,"threshold_uncertainty_score":0.9999585},"labels":[],"label_agreement":null},{"id":"W3011340068","doi":"10.1115/1.4046650","title":"Sequential Radial Basis Function-Based Optimization Method Using Virtual Sample Generation","year":2020,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Beijing Institute of Technology; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Radial basis function; Computer science; Metamodeling; Mathematical optimization; Kriging; Robustness (evolution); Optimization problem; Artificial intelligence; Algorithm; Machine learning; Mathematics","score_opus":0.10168092833975943,"score_gpt":0.3123270109360753,"score_spread":0.21064608259631584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011340068","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":[0.000029223898,0.000023650917,0.9979077,0.00082527776,0.00095580315,0.00019299281,0.000004174197,0.000057437854,0.0000037512123],"genre_scores_gemma":[0.016254127,0.000007924722,0.9815163,0.0014063965,0.00078267173,0.00000290945,0.0000042308684,0.000022609769,0.0000028525153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975269,0.0007326163,0.0006379396,0.00031941224,0.00057706493,0.00020606432],"domain_scores_gemma":[0.9978876,0.0004939451,0.00057546905,0.00017522296,0.00059143925,0.00027633083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009214602,0.00017745857,0.0003045577,0.00016388763,0.00016860903,0.00016166255,0.0004297067,0.00012113339,0.00011477208],"category_scores_gemma":[0.0011958259,0.000167919,0.00016519001,0.0006425068,0.000017529615,0.0009722478,0.000062633175,0.00026933692,0.000004442398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012402603,0.000052637122,7.200428e-7,0.0000021760727,0.000034672383,0.000013044608,0.000056726858,0.9578744,0.027488844,0.0018660584,0.00010344254,0.0123832375],"study_design_scores_gemma":[0.0012942009,0.0007524323,0.000001153922,0.000009478527,0.000051674957,0.00002955955,0.000012125241,0.96133024,0.035786662,0.00048002895,0.000086454536,0.0001660189],"about_ca_topic_score_codex":0.000004354321,"about_ca_topic_score_gemma":3.1679028e-7,"teacher_disagreement_score":0.01639141,"about_ca_system_score_codex":0.00018735955,"about_ca_system_score_gemma":0.00040191942,"threshold_uncertainty_score":0.6847538},"labels":[],"label_agreement":null},{"id":"W3011495853","doi":"10.1080/03155986.2020.1730677","title":"Global optimization for mixed categorical-continuous variables based on Gaussian process models with a randomized categorical space exploration step","year":2020,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Categorical variable; Mathematical optimization; Maximization; Computer science; Kernel (algebra); Benchmark (surveying); Gaussian; Gaussian process; Optimization problem; Kriging; Continuous optimization; Mathematics; Algorithm; Artificial intelligence; Machine learning; Multi-swarm optimization","score_opus":0.04782811463025451,"score_gpt":0.32053925902598396,"score_spread":0.27271114439572947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011495853","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":[0.000036287016,0.000023399953,0.9899312,0.0031520817,0.00012564895,0.0034362574,0.00004645908,0.00013785958,0.0031107527],"genre_scores_gemma":[0.6910223,0.000023297145,0.30491677,0.0007320163,0.00014507306,0.002533894,0.00053227216,0.00001932225,0.00007507367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99682325,0.00027706826,0.00076108123,0.00038578283,0.0013625119,0.00039031616],"domain_scores_gemma":[0.9956224,0.0005945462,0.0002679132,0.0002495472,0.0029747053,0.00029091063],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001481363,0.00025291476,0.00044357718,0.00028403447,0.000589114,0.0014364101,0.00036949158,0.00014350408,0.000006051223],"category_scores_gemma":[0.00095443515,0.00019170855,0.00005578564,0.0012832851,0.000118825374,0.0069973115,0.0000751927,0.00019204157,0.000013224771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020045058,0.000024686804,0.000005804281,0.00007565128,0.000015927422,8.575352e-7,0.0007396392,0.68371224,4.603795e-7,0.3122665,0.00020978901,0.0009439407],"study_design_scores_gemma":[0.020336214,0.00033839152,0.0000049760206,0.000030565163,0.00000696343,0.000010388535,0.0007404094,0.97617304,0.000018697014,0.0010688823,0.0010347443,0.00023674185],"about_ca_topic_score_codex":0.00009005847,"about_ca_topic_score_gemma":0.0000043675736,"teacher_disagreement_score":0.690986,"about_ca_system_score_codex":0.00024666564,"about_ca_system_score_gemma":0.00067491137,"threshold_uncertainty_score":0.9996002},"labels":[],"label_agreement":null},{"id":"W3015993571","doi":"10.1007/s10898-022-01127-1","title":"Numerical certification of Pareto optimality for biobjective nonlinear problems","year":2022,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"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; Group for Research in Decision Analysis; HEC Montréal","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Parameterized complexity; Set (abstract data type); Pareto principle; Mathematical optimization; Class (philosophy); Mathematics; Polygon (computer graphics); Regular polygon; Computer science; Algorithm","score_opus":0.019277783038109963,"score_gpt":0.2869851079830246,"score_spread":0.26770732494491467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015993571","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":[0.00037706623,0.00011607129,0.99765587,0.0006068814,0.0005097133,0.00045499578,0.000061788,0.00003390756,0.00018369373],"genre_scores_gemma":[0.15760945,0.000020787496,0.8421575,0.00007917713,0.00005918761,0.000031048996,0.000018909006,0.000010986819,0.000012967843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998044,0.00017399158,0.0007814679,0.00026591492,0.0005430991,0.00019149353],"domain_scores_gemma":[0.99676925,0.00009204579,0.0013628157,0.00025972648,0.0014228789,0.00009328268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059498387,0.00014302638,0.00030662387,0.00011524765,0.00020188688,0.00004712951,0.0006075345,0.000048143007,0.000022390497],"category_scores_gemma":[0.0003156515,0.00014220162,0.00016261925,0.0011501563,0.000045079058,0.0007998661,0.0001335202,0.00014719633,5.898177e-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":[0.00010483109,0.00030396314,0.00034504308,0.000013044636,0.00003725907,0.0000017187446,0.00020101316,0.99428797,0.00006720517,0.0018617082,0.00013115566,0.0026451044],"study_design_scores_gemma":[0.0009888713,0.000627854,0.0005275108,0.000011761232,0.000021843081,0.00007878952,0.00012432244,0.99574816,0.00044207778,0.00087897194,0.00040939145,0.00014041559],"about_ca_topic_score_codex":0.0000092680175,"about_ca_topic_score_gemma":5.7602466e-7,"teacher_disagreement_score":0.15723237,"about_ca_system_score_codex":0.00062365155,"about_ca_system_score_gemma":0.00028466512,"threshold_uncertainty_score":0.57988137},"labels":[],"label_agreement":null},{"id":"W3021188214","doi":"10.46254/j.ieom.20190201","title":"Comparison Study of Discrete Optimization Problem Using Meta-Heuristic Approaches: A Case Study","year":2019,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering and Operations Management","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Mathematical optimization; Ant colony optimization algorithms; Metaheuristic; Simulated annealing; Computer science; Particle swarm optimization; Genetic algorithm; Meta-optimization; Algorithm; Tabu search; Heuristics; Mathematics","score_opus":0.10075415801037864,"score_gpt":0.32113747098084416,"score_spread":0.22038331297046554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021188214","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":[0.12585022,0.000029745619,0.8727719,0.00004471317,0.000510427,0.0007357723,0.000002100449,0.00001586099,0.000039270413],"genre_scores_gemma":[0.78218275,0.0000038003043,0.21770178,0.0000035783526,0.000058734742,0.000014199592,0.0000012945014,0.000008597303,0.0000252515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998506,0.00007914947,0.0006799012,0.00020189106,0.00044009413,0.00009296104],"domain_scores_gemma":[0.99909896,0.000051297455,0.00027337717,0.0001740813,0.00035049513,0.000051778225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004258741,0.0001427326,0.00031152306,0.0004442795,0.00005586942,0.00018514114,0.00035864208,0.000030975418,0.000009092068],"category_scores_gemma":[0.000045471745,0.00012261842,0.000056478202,0.00029466068,0.000009464465,0.0006238782,0.00019406587,0.0001745681,3.8462366e-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":[0.00001364271,0.0006207928,0.00058043154,0.0000049055216,0.0012860838,0.00015257536,0.0015479841,0.99367476,0.000008178185,0.00065629056,0.000002015148,0.0014523478],"study_design_scores_gemma":[0.0023157212,0.0004451795,0.000033799108,0.000027502989,0.00028223364,0.00023260711,0.004808933,0.99170357,0.00001912694,0.00000388825,0.000016535683,0.000110904606],"about_ca_topic_score_codex":0.00007665222,"about_ca_topic_score_gemma":0.0000059329373,"teacher_disagreement_score":0.65633255,"about_ca_system_score_codex":0.000095827694,"about_ca_system_score_gemma":0.000030445424,"threshold_uncertainty_score":0.5000234},"labels":[],"label_agreement":null},{"id":"W3021190556","doi":"10.1007/s00158-020-02572-w","title":"Multi-model management for time-dependent multidisciplinary design optimization problems","year":2020,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Multidisciplinary design optimization; Multidisciplinary approach; Computer science; Context (archaeology); Mathematical optimization; Engineering design process; Process (computing); Convergence (economics); Industrial engineering; Mathematics; Engineering; Mechanical engineering","score_opus":0.030645738484828777,"score_gpt":0.2753018813591183,"score_spread":0.24465614287428955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021190556","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":[0.00015364727,0.00015028898,0.9941533,0.0012822151,0.0002616001,0.0032130734,0.00006038144,0.00063442084,0.000091064634],"genre_scores_gemma":[0.0116134975,0.00019634597,0.986671,0.00014936527,0.000097287244,0.00034441578,0.00025157887,0.00008180683,0.0005947356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996802,0.00013479238,0.0006666142,0.0014076963,0.00040198263,0.0005869289],"domain_scores_gemma":[0.9983046,0.00011625789,0.00036528564,0.00045547713,0.00038107397,0.0003772911],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026402448,0.0005708943,0.00044122213,0.00020764512,0.0009917744,0.00025174595,0.0006858696,0.00018574303,0.000022511078],"category_scores_gemma":[0.00007413713,0.00053129543,0.00012184302,0.00059016416,0.00012441212,0.0018770663,0.0009145203,0.00019577605,0.000010386487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012788583,0.00006454079,0.000026970505,0.00014192665,0.000052690313,0.000007244817,0.0020190282,0.9930974,0.00063215377,0.00036787218,0.00003329668,0.0034289495],"study_design_scores_gemma":[0.0034656178,0.0002913105,0.000102235404,0.00004021124,0.000075934884,0.000020798221,0.00015378972,0.99426484,0.0003100996,0.0005733786,0.0000059702525,0.0006957879],"about_ca_topic_score_codex":0.0000016773567,"about_ca_topic_score_gemma":6.56487e-7,"teacher_disagreement_score":0.01145985,"about_ca_system_score_codex":0.00013057613,"about_ca_system_score_gemma":0.000066175875,"threshold_uncertainty_score":0.99971384},"labels":[],"label_agreement":null},{"id":"W3023765545","doi":"10.3390/econometrics4010012","title":"Evolutionary Sequential Monte Carlo Samplers for Change-Point Models","year":2016,"lang":"en","type":"article","venue":"Econometrics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Agence Nationale de la Recherche","keywords":"Markov chain Monte Carlo; Particle filter; Monte Carlo method; Computer science; Algorithm; Marginal likelihood; Mathematical optimization; Inference; Heuristic; Mathematics; Bayesian probability; Artificial intelligence; Statistics; Kalman filter","score_opus":0.12649954573386915,"score_gpt":0.2851502513235134,"score_spread":0.15865070558964425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023765545","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":[0.0002510411,0.0003048008,0.99636567,0.001013862,0.00076093205,0.00049647986,0.000120493874,0.00016882285,0.000517921],"genre_scores_gemma":[0.1586813,0.00018810073,0.8391451,0.0003407126,0.00029573828,0.00030205707,0.000005574012,0.000030617022,0.0010107931],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870986,0.000024685409,0.0002610229,0.000517867,0.00013460527,0.00035196464],"domain_scores_gemma":[0.9987897,0.0003051624,0.00015688996,0.0003697242,0.00024137211,0.00013714453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020773444,0.00015466502,0.00018177641,0.0006806356,0.00012920468,0.00005754051,0.00053062564,0.000068935566,0.000030828385],"category_scores_gemma":[0.00024380232,0.00013244293,0.000108303364,0.0009334041,0.00004900197,0.002131886,0.00021153943,0.00004955092,0.0000423944],"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":[0.00005710986,0.00034204504,0.0010756108,0.00004869732,0.00017836933,0.000013351656,0.001650843,0.26928812,0.00019583915,0.1874841,0.005392255,0.5342736],"study_design_scores_gemma":[0.0010141088,0.00009054712,0.0005520067,0.000009069187,0.000005082379,0.000007590164,0.000020050858,0.9746607,0.00017980202,0.015394018,0.007771796,0.00029524034],"about_ca_topic_score_codex":0.000017184124,"about_ca_topic_score_gemma":0.000004481537,"teacher_disagreement_score":0.7053726,"about_ca_system_score_codex":0.0004416855,"about_ca_system_score_gemma":0.000066563305,"threshold_uncertainty_score":0.54008657},"labels":[],"label_agreement":null},{"id":"W3024820404","doi":"","title":"Optimal Scale Response Format: A Generalizability Theory Perspective","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Generalizability theory; Perspective (graphical); Scale (ratio); Computer science; Data science; Mathematics; Artificial intelligence; Statistics; Geography; Cartography","score_opus":0.008989443071572683,"score_gpt":0.2704387601367444,"score_spread":0.2614493170651717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024820404","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":[0.013732965,0.000051996805,0.979329,0.0012984367,0.00016049242,0.00028180776,0.000003891161,0.00046641228,0.004675025],"genre_scores_gemma":[0.14703931,0.000005765187,0.85145193,0.00064145343,0.000028240405,0.00003132987,8.9471797e-7,0.0000127977455,0.00078830175],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843395,0.00024177757,0.00021185445,0.0005308402,0.0002537087,0.00032784478],"domain_scores_gemma":[0.99859273,0.00014994414,0.00007020294,0.00068110344,0.0003666918,0.00013933555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074176973,0.00017728627,0.00015847957,0.00012484154,0.00020586397,0.000101261256,0.00060635805,0.00006262755,0.00006543475],"category_scores_gemma":[0.00027356317,0.0001554776,0.00009081604,0.0005245643,0.00014735911,0.0012027143,0.00029526252,0.00013449896,0.00011432884],"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":[0.0003665138,0.00030575684,0.00003943921,0.0000032939759,0.000022987644,0.00002004562,0.009455159,0.46702614,0.0010960703,0.51883185,0.00006161258,0.0027711438],"study_design_scores_gemma":[0.0062841694,0.0007466348,0.005385314,0.000024007391,0.000016934016,0.00027198048,0.0057393806,0.63878554,0.056523725,0.28343844,0.0013636459,0.0014202383],"about_ca_topic_score_codex":0.000035199515,"about_ca_topic_score_gemma":0.000007691368,"teacher_disagreement_score":0.23539339,"about_ca_system_score_codex":0.00057052245,"about_ca_system_score_gemma":0.00019818728,"threshold_uncertainty_score":0.63401926},"labels":[],"label_agreement":null},{"id":"W3028365645","doi":"10.1016/j.swevo.2020.100713","title":"Surrogate-assisted grey wolf optimization for high-dimensional, computationally expensive black-box problems","year":2020,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Surrogate model; Robustness (evolution); Global optimization; Computation; Optimization problem; Local search (optimization); Mathematical optimization; Artificial intelligence; Radial basis function; Algorithm; Data mining; Machine learning; Mathematics; Artificial neural network","score_opus":0.023331193630724365,"score_gpt":0.251606041909398,"score_spread":0.22827484827867364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028365645","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":[0.0049102493,0.00020966295,0.9881639,0.0047234064,0.0003791896,0.0010849537,0.000045872497,0.000425365,0.00005740046],"genre_scores_gemma":[0.34087098,0.00002197052,0.6572386,0.0009834897,0.000120039884,0.00006794659,0.000633045,0.000027903101,0.000036014095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770474,0.00012960531,0.0005263506,0.0008565721,0.00045424156,0.0003284722],"domain_scores_gemma":[0.99750614,0.00036247977,0.0003372814,0.00016356511,0.0013917583,0.0002387616],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015320547,0.00030718604,0.00031025178,0.00018500182,0.0004752396,0.00014807987,0.0002605894,0.00011790438,0.0000135098735],"category_scores_gemma":[0.00018052408,0.00033530474,0.0000800431,0.0006980703,0.00014868213,0.0012789944,0.00018846746,0.00013750607,0.00002663743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053845757,0.000097408025,0.000063277985,0.000048844027,0.00003990008,0.0000048123566,0.0007471008,0.98676854,0.00011320512,0.005432278,0.0014200741,0.005210706],"study_design_scores_gemma":[0.0020765914,0.00028167828,0.0037758155,0.000032923293,0.00001882488,0.00002675733,0.00007874255,0.9849353,0.000096706026,0.008160059,0.0001391902,0.00037744935],"about_ca_topic_score_codex":0.0000148200215,"about_ca_topic_score_gemma":0.000002190713,"teacher_disagreement_score":0.33596072,"about_ca_system_score_codex":0.0001452576,"about_ca_system_score_gemma":0.00019773527,"threshold_uncertainty_score":0.9999099},"labels":[],"label_agreement":null},{"id":"W3028421519","doi":"10.1080/03155986.2019.1607810","title":"An algorithmic framework for the optimization of computationally expensive bi-fidelity black-box problems","year":2019,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Advanced Scientific Computing Research","keywords":"Fidelity; Computer science; Surrogate model; Black box; High fidelity; Computation; Sampling (signal processing); Mathematical optimization; Algorithm; Function (biology); Optimization problem; Machine learning; Artificial intelligence; Mathematics; Engineering","score_opus":0.04286155012797854,"score_gpt":0.3590349756424031,"score_spread":0.31617342551442457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028421519","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":[0.0008038753,0.0000741247,0.9949788,0.0004704806,0.00030461114,0.0028260706,0.000070757626,0.00004653329,0.00042476645],"genre_scores_gemma":[0.36912897,0.000079753445,0.6293243,0.00037462395,0.00013217509,0.00051458477,0.00028105392,0.000014484161,0.00015001769],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974441,0.00014563001,0.00078759855,0.00024132128,0.0011073366,0.00027403067],"domain_scores_gemma":[0.9926237,0.0013785361,0.00030251493,0.0004388772,0.005153623,0.000102749145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019720874,0.00015000043,0.0002094712,0.00033881253,0.0004615658,0.00077161955,0.0005698506,0.00012500865,0.000019033232],"category_scores_gemma":[0.0005434776,0.00011403488,0.0000458751,0.0006466414,0.00016160596,0.0050855316,0.00014498795,0.0002309358,0.000038114067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015437472,0.000016931688,0.00007346361,0.000063782325,0.000017139371,5.048129e-8,0.0019909088,0.75172895,0.000018731496,0.24321754,0.000071643,0.0027854],"study_design_scores_gemma":[0.0006434468,0.00018820315,0.0006974725,0.000059799568,0.0000020867374,0.000007704157,0.0015846936,0.99224305,0.000084310435,0.0012167398,0.0031349494,0.0001375672],"about_ca_topic_score_codex":0.00008018182,"about_ca_topic_score_gemma":0.0000022443007,"teacher_disagreement_score":0.3683251,"about_ca_system_score_codex":0.000107507054,"about_ca_system_score_gemma":0.00040462852,"threshold_uncertainty_score":0.7440745},"labels":[],"label_agreement":null},{"id":"W3029701887","doi":"10.1145/3387168.3387205","title":"Solving Dynamic Multi-Objective Optimization Problems Using Cultural Algorithm based on Decomposition","year":2019,"lang":"en","type":"article","venue":"Proceedings of the 3rd International Conference on Vision, Image and Signal Processing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Benchmark (surveying); Mathematical optimization; Decomposition; Multi-objective optimization; Optimization problem; Computer science; Population; Algorithm; Local optimum; Mathematics; Chemistry","score_opus":0.017576925004042804,"score_gpt":0.3094245261946589,"score_spread":0.2918476011906161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3029701887","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":[0.009215101,0.000024195371,0.98771197,0.00048737388,0.00020018703,0.00051614933,0.000009365921,0.00008961944,0.001746061],"genre_scores_gemma":[0.57350713,0.000013333425,0.4261816,0.00014882859,0.000023498898,0.000011931587,0.000006432632,0.000017089456,0.000090150665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980023,0.00002755003,0.00040955932,0.00066167105,0.0006498986,0.00024904433],"domain_scores_gemma":[0.99714375,0.00005760839,0.0005903917,0.00012518273,0.0020102477,0.00007284038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002792043,0.00029011373,0.0002455135,0.00028008848,0.00033659433,0.00065004046,0.00073941896,0.00008943791,0.000036200916],"category_scores_gemma":[0.000079428195,0.00022303876,0.00008543992,0.00042194346,0.00013211899,0.0025999746,0.00023837919,0.0002862219,0.0000044644103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019485912,0.0006919376,0.00075884,0.0002368388,0.00006797746,0.0000023523585,0.0021567196,0.67004436,0.21357435,0.0032667965,0.000022850038,0.1089821],"study_design_scores_gemma":[0.00093486946,0.00019998624,0.0005006115,0.0008964538,0.000011064835,0.000014721703,0.00027473219,0.98659337,0.009581575,0.0007208267,0.0000046170294,0.00026717654],"about_ca_topic_score_codex":0.000008333197,"about_ca_topic_score_gemma":3.7100006e-7,"teacher_disagreement_score":0.564292,"about_ca_system_score_codex":0.00020917525,"about_ca_system_score_gemma":0.00012407407,"threshold_uncertainty_score":0.90952563},"labels":[],"label_agreement":null},{"id":"W3030155800","doi":"10.1016/j.asoc.2020.106429","title":"Kriging-assisted Discrete Global Optimization (KDGO) for black-box problems with costly objective and constraints","year":2020,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Fundamental Research Funds for Central Universities of the Central South University; Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Kriging; Mathematical optimization; Computer science; Robustness (evolution); Black box; Underwater glider; Benchmark (surveying); Sampling (signal processing); Algorithm; Mathematics; Artificial intelligence; Machine learning","score_opus":0.014565376420465117,"score_gpt":0.2493201298439894,"score_spread":0.23475475342352428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030155800","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":[0.000600118,0.000024530344,0.99499935,0.00045337522,0.00007481761,0.0014087155,0.000020093885,0.0005480479,0.0018709789],"genre_scores_gemma":[0.44160295,0.0000020873356,0.5577892,0.00049311586,0.000044783126,0.000026463458,0.000017561368,0.000020263044,0.0000035777987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979429,0.00004271249,0.00035279372,0.0009338678,0.00027668822,0.00045102183],"domain_scores_gemma":[0.99866575,0.0002529639,0.00032502666,0.00024325268,0.0002834448,0.00022954428],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017063128,0.0003238028,0.00035026768,0.000049596776,0.00036784983,0.00026615086,0.00039242892,0.00008554587,0.000003391175],"category_scores_gemma":[0.000097961114,0.00030918172,0.000045843262,0.0007348428,0.00032404013,0.0003428417,0.00028499952,0.00016944416,0.0000045667966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049654496,0.000032403557,0.00022103368,0.000053879918,0.00006114505,0.0000041286876,0.0017421849,0.9452683,0.00012901185,0.012962594,0.000023335992,0.039452355],"study_design_scores_gemma":[0.0022307788,0.00015863279,0.00027526988,0.00004851608,0.000021641054,0.000023199338,0.00037489433,0.99556947,0.00023900549,0.0005924443,0.000063842905,0.0004023248],"about_ca_topic_score_codex":0.0000042592073,"about_ca_topic_score_gemma":0.0000023908215,"teacher_disagreement_score":0.44100282,"about_ca_system_score_codex":0.00011148361,"about_ca_system_score_gemma":0.0001270725,"threshold_uncertainty_score":0.99993604},"labels":[],"label_agreement":null},{"id":"W3034398297","doi":"10.2514/6.2020-3152","title":"An efficient application of Bayesian optimization to an industrial MDO framework for aircraft design.","year":2020,"lang":"en","type":"article","venue":"AIAA AVIATION 2020 FORUM","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Bayesian optimization; Solver; Set (abstract data type); Bayesian probability; Aviation; Global optimization","score_opus":0.0265716939448896,"score_gpt":0.29424118991350096,"score_spread":0.26766949596861134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034398297","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":[0.00010013489,0.000007632391,0.99245745,0.004989445,0.00025295487,0.0018764747,0.000028661152,0.00027422706,0.000013016369],"genre_scores_gemma":[0.22643548,0.0000016958303,0.7718907,0.0011637189,0.00017763629,0.00020319161,0.00009467015,0.000028663224,0.0000042621677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980255,0.00013694113,0.00047520432,0.00070382864,0.0003586466,0.0002998855],"domain_scores_gemma":[0.9982032,0.00016003093,0.0003410431,0.00053315837,0.00043977934,0.00032277213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027448309,0.00020086879,0.00023807697,0.00012712073,0.00018529271,0.00010184488,0.00066773023,0.00018418995,0.000014766568],"category_scores_gemma":[0.0004716427,0.00022545617,0.00006241716,0.0012456826,0.000025357187,0.00077434524,0.000095392315,0.00013975815,0.000014518254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007060003,0.00012632296,0.00014089423,0.000005892652,0.000008862529,2.845111e-7,0.0009644801,0.95708,0.00059188943,0.016852424,0.0001316519,0.02402671],"study_design_scores_gemma":[0.00070013985,0.00072537834,0.00009392222,0.000010456473,0.000011181551,7.756895e-7,0.00014682292,0.99076605,0.005179748,0.0018521192,0.00027367877,0.00023974881],"about_ca_topic_score_codex":0.00000725346,"about_ca_topic_score_gemma":0.0000017736724,"teacher_disagreement_score":0.22633535,"about_ca_system_score_codex":0.00010383956,"about_ca_system_score_gemma":0.00011582416,"threshold_uncertainty_score":0.9193836},"labels":[],"label_agreement":null},{"id":"W3045592461","doi":"10.2514/6.2019-3667","title":"Mode Pursuing Sampling Method Using Coordinate Perturbation for High-dimensional Expensive Black-box Optimization","year":2019,"lang":"en","type":"article","venue":"AIAA Aviation 2019 Forum","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Perturbation (astronomy); Mode (computer interface); Black box; Physics; Algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Quantum mechanics","score_opus":0.017598279990905234,"score_gpt":0.31173241179551214,"score_spread":0.2941341318046069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045592461","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":[0.0021646987,0.000040306873,0.9939579,0.0009598999,0.0012457647,0.0012970582,0.000031122865,0.00022957314,0.00007370165],"genre_scores_gemma":[0.07742962,0.0000066666025,0.9205756,0.0006407584,0.0000936029,0.000047981746,0.00019306298,0.000053365802,0.000959347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976562,0.00012123186,0.00049325573,0.00079439074,0.00044550427,0.00048938696],"domain_scores_gemma":[0.9973546,0.00041856428,0.0005417012,0.00051767594,0.0010660486,0.000101409125],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038391238,0.00029537117,0.0003263439,0.00032268444,0.00033134516,0.00019090608,0.00038806695,0.00016751692,0.00004029983],"category_scores_gemma":[0.00024621218,0.00031611283,0.00011332557,0.000514357,0.000030280415,0.0023333773,0.00020280213,0.00014691525,0.00005860501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026626216,0.00003640082,0.00013176633,0.000011142699,0.000025789492,3.6416688e-7,0.00032898446,0.96590126,0.003533362,0.026059715,0.00012844567,0.0038161401],"study_design_scores_gemma":[0.0013861454,0.00009511044,0.0001438435,0.000041191943,0.000019084173,0.000008130853,0.000121479476,0.9843705,0.005915093,0.007417949,0.00009300393,0.00038841696],"about_ca_topic_score_codex":0.00004668102,"about_ca_topic_score_gemma":0.0000023866423,"teacher_disagreement_score":0.07526492,"about_ca_system_score_codex":0.00037373242,"about_ca_system_score_gemma":0.00012718837,"threshold_uncertainty_score":0.9999291},"labels":[],"label_agreement":null},{"id":"W3045595564","doi":"10.1002/cjs.11559","title":"A sequential split‐and‐conquer approach for the analysis of big dependent data in computer experiments","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Divide and conquer algorithms; Computer science; Inference; Frequentist inference; Gaussian process; Data mining; Computation; Algorithm; Uncertainty quantification; Machine learning; Gaussian; Artificial intelligence; Bayesian probability; Bayesian inference","score_opus":0.09078864965144023,"score_gpt":0.29937832121815794,"score_spread":0.2085896715667177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045595564","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":[0.00017616927,0.00019274928,0.99853575,0.00021924028,0.00015608812,0.00013532599,0.00057278754,0.000001915294,0.000009989074],"genre_scores_gemma":[0.13773994,0.000017757293,0.8618588,0.00027440066,0.00006358115,0.000002009013,0.000032673677,0.0000062829126,0.000004591363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909073,0.000046864046,0.00035915078,0.0001855294,0.00016246874,0.0001552322],"domain_scores_gemma":[0.99883217,0.00016486338,0.000248339,0.00026544402,0.00023225612,0.00025695478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023863322,0.0000824852,0.00023166915,0.00023004175,0.0000582051,0.0000731939,0.0007545212,0.000025566753,0.0000070775295],"category_scores_gemma":[0.00014293498,0.00006715647,0.000030369947,0.0004557655,0.00007723535,0.00017985853,0.00009495779,0.00010829697,1.8460449e-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":[0.000041277108,0.000068773974,0.00412263,0.000053668435,0.0014741299,0.00017555826,0.008536211,0.78086764,0.000060741047,0.01712919,0.0011341951,0.18633601],"study_design_scores_gemma":[0.00046849475,0.00004793517,0.0019479386,0.000004467042,0.000110899266,0.000009656257,0.00012118732,0.99683404,0.0000288045,0.00011510014,0.00024199714,0.00006950987],"about_ca_topic_score_codex":0.0006560264,"about_ca_topic_score_gemma":0.0018781376,"teacher_disagreement_score":0.21596639,"about_ca_system_score_codex":0.000066850844,"about_ca_system_score_gemma":0.00044429873,"threshold_uncertainty_score":0.27385613},"labels":[],"label_agreement":null},{"id":"W3045671334","doi":"10.1007/s00158-020-02591-7","title":"A relative adequacy framework for multimodel management in multidisciplinary design optimization","year":2020,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Multidisciplinary approach; Multidisciplinary design optimization; Engineering design process; Computer science; Management science; Systems engineering; Engineering; Risk analysis (engineering); Mechanical engineering; Business","score_opus":0.03661798131880695,"score_gpt":0.305770368941454,"score_spread":0.26915238762264704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045671334","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":[0.00044425388,0.00019940962,0.9938261,0.0020984414,0.0003045082,0.0026706348,0.000028262497,0.00033353834,0.00009483069],"genre_scores_gemma":[0.057873897,0.00021544694,0.94113,0.00014141509,0.00009579762,0.0003033856,0.00012169574,0.00005677936,0.0000616071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970248,0.00017949412,0.0006705955,0.0012683804,0.0003150407,0.0005417091],"domain_scores_gemma":[0.9984068,0.000354009,0.00032963595,0.00038716287,0.00024346306,0.0002788797],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021543451,0.0004952083,0.00043213385,0.00028269363,0.0006073503,0.00015765974,0.00054544455,0.00022902533,0.000017151342],"category_scores_gemma":[0.00021093096,0.0004750484,0.00010306674,0.0010688774,0.00012312127,0.0024448687,0.00067882036,0.00031045795,0.0000034587288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025325472,0.000048313563,0.00012923813,0.0000804134,0.000038381655,0.000016610687,0.004685275,0.9717186,0.00002154795,0.016171409,0.00000648304,0.0068304352],"study_design_scores_gemma":[0.0028173053,0.0002767081,0.0008825687,0.000074424766,0.000042025196,0.000011048129,0.00055344874,0.9812854,0.0000787039,0.013385508,0.000004001753,0.0005888386],"about_ca_topic_score_codex":0.0000040893656,"about_ca_topic_score_gemma":0.0000011014494,"teacher_disagreement_score":0.057429645,"about_ca_system_score_codex":0.0001552697,"about_ca_system_score_gemma":0.000053722306,"threshold_uncertainty_score":0.9997701},"labels":[],"label_agreement":null},{"id":"W3045713108","doi":"10.1115/1.4047909","title":"Mode-Pursuing Sampling Method Using Discriminative Coordinate Perturbation for High-Dimensional Expensive Black-Box Optimization","year":2020,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Beijing Institute of Technology; National Natural Science Foundation of China","keywords":"Mathematical optimization; Computer science; Perturbation (astronomy); Robustness (evolution); Discriminative model; Optimization problem; Algorithm; Mathematics; Artificial intelligence","score_opus":0.10074389442939438,"score_gpt":0.3532763690213026,"score_spread":0.2525324745919082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045713108","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":[0.00009600023,0.000044227916,0.997038,0.0018191247,0.00043885238,0.0005037331,0.0000062451327,0.000048870854,0.000004910288],"genre_scores_gemma":[0.03758354,0.0000111762365,0.9614003,0.00071529154,0.00023027322,0.000008117322,0.0000033619083,0.00003441343,0.000013540034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975716,0.00042760264,0.0007275437,0.00043090543,0.0005530294,0.00028935765],"domain_scores_gemma":[0.99578285,0.0011831778,0.0009178964,0.00016554262,0.0016983898,0.0002521371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008492855,0.00024325396,0.0004721434,0.00017474988,0.00021022078,0.00013180274,0.0005055063,0.00012075672,0.000013771528],"category_scores_gemma":[0.00203841,0.00021661956,0.0001683199,0.00045576147,0.00003325329,0.0014056857,0.00015305259,0.00029319306,0.0000016269382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018563849,0.000054218337,1.2841909e-7,0.000010788044,0.000051730658,0.000012800506,0.0007539501,0.9646004,0.016205741,0.012456555,0.000047932182,0.0056201387],"study_design_scores_gemma":[0.0014170376,0.00055572,0.0000014860208,0.000069998496,0.000060348535,0.000051570092,0.00016578005,0.93666184,0.043307994,0.017471315,0.0000061420265,0.00023076429],"about_ca_topic_score_codex":0.0000020353657,"about_ca_topic_score_gemma":7.719673e-8,"teacher_disagreement_score":0.03748754,"about_ca_system_score_codex":0.0002471335,"about_ca_system_score_gemma":0.00018418496,"threshold_uncertainty_score":0.88334894},"labels":[],"label_agreement":null},{"id":"W3047794975","doi":"10.4018/978-1-7998-3970-5.ch014","title":"Extreme Value Metaheuristics and Coupled Mapped Lattice Approaches for Gas Turbine-Absorption Chiller Optimization","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in computer and electrical engineering book series","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Alberta Energy","funders":"","keywords":"Metaheuristic; Differential evolution; Mathematical optimization; Computer science; Extreme value theory; Stochastic optimization; Turbine; Chiller; Simulated annealing; Genetic algorithm; Engineering; Mathematics; Mechanical engineering; Physics","score_opus":0.01883506152196596,"score_gpt":0.21273728773455414,"score_spread":0.19390222621258818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047794975","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":[0.000001269082,0.010505883,0.9873073,0.00021547134,0.00028484568,0.0006400537,0.000009492901,0.00026272266,0.0007729388],"genre_scores_gemma":[0.00019588706,0.011815191,0.9852658,0.00021646761,0.00027922244,0.00006904433,0.000062767234,0.00008560843,0.002010027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981098,0.000017738448,0.0004620004,0.0008453406,0.00022588069,0.0003392068],"domain_scores_gemma":[0.9989918,0.00031901101,0.0001789196,0.00025450523,0.00010881664,0.00014693143],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001079818,0.00051106146,0.00060869916,0.00025400327,0.00010868602,0.00015481909,0.00027251465,0.00021136703,0.000002720757],"category_scores_gemma":[0.000074070536,0.000533463,0.00007425091,0.00016895386,0.000079730096,0.0013573804,0.0002067898,0.00038296342,0.0000010770846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003373763,0.000013523872,0.0000012996453,0.00013564015,0.000038820057,0.000011118359,0.00008766164,0.7888707,0.0000042888114,0.18382858,0.000009941589,0.026964724],"study_design_scores_gemma":[0.0006483721,0.00024588357,0.000009902009,0.00008812084,0.00003493346,0.000046080513,8.6905146e-7,0.9715065,0.00002006007,0.006754131,0.020092702,0.0005524525],"about_ca_topic_score_codex":7.0222836e-7,"about_ca_topic_score_gemma":7.1356965e-7,"teacher_disagreement_score":0.18263581,"about_ca_system_score_codex":0.00009304306,"about_ca_system_score_gemma":0.00003847462,"threshold_uncertainty_score":0.9997117},"labels":[],"label_agreement":null},{"id":"W3082029294","doi":"10.1016/j.ejor.2020.08.048","title":"Inferring linear feasible regions using inverse optimization","year":2020,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Mathematical optimization; Feasible region; Set (abstract data type); Constraint (computer-aided design); Constrained optimization; Optimization problem; Function (biology); Algorithm; Mathematics","score_opus":0.23612361330620157,"score_gpt":0.39858356278024537,"score_spread":0.1624599494740438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082029294","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":[0.0014404262,0.000051242645,0.9923411,0.003563334,0.00012502783,0.00012387741,0.0000015948561,0.000026497726,0.0023269146],"genre_scores_gemma":[0.10024989,0.00006501201,0.89844364,0.0005998658,0.000498825,7.3334405e-7,0.0000028895263,0.000025893389,0.000113227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99714094,0.0009724917,0.00045402246,0.00024167923,0.0009508073,0.00024003218],"domain_scores_gemma":[0.996946,0.00015546978,0.00017182845,0.00018974887,0.0022306787,0.00030627864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017845556,0.00010106044,0.00014114058,0.0003154989,0.00038834527,0.00026775085,0.0007874076,0.00001961937,0.00007272626],"category_scores_gemma":[0.0016063337,0.000098417026,0.00006430124,0.00093659695,0.00008999852,0.0016436541,0.0003521738,0.000541989,0.000080270846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019220028,0.000036138626,0.000059108075,0.0000042215934,0.000014409819,0.00015110854,0.00067238923,0.99371755,0.0018262285,0.0021551622,0.00039035807,0.000954084],"study_design_scores_gemma":[0.0006576843,0.00020146546,0.0002031085,0.00003443745,0.0000025059667,0.000106893516,0.00009922151,0.9960894,0.00060352386,0.000051829018,0.0018484459,0.00010148696],"about_ca_topic_score_codex":0.0000018337855,"about_ca_topic_score_gemma":3.3091754e-7,"teacher_disagreement_score":0.098809466,"about_ca_system_score_codex":0.00013267041,"about_ca_system_score_gemma":0.0004789397,"threshold_uncertainty_score":0.401333},"labels":[],"label_agreement":null},{"id":"W3082083549","doi":"10.1007/978-3-030-58112-1_13","title":"Simple Surrogate Model Assisted Optimization with Covariance Matrix Adaptation","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Dalhousie University","funders":"","keywords":"Computer science; Simple (philosophy); CMA-ES; Adaptation (eye); Covariance matrix; Algorithm; Mathematical optimization; Matrix (chemical analysis); Covariance; Artificial intelligence; Covariance function; Mathematics; Statistics","score_opus":0.023973858364599566,"score_gpt":0.2611975937309552,"score_spread":0.23722373536635563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082083549","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":[0.0000011221799,0.00010350598,0.9968519,0.0006072871,0.0004215269,0.00069541077,0.000018541306,0.00040374513,0.0008969559],"genre_scores_gemma":[0.012332571,0.00003558219,0.9861922,0.00090398587,0.00014397092,0.000021062006,0.00004630519,0.00006964037,0.00025466998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956136,0.000049902326,0.0005877501,0.0019946853,0.0011801497,0.0005739356],"domain_scores_gemma":[0.9969685,0.00027772994,0.0005966045,0.0011091563,0.0008013731,0.00024661032],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037604754,0.00065652997,0.0005924939,0.0006099119,0.00034054634,0.00057135615,0.0020912606,0.00028239438,0.000015213589],"category_scores_gemma":[0.00013759237,0.00061698083,0.00009084471,0.0013614437,0.00046794154,0.001591525,0.0007065423,0.00070577435,0.000024604522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017576614,0.00002019599,0.0000028441746,0.000018507642,0.000011193361,0.000051524687,0.00039042864,0.9248987,0.000025628651,0.008726569,0.0000036331346,0.06583324],"study_design_scores_gemma":[0.00063319673,0.00015797802,0.0000096334015,0.00013735087,0.000012324843,0.00004894982,3.7856717e-7,0.97686404,0.00020919593,0.021146744,0.00007736745,0.0007028295],"about_ca_topic_score_codex":0.000011289732,"about_ca_topic_score_gemma":0.00004295294,"teacher_disagreement_score":0.06513041,"about_ca_system_score_codex":0.0005216667,"about_ca_system_score_gemma":0.001021718,"threshold_uncertainty_score":0.9996281},"labels":[],"label_agreement":null},{"id":"W3092716745","doi":"10.1016/j.envsoft.2020.104902","title":"Cluster-based multi-objective optimization for identifying diverse design options: Application to water resources problems","year":2020,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Computer science; Flexibility (engineering); Multi-objective optimization; Benchmark (surveying); Data mining; Mathematical optimization; Cluster (spacecraft); Set (abstract data type); Pareto principle; Process (computing); Machine learning; Mathematics; Statistics; Geography","score_opus":0.050291367491627874,"score_gpt":0.2579614541183884,"score_spread":0.2076700866267605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092716745","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":[0.00044030533,0.00006413867,0.99599636,0.00033124493,0.00009489708,0.0025847603,0.000040707237,0.00044632694,0.0000012507263],"genre_scores_gemma":[0.0939713,0.000016688806,0.9042598,0.00071935914,0.000058746456,0.00075879117,0.00012400099,0.000058921538,0.000032369247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977689,0.00012814472,0.00037022255,0.0009918576,0.00034065128,0.00040024886],"domain_scores_gemma":[0.99901414,0.00016472694,0.00014674875,0.000388384,0.000053928885,0.00023210107],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025530736,0.00030535113,0.0002222622,0.0001295366,0.0004991009,0.0001605183,0.00048426952,0.00010269006,0.00001632245],"category_scores_gemma":[0.000053617536,0.0002990515,0.00010490274,0.00023704793,0.00005724118,0.00082944526,0.0002809138,0.00014410053,0.00009427411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004600352,0.000120748206,0.000057738216,0.00002482695,0.00001603312,0.0000012115065,0.0044811815,0.99071306,0.0012128626,0.000010697909,0.000008296672,0.0033073248],"study_design_scores_gemma":[0.0011284923,0.00014575555,0.000016147955,0.000027651828,0.000019832882,0.0000014809891,0.00016157405,0.99086505,0.0068894164,0.00014041342,0.0002164509,0.00038771826],"about_ca_topic_score_codex":0.000008915856,"about_ca_topic_score_gemma":8.357723e-7,"teacher_disagreement_score":0.09353099,"about_ca_system_score_codex":0.00029431935,"about_ca_system_score_gemma":0.000015669893,"threshold_uncertainty_score":0.9999462},"labels":[],"label_agreement":null},{"id":"W3094400170","doi":"10.1002/cjce.23899","title":"Multiobjective optimization of area‐to‐point heat conduction structure using binary quantum‐behaved <scp>PSO</scp> and Tchebycheff decomposition method","year":2020,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Mathematical optimization; Particle swarm optimization; Multi-objective optimization; Quantum; Binary number; Pareto principle; Mathematics; Computer science; Physics","score_opus":0.01783087719387239,"score_gpt":0.2564719722182549,"score_spread":0.23864109502438252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094400170","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":[0.15236335,0.00009191596,0.8467583,0.00046283004,0.00015253095,0.00014235427,0.000007050996,0.000018030098,0.0000036068807],"genre_scores_gemma":[0.5637014,0.0000014833373,0.43613082,0.00009256128,0.00005744333,9.563156e-7,0.0000018711461,0.000013057324,3.8380855e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990078,0.000052768333,0.00034586116,0.00019315384,0.00018992968,0.00021048474],"domain_scores_gemma":[0.9987199,0.00016951685,0.0001673814,0.00013179531,0.00035478966,0.0004566543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001904929,0.00016141348,0.00025889368,0.00020014444,0.000087894776,0.00006891208,0.00030054833,0.00008520128,0.0000038030694],"category_scores_gemma":[0.0005396064,0.00014305777,0.00005938565,0.0005224958,0.000045133635,0.00045095113,0.000051076997,0.00032380805,1.6566328e-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":[0.0000035611513,0.0000031671004,0.000009734286,0.0000102692975,0.000019855523,0.0000063405137,0.0014592451,0.71112674,0.28707364,0.00008985783,0.0000065358536,0.00019103414],"study_design_scores_gemma":[0.0002858575,0.000051812276,0.000050756425,0.00004853613,0.000019062534,0.00016287639,0.00007109818,0.7714268,0.22775373,0.00006224818,0.000007727196,0.000059508904],"about_ca_topic_score_codex":0.0001658081,"about_ca_topic_score_gemma":0.000009618446,"teacher_disagreement_score":0.41133806,"about_ca_system_score_codex":0.00029142137,"about_ca_system_score_gemma":0.00019552118,"threshold_uncertainty_score":0.58337265},"labels":[],"label_agreement":null},{"id":"W3096998548","doi":"10.2514/1.j059826","title":"Evaluating the Risk of Local Optima in Aerodynamic Shape Optimization","year":2020,"lang":"en","type":"article","venue":"AIAA Journal","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Bombardier Transportation; Government of Ontario","keywords":"Local optimum; Aerodynamics; Shape optimization; Set (abstract data type); Optimal design; Mathematical optimization; Geometric design; Computer science; Optimization problem; Mathematics; Engineering; Finite element method; Geometry; Structural engineering; Aerospace engineering; Machine learning","score_opus":0.03472903478689438,"score_gpt":0.3188054304586147,"score_spread":0.2840763956717203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096998548","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":[0.011022283,0.0001245863,0.98691094,0.001497936,0.00015923913,0.00014217697,0.0000016591117,0.000029029954,0.0001121622],"genre_scores_gemma":[0.40080625,0.000092678536,0.598763,0.00026057003,0.000057911187,0.0000032983392,8.112455e-7,0.000009684322,0.000005776344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838966,0.00033251534,0.0004359653,0.00023365155,0.0004026463,0.00020557045],"domain_scores_gemma":[0.99880445,0.00019660286,0.00042979646,0.00020491784,0.00026850554,0.00009572692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000766621,0.000119056356,0.00016770799,0.000086975226,0.00016754866,0.00008629994,0.00069498917,0.000042673502,0.00010330836],"category_scores_gemma":[0.00047995147,0.000088646266,0.000069608664,0.0007296287,0.000074575735,0.0006353098,0.00017649017,0.0004515196,0.000008624468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013215686,0.000020785174,0.00034876648,0.0000021444846,0.00001170097,0.0000063501802,0.00072080974,0.9132355,0.00010598414,0.00020955963,0.000008224539,0.08531698],"study_design_scores_gemma":[0.000727868,0.00019746079,0.0020781239,0.000019187073,0.000008964416,0.00004944014,0.00018395812,0.99617416,0.00011480686,0.000343604,0.0000064796104,0.000095922296],"about_ca_topic_score_codex":0.0000065893464,"about_ca_topic_score_gemma":0.0000027325723,"teacher_disagreement_score":0.38978398,"about_ca_system_score_codex":0.000090843525,"about_ca_system_score_gemma":0.00013207972,"threshold_uncertainty_score":0.36148897},"labels":[],"label_agreement":null},{"id":"W3098133464","doi":"","title":"Efficient Emulators of Computer Experiments Using Compactly Supported Correlation Functions, With An Application to Cosmology","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":97,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Correlation; Computer science; Correlation function (quantum field theory); Gaussian; Gaussian process; Function (biology); Computer experiment; Product (mathematics); Algorithm; Dimension (graph theory); Simulation; Mathematics; Physics","score_opus":0.03160110112162959,"score_gpt":0.2862074674335189,"score_spread":0.25460636631188927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098133464","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":[0.16374072,0.0000023391867,0.8352632,0.000007359063,0.00016471236,0.00044208075,0.0000018864253,0.00012932473,0.00024837113],"genre_scores_gemma":[0.5088035,5.5545584e-8,0.49107817,0.00006547773,0.000008708706,0.0000108293525,0.000007760971,0.000007833602,0.000017678338],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998843,0.00006241399,0.00028043447,0.00041958241,0.00020595844,0.0001885693],"domain_scores_gemma":[0.9988467,0.000023687686,0.00019044471,0.000474795,0.00032672926,0.00013760764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010013634,0.00014352848,0.00018049331,0.00022094324,0.0000993715,0.000017409646,0.00026250517,0.000051272298,0.000030896485],"category_scores_gemma":[0.0000050881445,0.00012743715,0.00002299729,0.0005822671,0.00005396073,0.0002585994,0.000090590605,0.000058183217,0.00002120871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005580327,0.00040441996,0.004583616,0.0000025431295,0.00001978868,0.00000207676,0.0017608196,0.9830009,0.001706971,0.0035794666,0.000010830408,0.0048728096],"study_design_scores_gemma":[0.00048080014,0.00038475965,0.012724154,0.0000059378513,0.0000070262845,0.000021734635,0.00011232102,0.9808718,0.0051672524,0.000051949617,0.000015692016,0.00015654767],"about_ca_topic_score_codex":0.00011511426,"about_ca_topic_score_gemma":0.000006237318,"teacher_disagreement_score":0.34506276,"about_ca_system_score_codex":0.00009473818,"about_ca_system_score_gemma":0.0000494684,"threshold_uncertainty_score":0.5196736},"labels":[],"label_agreement":null},{"id":"W3099057434","doi":"10.6084/m9.figshare.6287681.v2","title":"Local Gaussian Process Model for Large-Scale Dynamic Computer Experiments","year":2019,"lang":"en","type":"article","venue":"Figshare","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Science and Engineering Research Board; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer experiment; Computer science; Gaussian process; Singular value decomposition; Set (abstract data type); Algorithm; Process (computing); Gaussian; Euclidean distance; Simulation; Artificial intelligence","score_opus":0.020481621745374476,"score_gpt":0.3017479135736517,"score_spread":0.2812662918282772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099057434","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":[0.000032070417,0.000031800442,0.98462945,0.000045982953,0.00012304932,0.0007092467,0.013861295,0.00021749242,0.0003496241],"genre_scores_gemma":[0.17284627,6.538843e-7,0.8068467,0.00084946916,0.000059519745,0.00083094824,0.016554046,0.00006460667,0.0019478375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867755,0.0000140089105,0.0001712958,0.00055316044,0.00021587171,0.00036808534],"domain_scores_gemma":[0.9990968,0.0000391188,0.000099308774,0.00043648828,0.00022345694,0.00010480631],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000029590528,0.00017991885,0.0001701085,0.00006942303,0.000099056655,0.00009272923,0.0006729822,0.00008079591,0.0026317325],"category_scores_gemma":[0.00002311769,0.00017682412,0.00006942485,0.00020293896,0.0000044643366,0.0007055834,0.00025182558,0.00009601298,0.00083045673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012416807,0.00018885432,0.00001029883,0.0001582039,0.000017445389,0.0000036416682,0.0021027043,0.9781784,0.000035990754,0.00023863536,0.008810193,0.010243213],"study_design_scores_gemma":[0.0008119048,0.000048555743,0.00005019317,0.00016595394,0.0000010976681,0.0000037519346,0.00004208376,0.996489,0.00058017217,0.00019470419,0.0013720272,0.0002405648],"about_ca_topic_score_codex":2.0813926e-7,"about_ca_topic_score_gemma":0.0000019470042,"teacher_disagreement_score":0.17778279,"about_ca_system_score_codex":0.0000957291,"about_ca_system_score_gemma":0.00008459689,"threshold_uncertainty_score":0.9999475},"labels":[],"label_agreement":null},{"id":"W3100953776","doi":"10.1115/detc2001/dac-21141","title":"Improvement on the Adaptive Response Surface Method for High-Dimensional Computation-Intensive Design Problems","year":2001,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 Manitoba","funders":"","keywords":"Latin hypercube sampling; Computation; Computer science; Hypercube; Central composite design; Speedup; Set (abstract data type); Mathematical optimization; Response surface methodology; Parallel computing; Algorithm; Mathematics; Statistics","score_opus":0.048656188231832084,"score_gpt":0.3065736388663195,"score_spread":0.2579174506344874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3100953776","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":[0.0005367911,0.000011037777,0.98960483,0.0073129004,0.0002734625,0.0019763135,0.0000075006583,0.00018472616,0.00009242485],"genre_scores_gemma":[0.03167049,0.0000025100617,0.9625051,0.0040093153,0.000024540874,0.00015719375,0.0000041533413,0.000021689608,0.0016049915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792576,0.00046866646,0.00029205033,0.00060973444,0.00037212064,0.00033167042],"domain_scores_gemma":[0.9935017,0.004383139,0.00018516964,0.00038287888,0.0014606565,0.00008645428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013134939,0.00022804562,0.0001977576,0.00008319884,0.00033755164,0.00008840986,0.00043144161,0.000053663407,0.000024789098],"category_scores_gemma":[0.00051396264,0.00015348764,0.000068291054,0.00045271788,0.0000567788,0.00029896633,0.00015052236,0.00013331408,0.000040932253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005308098,0.000078293655,8.389503e-7,0.0000011278348,0.000038794034,0.0000048956845,0.00035432202,0.9587965,0.0039152987,0.02454637,0.0013333829,0.010399342],"study_design_scores_gemma":[0.0009969379,0.0009895451,0.00007756616,0.000014013197,0.0000060580023,0.000012368726,0.00016393041,0.9666062,0.017018255,0.0137360105,0.00016682933,0.00021228501],"about_ca_topic_score_codex":0.00003611256,"about_ca_topic_score_gemma":0.0000037258224,"teacher_disagreement_score":0.031133698,"about_ca_system_score_codex":0.00018456072,"about_ca_system_score_gemma":0.00014245186,"threshold_uncertainty_score":0.62590444},"labels":[],"label_agreement":null},{"id":"W3101200922","doi":"10.5267/j.ijiec.2020.10.001","title":"A chaotic-based improved many-objective Jaya algorithm for many-objective optimization problems","year":2020,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Benchmark (surveying); Algorithm; Sorting; Mathematical optimization; Convergence (economics); Chaotic; Computer science; Local optimum; Tournament selection; Premature convergence; Evolutionary algorithm; Selection (genetic algorithm); Mathematics; Artificial intelligence; Particle swarm optimization","score_opus":0.02531355818343585,"score_gpt":0.2650028868976819,"score_spread":0.23968932871424609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101200922","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":[0.00009212041,0.000038770926,0.99313354,0.0032350083,0.002477165,0.000740668,0.00007600667,0.00017758156,0.000029145927],"genre_scores_gemma":[0.04970952,0.000008895489,0.9482287,0.0004432202,0.0014316596,0.0000610525,0.00004787789,0.000053493863,0.000015552998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976936,0.00006720777,0.0008976916,0.00041886524,0.00061626575,0.0003063702],"domain_scores_gemma":[0.9956793,0.0005265531,0.00079226797,0.00016023515,0.0025815386,0.00026005914],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031120202,0.0003136289,0.00039590755,0.0005798322,0.00012419357,0.0003015703,0.0010607225,0.00016319127,0.000013050875],"category_scores_gemma":[0.0010420227,0.00033254456,0.00026322177,0.00076409423,0.000041883868,0.0011125067,0.00013695522,0.0005166368,0.0000040666137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046356312,0.00011312908,0.000009013909,0.0000064626765,0.0002495629,0.000017770737,0.00059627916,0.96839553,0.00021393369,0.0011789008,0.000115183444,0.02905788],"study_design_scores_gemma":[0.0045106346,0.000459416,0.00003638401,0.0001080668,0.00003642832,0.00005440245,0.00007033403,0.99256563,0.0010680347,0.00017844519,0.00060142094,0.00031077195],"about_ca_topic_score_codex":0.000009911049,"about_ca_topic_score_gemma":4.0465017e-7,"teacher_disagreement_score":0.049617402,"about_ca_system_score_codex":0.00045796213,"about_ca_system_score_gemma":0.00048685062,"threshold_uncertainty_score":0.9999127},"labels":[],"label_agreement":null},{"id":"W3103210112","doi":"","title":"Improving NSGA-II with an Adaptive Mutation Operator","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Operator (biology); Adaptive mutation; Mutation; Evolutionary algorithm; Mathematical optimization; Computer science; Multi-objective optimization; Evolutionary computation; Process (computing); Pareto principle; Mathematics; Genetic algorithm; Biology","score_opus":0.015759731976079192,"score_gpt":0.2606782161603119,"score_spread":0.2449184841842327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3103210112","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":[0.0005889152,0.000023899282,0.9946418,0.00022170306,0.00045312935,0.0006686745,0.000017336797,0.0006058229,0.0027787464],"genre_scores_gemma":[0.112742126,0.000004647277,0.88546157,0.00021030498,0.00014548888,0.00013598513,0.000018087838,0.00004079153,0.0012410063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976827,0.0001180618,0.00028334223,0.001207311,0.00038269832,0.0003259172],"domain_scores_gemma":[0.9976625,0.000060743787,0.0003036042,0.0010396375,0.00075209443,0.00018146246],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019270986,0.00037651372,0.000289468,0.00018971878,0.00025091387,0.00025859463,0.0010184061,0.00018391976,0.000039211776],"category_scores_gemma":[0.000058366968,0.00026083528,0.000050656865,0.00022536515,0.00007503575,0.0015688401,0.0011167482,0.0003279689,0.000031778487],"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":[0.00020123829,0.00050703547,0.00007463467,0.00006936279,0.00022632224,0.00018931374,0.005000679,0.39268968,0.0013106611,0.076159514,0.00009935601,0.5234722],"study_design_scores_gemma":[0.00071419484,0.00058253546,0.00011235811,0.00007507363,0.000010600718,0.000025263635,0.00009626438,0.98935866,0.0049580885,0.0034186817,0.000052608342,0.00059569627],"about_ca_topic_score_codex":0.00006722,"about_ca_topic_score_gemma":0.00004291098,"teacher_disagreement_score":0.59666896,"about_ca_system_score_codex":0.00025547808,"about_ca_system_score_gemma":0.00044767608,"threshold_uncertainty_score":0.9999844},"labels":[],"label_agreement":null},{"id":"W3107847621","doi":"10.1007/s43069-021-00075-y","title":"Combining Cross Entropy and MADS methods for inequality constrained global optimization","year":2020,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Inequality; Mathematics; Computer science; Econometrics; Mathematical optimization; Economics; Mathematical analysis","score_opus":0.027354151793291905,"score_gpt":0.3428868302780182,"score_spread":0.31553267848472627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107847621","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":[0.00023724612,0.00074321666,0.9899344,0.0037134301,0.0005871391,0.002572866,0.00036425827,0.0017110908,0.00013637521],"genre_scores_gemma":[0.027227065,0.00019850474,0.9689995,0.0018786784,0.0001389164,0.0012049264,0.00021949387,0.00007882931,0.000054102158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954159,0.0006525183,0.0010320365,0.001674097,0.00038281994,0.0008425793],"domain_scores_gemma":[0.9959639,0.00053883385,0.0009344582,0.0013351524,0.0006721554,0.00055555475],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0015313998,0.0007606729,0.000961489,0.0002572966,0.00041626202,0.0011300837,0.001487713,0.00074488955,0.000008804578],"category_scores_gemma":[0.0017755575,0.00086628704,0.0002869364,0.0007304132,0.00028037737,0.0007167866,0.0026938627,0.0008084849,0.0000015583126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011702613,0.0001629248,0.0028373108,0.00020785439,0.00014576294,0.000021155403,0.00044001557,0.8018777,0.00042459878,0.12696749,0.00009386724,0.06670433],"study_design_scores_gemma":[0.0013617744,0.00013915573,0.0016791628,0.00007352701,0.00004490157,0.0000574255,0.00003240936,0.9545847,0.0019631134,0.039095063,0.00020269827,0.00076606136],"about_ca_topic_score_codex":0.00067146594,"about_ca_topic_score_gemma":0.000049599377,"teacher_disagreement_score":0.15270706,"about_ca_system_score_codex":0.00094943354,"about_ca_system_score_gemma":0.0007003852,"threshold_uncertainty_score":0.99990684},"labels":[],"label_agreement":null},{"id":"W3111000592","doi":"10.1007/978-3-030-58930-1_12","title":"Hybridization of the Differential Evolution Algorithm for Continuous Multi-objective Optimization","year":2020,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Mathematical optimization; Differential evolution; Metaheuristic; Computer science; Convergence (economics); Algorithm; Evolutionary algorithm; Multi-objective optimization; Meta-optimization; Mathematics","score_opus":0.054487132783635574,"score_gpt":0.32386920424057025,"score_spread":0.26938207145693466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111000592","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":[5.390478e-7,0.0008065982,0.9946275,0.0002002499,0.0015937707,0.0015930637,0.00014678149,0.000091297756,0.0009402361],"genre_scores_gemma":[0.0073390994,0.00028031258,0.98779213,0.00010832218,0.00018058896,0.00014012969,0.000097485885,0.00006117777,0.0040007555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972822,0.00008344106,0.0009447598,0.000866234,0.0005802935,0.00024306242],"domain_scores_gemma":[0.9957838,0.0007893736,0.0009423921,0.00035975786,0.0020716323,0.00005304968],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017756797,0.00044807864,0.00064935413,0.00026797777,0.0002377631,0.000041060182,0.0008694188,0.00017457706,0.00001008483],"category_scores_gemma":[0.00053610373,0.00040346957,0.00024081694,0.00036849212,0.0005218233,0.0003284064,0.0006200938,0.00033648693,0.0000065751133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011812704,0.00005014971,0.000004679549,0.00005083377,0.00013212686,0.0000022283994,0.00063398824,0.8454452,0.000001635268,0.12537754,0.000045078472,0.028244734],"study_design_scores_gemma":[0.00037322406,0.00009024046,0.000040972045,0.00027635458,0.00003462025,0.0000075152366,0.00012438229,0.9196388,0.00014992912,0.078835264,0.000084598985,0.00034409686],"about_ca_topic_score_codex":0.0000054313496,"about_ca_topic_score_gemma":0.000008575987,"teacher_disagreement_score":0.07419361,"about_ca_system_score_codex":0.0006030543,"about_ca_system_score_gemma":0.0002624601,"threshold_uncertainty_score":0.9998417},"labels":[],"label_agreement":null},{"id":"W3111176240","doi":"10.1109/smc42975.2020.9283294","title":"Age-Layered Strategies for Many-Objective Optimization","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Mathematical optimization; Sorting; Multi-objective optimization; Pareto principle; Reduction (mathematics); Ranking (information retrieval); Inefficiency; Computer science; Evolutionary algorithm; Optimization problem; Convergence (economics); Mathematics; Artificial intelligence; Algorithm; Economics","score_opus":0.03053826607247536,"score_gpt":0.2782520772688626,"score_spread":0.24771381119638722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111176240","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":[0.000013028509,0.000017327004,0.9896592,0.0014911699,0.00017158472,0.00061208574,0.0000064119035,0.000573528,0.0074556856],"genre_scores_gemma":[0.034463134,0.000009514964,0.96347857,0.001472026,0.0000931736,0.000087774686,0.000014702486,0.00002094892,0.00036019072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987904,0.000037293423,0.00021663638,0.0005306859,0.00017586393,0.0002491315],"domain_scores_gemma":[0.9991243,0.000102560654,0.00010142758,0.00025104135,0.0002884693,0.0001321856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006813317,0.0001797732,0.00018479236,0.00006647825,0.00014416128,0.00027045154,0.00049499644,0.000060626706,0.000047040663],"category_scores_gemma":[0.00012160361,0.00016605815,0.00007267851,0.00051168504,0.000039199622,0.0014476795,0.00014234483,0.00008221156,0.000024671892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014761702,0.000025832853,0.000003153663,0.000009207232,0.000016710104,0.000005467145,0.0010599132,0.9102401,0.00024269,0.08484063,0.00039450583,0.0031469814],"study_design_scores_gemma":[0.0007375643,0.00014234526,0.000032263266,0.000003301938,0.0000041797343,0.0000026904543,0.000509606,0.9929745,0.0013696376,0.003370057,0.000635472,0.00021837375],"about_ca_topic_score_codex":0.0000053017534,"about_ca_topic_score_gemma":0.0000023445718,"teacher_disagreement_score":0.08273437,"about_ca_system_score_codex":0.00005284368,"about_ca_system_score_gemma":0.00010528588,"threshold_uncertainty_score":0.67716545},"labels":[],"label_agreement":null},{"id":"W3120457970","doi":"10.1109/ssci47803.2020.9308352","title":"Deterministic Numeric Simulation and Surrogate Models with White and Black Machine Learning Methods: A Case Study on Inverse Mappings","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"National Research Council Canada","funders":"","keywords":"Black box; Computer science; Surrogate model; Inverse problem; Emulation; Artificial intelligence; Machine learning; Range (aeronautics); Partial differential equation; Mathematical optimization; Algorithm; Mathematics","score_opus":0.06153928276110581,"score_gpt":0.33126714973596255,"score_spread":0.26972786697485673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120457970","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":[0.05721984,0.000009086446,0.9418189,0.00015962147,0.00001519025,0.00043217672,9.4406823e-7,0.00017987545,0.00016439288],"genre_scores_gemma":[0.56035733,0.0000024347569,0.4393275,0.00023211232,0.0000058728046,0.0000061859023,6.144323e-7,0.000010907624,0.000057049907],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868727,0.0002458887,0.00018047333,0.0005716993,0.0001523681,0.00016232021],"domain_scores_gemma":[0.9991696,0.00027226165,0.00011629374,0.00017361595,0.00009956337,0.00016865204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019763928,0.00019058674,0.00020829384,0.000093160364,0.00016400081,0.00013658585,0.00010507859,0.000027578233,0.0000046260548],"category_scores_gemma":[0.00011589938,0.000157898,0.0000134060865,0.0004424377,0.000063167456,0.000639114,0.0001853398,0.0001701319,0.0000035369792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026702764,0.00004112463,0.0015968599,0.000010410995,0.000017281722,0.00040227154,0.012899683,0.9732672,0.000015823556,0.000086594664,5.0051153e-7,0.011635543],"study_design_scores_gemma":[0.0011958367,0.0007011753,0.0001936384,0.000007775182,0.000015652493,0.00014146738,0.0023441226,0.9950729,0.000012919768,0.00009480907,0.000018238368,0.00020146994],"about_ca_topic_score_codex":0.000036665264,"about_ca_topic_score_gemma":0.000017514818,"teacher_disagreement_score":0.50313747,"about_ca_system_score_codex":0.000021963804,"about_ca_system_score_gemma":0.000018146757,"threshold_uncertainty_score":0.64388937},"labels":[],"label_agreement":null},{"id":"W3122249354","doi":"","title":"Evolutionary Sequential Monte Carlo Samplers for Change-point Models","year":2015,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Markov chain Monte Carlo; Particle filter; Monte Carlo method; Computer science; Algorithm; Marginal likelihood; Mathematical optimization; Inference; Hybrid Monte Carlo; Heuristic; Bayesian probability; Mathematics; Artificial intelligence; Statistics; Kalman filter","score_opus":0.13317681680389143,"score_gpt":0.3639498034040559,"score_spread":0.2307729866001645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122249354","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":[0.0029916973,0.0010677561,0.9611576,0.0022103959,0.0036577315,0.008496776,0.0009174643,0.0005412312,0.018959317],"genre_scores_gemma":[0.08746338,0.005152249,0.8986011,0.00030132727,0.0011258852,0.004891679,0.00019320774,0.00020149378,0.0020696572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995744,0.00030709855,0.00069689244,0.0017088846,0.000465962,0.0010771534],"domain_scores_gemma":[0.9965539,0.00043679695,0.0002978277,0.0014138322,0.0009199948,0.0003776744],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001823114,0.00043585527,0.00061111455,0.0007860365,0.00023247971,0.00024583598,0.001969744,0.00046852892,0.0000101502865],"category_scores_gemma":[0.0004913401,0.00051431486,0.00024984038,0.0002557743,0.00028366398,0.0010102128,0.0033216903,0.0012102674,0.0000071278296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005830235,0.00009722268,0.000031913234,0.00006215925,0.000060205388,0.000014195669,0.0010602194,0.9071484,0.000008258755,0.002256179,0.00017587203,0.08902705],"study_design_scores_gemma":[0.0008918881,0.00008912618,0.000083886865,0.00008273237,0.0000046368204,0.000012063541,0.00020779067,0.96730006,0.000021696464,0.027002545,0.0038180698,0.0004855315],"about_ca_topic_score_codex":0.00023598873,"about_ca_topic_score_gemma":0.00018247034,"teacher_disagreement_score":0.088541515,"about_ca_system_score_codex":0.0035987543,"about_ca_system_score_gemma":0.0014350634,"threshold_uncertainty_score":0.9997308},"labels":[],"label_agreement":null},{"id":"W3133833020","doi":"10.1007/s11004-021-09927-z","title":"Correction to: Uncertainty Assessment over any Volume without Simulation: Revisiting Multi-Gaussian Kriging","year":2021,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Queen's University","funders":"","keywords":"Kriging; Geostatistics; Hydrogeology; Uncertainty quantification; Gaussian; Variogram; Statistics; Econometrics; Gaussian process; Environmental science; Volume (thermodynamics); Mathematics; Geology; Statistical physics; Soil science; Computer science; Geotechnical engineering; Physics; Spatial variability; Thermodynamics","score_opus":0.024428264746360666,"score_gpt":0.3459286096820115,"score_spread":0.32150034493565083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133833020","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":[0.0017217128,0.000019155446,0.9915184,0.00071738183,0.001150799,0.00027657778,0.0000022420766,0.00024861662,0.0043451586],"genre_scores_gemma":[0.28821352,0.0000023531263,0.7077133,0.0003953181,0.00009011226,0.000026042142,0.000002104184,0.000010560922,0.0035466512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742246,0.00014694499,0.0004503207,0.0007961729,0.00072028214,0.00046381803],"domain_scores_gemma":[0.9983252,0.0004008872,0.00017863118,0.0005106671,0.00033739177,0.00024719772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070046395,0.00021641156,0.00030072132,0.00013066789,0.0004937986,0.0005822869,0.00046383115,0.000049022932,0.0002578906],"category_scores_gemma":[0.0013781674,0.00018922955,0.0000832391,0.0014623418,0.000111585476,0.00097526173,0.00036690745,0.00019667532,0.000102026286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028859843,0.0003174017,0.0028522746,0.000052202326,0.000015389072,0.000047042693,0.001473647,0.9274135,0.00054253,0.01937744,0.00012895958,0.047776725],"study_design_scores_gemma":[0.00022454567,0.00004200565,0.0052086054,0.00015350076,0.000006237623,0.00003102491,0.0003173503,0.9909163,0.00014844954,0.001733092,0.0009557977,0.00026309432],"about_ca_topic_score_codex":0.000016376658,"about_ca_topic_score_gemma":0.000010571913,"teacher_disagreement_score":0.2864918,"about_ca_system_score_codex":0.00016457177,"about_ca_system_score_gemma":0.00021434472,"threshold_uncertainty_score":0.77165574},"labels":[],"label_agreement":null},{"id":"W3139526846","doi":"10.1080/00949655.2021.1900182","title":"Approximately optimal subset selection for statistical design and modelling","year":2021,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; Compute Canada","keywords":"Mathematics; Mathematical optimization; Selection (genetic algorithm); Cross-entropy method; Entropy (arrow of time); Optimization problem; Set (abstract data type); Optimal design; Principle of maximum entropy; Algorithm; Applied mathematics; Statistics; Computer science; Artificial intelligence; Quadratic assignment problem","score_opus":0.04670705401099513,"score_gpt":0.32965979382469884,"score_spread":0.2829527398137037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139526846","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":[0.0012003707,0.00006481707,0.9982799,0.000112598194,0.00010716394,0.00018939895,0.000014945813,0.000023912242,0.000006882773],"genre_scores_gemma":[0.29141653,0.000017464065,0.70844895,0.000052328458,0.000033304772,0.000002222224,0.000015898024,0.000007607442,0.0000057268326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876314,0.00016039246,0.0004526985,0.0002335505,0.00024572507,0.00014448151],"domain_scores_gemma":[0.99674267,0.0018978986,0.0002360699,0.000044667308,0.0009463384,0.00013237115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039888322,0.000114788905,0.00021873268,0.000112402675,0.00016053577,0.00019999879,0.00005299802,0.000055585653,0.000005463928],"category_scores_gemma":[0.0003945804,0.00011266201,0.000022636012,0.00018472884,0.00004524959,0.00056721247,0.00002953057,0.0001254718,5.422345e-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":[0.00007571016,0.000045246652,0.000024911333,0.000022426766,0.00001607514,0.0000083148325,0.00019098194,0.93927974,0.000054427823,0.028698988,0.000023208384,0.031559993],"study_design_scores_gemma":[0.0010929976,0.00024385464,0.00039724744,0.000014877501,0.00002288807,0.000104241524,0.000036895253,0.9673978,0.00011292968,0.03040934,0.00005063126,0.00011629112],"about_ca_topic_score_codex":8.508268e-7,"about_ca_topic_score_gemma":2.4696664e-7,"teacher_disagreement_score":0.29021615,"about_ca_system_score_codex":0.000049502418,"about_ca_system_score_gemma":0.000100014426,"threshold_uncertainty_score":0.45942235},"labels":[],"label_agreement":null},{"id":"W3141233211","doi":"10.1007/s10589-021-00272-9","title":"DMulti-MADS: mesh adaptive direct multisearch for bound-constrained blackbox multiobjective optimization","year":2021,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":20,"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; Group for Research in Decision Analysis","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Multi-objective optimization; Iterated local search; Extension (predicate logic); Context (archaeology); Set (abstract data type); Pareto principle; Mathematics; Computer science; Optimization problem; Metaheuristic","score_opus":0.018983299392605023,"score_gpt":0.29188553682389085,"score_spread":0.27290223743128583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3141233211","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":[0.000012748744,0.0002236435,0.9947384,0.0007569235,0.00013524728,0.0019070811,0.00019004333,0.00041042417,0.0016254594],"genre_scores_gemma":[0.02046117,0.00015064463,0.97581166,0.0005021246,0.00012631198,0.0013957545,0.00097148964,0.000051917425,0.0005289522],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972971,0.00016289225,0.0005871465,0.0011419009,0.00041357995,0.0003973758],"domain_scores_gemma":[0.99549097,0.0009029333,0.00031696376,0.00044700556,0.002586412,0.00025571897],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002480511,0.0003540139,0.00036184295,0.0002916678,0.00089640176,0.0003945708,0.00037094022,0.00015422037,0.000063855405],"category_scores_gemma":[0.0002929117,0.00041057405,0.00013204887,0.0013179879,0.00024491356,0.0010165336,0.00021023693,0.0001790778,0.000014511749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001750584,0.00024971567,0.00002495046,0.000017268203,0.00006611714,0.000002098261,0.00030434894,0.9384755,0.000035180536,0.05168473,0.000083179475,0.009039408],"study_design_scores_gemma":[0.0021111018,0.000060296537,0.00016658434,0.000022931652,0.00003109094,0.000032054046,0.00023376243,0.99268335,0.0002859869,0.0031828724,0.0007433397,0.00044664534],"about_ca_topic_score_codex":0.0000050379904,"about_ca_topic_score_gemma":0.000004034611,"teacher_disagreement_score":0.054207835,"about_ca_system_score_codex":0.00020278251,"about_ca_system_score_gemma":0.0004844683,"threshold_uncertainty_score":0.9998346},"labels":[],"label_agreement":null},{"id":"W3142815160","doi":"10.1115/1.4050749","title":"Multi-Objective Optimization for High-Dimensional Expensively Constrained Black-Box Problems","year":2021,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Constraint (computer-aided design); Mathematical optimization; Decomposition; Multi-objective optimization; Cluster analysis; Metamodeling; Mathematics; Artificial intelligence; Machine learning","score_opus":0.0373627401359308,"score_gpt":0.2772010246687062,"score_spread":0.23983828453277542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3142815160","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":[0.00007484742,0.000105358784,0.99750084,0.0008862166,0.0008001997,0.00055371353,0.000009179109,0.000056289988,0.000013373585],"genre_scores_gemma":[0.026042704,0.000039878814,0.9730752,0.0005114509,0.00013846558,0.00002195851,0.0000058058436,0.000030734776,0.00013376243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99743927,0.0003778437,0.0008146248,0.00046754975,0.0005548585,0.00034584993],"domain_scores_gemma":[0.9944807,0.0008570583,0.00076052116,0.00029123964,0.0033665441,0.00024393377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008492682,0.00025094763,0.0004965574,0.00018261197,0.00016079227,0.00012326112,0.00048799496,0.00016332266,0.000060122955],"category_scores_gemma":[0.001787858,0.00022696945,0.00020755627,0.00051165145,0.000076433935,0.0008576775,0.000144896,0.000303802,0.0000075095622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009774118,0.00035138804,4.6769108e-7,0.000011647001,0.00010401157,0.00011567252,0.00019264543,0.96280426,0.024585266,0.0076169865,0.00027013556,0.0038497518],"study_design_scores_gemma":[0.00375148,0.0005558289,0.000012117187,0.00008219456,0.000033231634,0.0003565966,0.00007914154,0.9203255,0.06826745,0.0062500765,0.000034991084,0.00025138783],"about_ca_topic_score_codex":0.0000012452739,"about_ca_topic_score_gemma":8.4864513e-7,"teacher_disagreement_score":0.043682184,"about_ca_system_score_codex":0.00019768412,"about_ca_system_score_gemma":0.000658143,"threshold_uncertainty_score":0.9255545},"labels":[],"label_agreement":null},{"id":"W3151456762","doi":"10.4018/978-1-5225-2498-4.ch013","title":"Design and Analysis of Computer Experiments","year":2017,"lang":"en","type":"book-chapter","venue":"Advances in computational intelligence and robotics book series","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Computer experiment; Process (computing); Emphasis (telecommunications); Engineering design process; Design of experiments; Computer Applications; Management science; Systems engineering; Industrial engineering; Simulation; Engineering; Mathematics; Programming language; Mechanical engineering","score_opus":0.03888834029460076,"score_gpt":0.31795843060800055,"score_spread":0.2790700903133998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151456762","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":[3.6169152e-7,0.010018095,0.9852537,0.000087594,0.00021965998,0.00026736446,0.000011284013,0.000032167558,0.0041097743],"genre_scores_gemma":[0.0004469668,0.016779965,0.97389996,0.000113454975,0.000028860477,0.000009928948,0.00003502642,0.000022268126,0.008663594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982643,0.000033449458,0.0005543733,0.00064176583,0.0003213771,0.00018470967],"domain_scores_gemma":[0.9982435,0.00044523078,0.0005447732,0.00037386845,0.0003146855,0.00007795495],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014673104,0.00034138508,0.0006381126,0.0005515746,0.0001773708,0.00011546017,0.00048078748,0.00014041764,0.000014217194],"category_scores_gemma":[0.000020895042,0.00034652292,0.000077312376,0.00009987488,0.0005799529,0.0019262596,0.00038543576,0.00019170794,0.00000280428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011020122,0.000014376011,0.000022657587,0.00001992348,0.00013601924,0.0000119825245,0.00030184907,0.708608,4.5102e-7,0.26667264,0.0000023967527,0.024198717],"study_design_scores_gemma":[0.00009098366,0.00013453494,0.00007287977,0.00013924601,0.00007729996,0.000014258854,0.000011965766,0.88163096,0.000064369204,0.11592583,0.0014948391,0.00034282982],"about_ca_topic_score_codex":0.0000021247263,"about_ca_topic_score_gemma":0.000006150761,"teacher_disagreement_score":0.17302302,"about_ca_system_score_codex":0.00004927748,"about_ca_system_score_gemma":0.00007494636,"threshold_uncertainty_score":0.9998987},"labels":[],"label_agreement":null},{"id":"W3153988432","doi":"","title":"Inverse Bayesian Optimization: Learning Human Search Strategies in a Sequential Optimization Task.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bayesian optimization; Hyperparameter optimization; Probabilistic logic; Computer science; Bayesian probability; Task (project management); Function (biology); Machine learning; Optimization problem; Range (aeronautics); Artificial intelligence; Mathematical optimization; Algorithm; Mathematics; Engineering","score_opus":0.05935918226286159,"score_gpt":0.23310943778464457,"score_spread":0.17375025552178297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153988432","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":[0.0054658456,0.000029890802,0.9911238,0.00005577129,0.00037318168,0.0004913957,0.000005773567,0.00039667942,0.0020576944],"genre_scores_gemma":[0.65812993,0.00020065004,0.34051955,0.00003837511,0.00006702619,0.000004112025,0.00022871295,0.000045347086,0.00076625956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996398,0.00066489016,0.00041323132,0.0017476073,0.00023159197,0.00054470956],"domain_scores_gemma":[0.9977773,0.00007777172,0.00034969667,0.000973091,0.0006122838,0.00020987967],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034833926,0.00047852664,0.0004780105,0.0008008359,0.00038931245,0.0006908613,0.0013560129,0.00044145767,0.00019833208],"category_scores_gemma":[0.000070605885,0.00065957685,0.00018200443,0.0019858717,0.00018280267,0.0021408526,0.0021572784,0.0012759275,0.000010743195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012528426,0.00011639439,0.00049489713,0.000053577765,0.000049008777,0.00060799246,0.0011432677,0.9868201,0.000029162906,0.0105631305,0.0000059442627,0.000103983846],"study_design_scores_gemma":[0.0009814976,0.000048136146,0.00007313794,0.00012970189,0.000026298521,0.00000935277,0.0017237085,0.995521,0.000063810185,0.0007986934,0.000010870845,0.00061376207],"about_ca_topic_score_codex":0.0002870717,"about_ca_topic_score_gemma":0.00020885855,"teacher_disagreement_score":0.6526641,"about_ca_system_score_codex":0.0007668812,"about_ca_system_score_gemma":0.00081546936,"threshold_uncertainty_score":0.99958557},"labels":[],"label_agreement":null},{"id":"W3160538881","doi":"10.1016/j.compchemeng.2021.107371","title":"An adaptive sampling surrogate model building framework for the optimization of reaction systems","year":2021,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"McMaster University","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Adaptive sampling; Computer science; Sampling (signal processing); Convergence (economics); Mathematical optimization; Engineering optimization; Surrogate model; Scale (ratio); Optimization problem; Linear programming; Algorithm; Machine learning; Mathematics; Monte Carlo method","score_opus":0.026938695011727475,"score_gpt":0.2758539798247078,"score_spread":0.2489152848129803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160538881","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":[0.0005598001,0.00017611025,0.9981192,0.000051310126,0.00064798596,0.00022240957,0.000004744922,0.0002150986,0.0000033756867],"genre_scores_gemma":[0.1946471,0.00001280434,0.8051528,0.000021754502,0.0001023541,0.00003059683,0.000008762811,0.000022178963,0.0000016755024],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989055,0.000016750973,0.00027752144,0.00039078243,0.00017985799,0.00022957963],"domain_scores_gemma":[0.998385,0.00063504995,0.00012814946,0.00043181085,0.000340759,0.000079218626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014881635,0.00016140913,0.00020875208,0.00006239238,0.000078241144,0.00009283947,0.0004323926,0.0000951391,3.009235e-7],"category_scores_gemma":[0.00018914611,0.00015594478,0.00007593156,0.00041338676,0.000020914571,0.00047750873,0.00013185074,0.00018370134,1.9688727e-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":[0.0000053093404,0.000022405564,5.2688875e-7,0.000023432467,0.000024457871,7.5530505e-7,0.00014428739,0.93014616,0.03667065,0.03098828,0.0000015040106,0.0019722334],"study_design_scores_gemma":[0.00020420886,0.000016937196,0.0000022918957,0.00011170056,0.000010899254,0.000010953085,0.000025755087,0.9695463,0.02945181,0.00044724921,0.000011306359,0.0001605731],"about_ca_topic_score_codex":0.0000023339262,"about_ca_topic_score_gemma":2.8960992e-8,"teacher_disagreement_score":0.1940873,"about_ca_system_score_codex":0.00012211151,"about_ca_system_score_gemma":0.000045528017,"threshold_uncertainty_score":0.63592434},"labels":[],"label_agreement":null},{"id":"W3162664130","doi":"10.1007/s10898-021-01019-w","title":"Integrating $$\\varepsilon $$-dominance and RBF surrogate optimization for solving computationally expensive many-objective optimization problems","year":2021,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 Guelph","funders":"National Research Foundation Singapore","keywords":"Mathematical optimization; Surrogate model; Optimization problem; Mathematics; Function (biology); Multi-objective optimization; Evolutionary algorithm; Computer science","score_opus":0.011906402326560034,"score_gpt":0.2696545108687438,"score_spread":0.2577481085421838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162664130","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":[0.00040625755,0.00068128353,0.9961787,0.00079241965,0.0008536095,0.000694045,0.000028873314,0.000092885704,0.00027194445],"genre_scores_gemma":[0.030510372,0.00044431927,0.96839327,0.0002945165,0.00015580512,0.000031713815,0.00008656644,0.00003998321,0.00004344206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99687374,0.00021093158,0.0012092931,0.000679165,0.00061732164,0.00040954092],"domain_scores_gemma":[0.9911081,0.00037711154,0.0018140331,0.00030026285,0.0061993585,0.00020111006],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006120827,0.00039728492,0.00059359113,0.000211551,0.00043790607,0.000511449,0.00042567574,0.00018754818,0.000023730066],"category_scores_gemma":[0.0015913706,0.00041236347,0.00018252854,0.0012901303,0.00009408558,0.0032166494,0.00021614786,0.00023983004,0.0000011548733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007527634,0.00013474023,0.00024642103,0.000042632008,0.00008225209,0.00002701714,0.00060900813,0.9894968,0.000062195875,0.005604344,0.00006436047,0.003554913],"study_design_scores_gemma":[0.0026388478,0.0002849164,0.00021965454,0.00027231287,0.000059092832,0.00048848655,0.00045392555,0.99379903,0.0004110954,0.0009502833,0.00003051228,0.0003918647],"about_ca_topic_score_codex":0.0000056190484,"about_ca_topic_score_gemma":0.0000076506185,"teacher_disagreement_score":0.030104114,"about_ca_system_score_codex":0.0006736686,"about_ca_system_score_gemma":0.00057317154,"threshold_uncertainty_score":0.9998328},"labels":[],"label_agreement":null},{"id":"W3163384192","doi":"10.18280/isi.260205","title":"Evolutionary Algorithms for Real Time Engineering Problems: A Comprehensive Review","year":2021,"lang":"en","type":"review","venue":"Ingénierie des systèmes d information","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Metaheuristic; Engineering optimization; Mathematical optimization; Computer science; Optimization problem; Variety (cybernetics); Process (computing); Evolutionary algorithm; Continuous optimization; Scheme (mathematics); Test functions for optimization; Multi-swarm optimization; Algorithm; Artificial intelligence; Mathematics","score_opus":0.03430728709100111,"score_gpt":0.29568628473752917,"score_spread":0.26137899764652806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163384192","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":[9.134968e-9,0.52160317,0.47561705,0.00000962421,0.00029620816,0.0018467369,0.000093014656,0.0003135507,0.00022060462],"genre_scores_gemma":[1.6633585e-8,0.6308804,0.3664756,0.00007218596,0.00008681131,0.0011237927,0.001241268,0.00003566277,0.000084231826],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99667156,0.00015667181,0.0016636138,0.0005129205,0.00045161296,0.0005436192],"domain_scores_gemma":[0.99588424,0.00042353384,0.0012201451,0.0007998448,0.0015144728,0.00015773866],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037730113,0.0006711258,0.0018535168,0.00049110583,0.0002796228,0.00031543075,0.0008874041,0.00032082063,0.000021319265],"category_scores_gemma":[0.00053785864,0.0006565628,0.0005839959,0.0014881123,0.000072052055,0.004183901,0.00038609956,0.00032154808,0.00018634256],"study_design_candidate":"design_other","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":[6.5863804e-7,0.000019591633,5.4896276e-8,0.0691861,0.00013433416,0.0000039098327,0.00023527998,0.0027811578,1.9499242e-7,0.0012787562,0.0012922444,0.9250677],"study_design_scores_gemma":[0.00020813922,0.000052267133,0.0000010295262,0.052070018,0.00014786172,0.00025404766,0.000012038187,0.136395,5.6039767e-7,0.00009872807,0.81015307,0.00060726504],"about_ca_topic_score_codex":0.0000067360947,"about_ca_topic_score_gemma":1.2244323e-7,"teacher_disagreement_score":0.9244605,"about_ca_system_score_codex":0.0011753562,"about_ca_system_score_gemma":0.00069570116,"threshold_uncertainty_score":0.99958855},"labels":[],"label_agreement":null},{"id":"W3180164923","doi":"10.1007/s10589-022-00381-z","title":"Quantifying uncertainty with ensembles of surrogates for blackbox optimization","year":2022,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Institut de Valorisation des Données","keywords":"Mathematical optimization; Computer science; Stochastic optimization; Context (archaeology); Optimization problem; Metaheuristic; Mathematics","score_opus":0.022842800052946446,"score_gpt":0.28093445588126226,"score_spread":0.25809165582831584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180164923","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":[0.000091616974,0.00008618096,0.9978354,0.00051452924,0.000040656792,0.0010374824,0.00008652782,0.00014165322,0.00016598076],"genre_scores_gemma":[0.112764746,0.000025607167,0.88558835,0.00014555614,0.00002162754,0.00089369074,0.0004900365,0.000021525608,0.00004888744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986576,0.00006842273,0.00035514732,0.00045147655,0.00030274622,0.000164618],"domain_scores_gemma":[0.9982921,0.00040298508,0.0003431841,0.00023413282,0.00065418874,0.0000734125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001962849,0.00015863735,0.0001922913,0.0002251148,0.0007092764,0.00007312562,0.00030490314,0.000030484925,0.000033372235],"category_scores_gemma":[0.000032454453,0.00016574125,0.00004424689,0.0009091331,0.00010092555,0.00036883567,0.00014926947,0.00008263627,6.151138e-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":[0.000017435772,0.00010525504,0.00010037487,0.000017838001,0.000020482505,1.5369676e-7,0.00018191841,0.9042312,0.000011094843,0.09348658,0.000023814588,0.0018038559],"study_design_scores_gemma":[0.00092161074,0.00010511491,0.00006670846,0.000006795649,0.000014950005,0.000017130262,0.00022299215,0.99596715,0.00004867593,0.0017666945,0.0006701176,0.00019204446],"about_ca_topic_score_codex":0.000004061205,"about_ca_topic_score_gemma":0.0000017676771,"teacher_disagreement_score":0.11267313,"about_ca_system_score_codex":0.00007212662,"about_ca_system_score_gemma":0.00014271254,"threshold_uncertainty_score":0.6758732},"labels":[],"label_agreement":null},{"id":"W3185522196","doi":"10.1017/9781108571401.027","title":"Optimal Design for Least Squares Estimators","year":2020,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimator; Computer science; Content (measure theory); Least-squares function approximation; Statistics; Mathematics","score_opus":0.03620858582150023,"score_gpt":0.2258413488710233,"score_spread":0.1896327630495231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185522196","genre_codex":"methods","genre_gemma":"other","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":[1.8118641e-7,0.000043299646,0.664525,0.000036782476,0.00022851556,0.00079216936,0.00013716651,0.00036270305,0.33387423],"genre_scores_gemma":[0.00003257776,0.000014428298,0.44129223,0.00009292821,0.00008352466,0.000004074034,0.000027491826,0.00005568977,0.55839705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99799776,0.000047366524,0.00023807338,0.0010351212,0.00031775562,0.00036389675],"domain_scores_gemma":[0.99796325,0.0002230409,0.0003324353,0.0006949446,0.0004016028,0.00038474007],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000088815614,0.00051623915,0.00049929036,0.00021670874,0.00031705605,0.00013293461,0.0014653358,0.00030460634,0.0000022913373],"category_scores_gemma":[0.00006353506,0.00064347446,0.00026828863,0.00002468305,0.00020649475,0.00037689577,0.0007350355,0.00037646297,0.00002008464],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009893264,0.000011733018,1.1172089e-7,0.000057431018,0.0001185286,0.00020197776,0.00008660804,0.02759601,0.00001592745,0.9570465,0.01161457,0.003151646],"study_design_scores_gemma":[0.001232533,0.00021320359,0.0000011121365,0.000098977616,0.00011064497,0.000026114134,0.000018925297,0.44352618,0.0007351137,0.000054703705,0.5531003,0.0008822307],"about_ca_topic_score_codex":0.000007967338,"about_ca_topic_score_gemma":1.4692681e-7,"teacher_disagreement_score":0.9569918,"about_ca_system_score_codex":0.00028054166,"about_ca_system_score_gemma":0.00031075202,"threshold_uncertainty_score":0.99960166},"labels":[],"label_agreement":null},{"id":"W3193433638","doi":"10.1007/978-3-030-64018-7_4","title":"Heuristics and Metaheuristics for Fixed-Charge Network Design","year":2020,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer 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":"Polytechnique Montréal; Université du Québec à Montréal","funders":"","keywords":"Metaheuristic; Heuristics; Fixed charge; Computer science; Mathematical optimization; Heuristic; Constructive; Field (mathematics); Population; Mathematics; Sociology","score_opus":0.04156584865863594,"score_gpt":0.2514892381633121,"score_spread":0.20992338950467615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193433638","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":[1.4039881e-9,0.00064265844,0.9379136,0.00032795206,0.0006159622,0.0009419208,0.000057041783,0.00032006815,0.05918077],"genre_scores_gemma":[0.000004383888,0.00038169854,0.81359154,0.00088579056,0.00041675608,0.000031429892,0.000031602904,0.00007880212,0.18457802],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979974,0.000030902025,0.0004487933,0.0008965773,0.00028038336,0.00034595383],"domain_scores_gemma":[0.99771285,0.00078745745,0.0003301725,0.0005404542,0.00038387414,0.00024516883],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022669595,0.00048500946,0.00057971734,0.000083933264,0.00020617728,0.0001716363,0.0005988643,0.0002614177,0.00006339031],"category_scores_gemma":[0.00024880207,0.0004770449,0.00012206053,0.000076948076,0.00009252857,0.00019498989,0.00041923492,0.0003097716,0.0000801678],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014070263,0.000009264893,3.8347585e-7,0.000038661205,0.0000934564,0.000026173515,0.0000366703,0.013766839,0.0000015916585,0.9551368,0.019366296,0.011509787],"study_design_scores_gemma":[0.00036923238,0.00015161971,5.763159e-7,0.000030850613,0.0000679165,0.000014787889,8.944772e-7,0.6677139,0.000028117995,0.13106759,0.20002829,0.0005262615],"about_ca_topic_score_codex":5.655761e-7,"about_ca_topic_score_gemma":7.173202e-7,"teacher_disagreement_score":0.8240692,"about_ca_system_score_codex":0.000059461898,"about_ca_system_score_gemma":0.00011861486,"threshold_uncertainty_score":0.99976814},"labels":[],"label_agreement":null},{"id":"W3197491224","doi":"10.1109/tnnls.2021.3105937","title":"Multi-Objective Neural Evolutionary Algorithm for Combinatorial Optimization Problems","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Knapsack problem; Artificial neural network; Evolutionary algorithm; Combinatorial optimization; Travelling salesman problem; Scalability; Mathematical optimization; Artificial intelligence; Heuristics; Optimization problem; Inference; Algorithm; Mathematics","score_opus":0.014620265587794617,"score_gpt":0.24346156437418906,"score_spread":0.22884129878639445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197491224","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":[0.000095607866,0.0007583652,0.9923946,0.00010172777,0.0053739604,0.000855884,0.000014923151,0.0003871035,0.000017831577],"genre_scores_gemma":[0.54038054,0.00022750422,0.45684323,0.00012449353,0.0004988008,0.0005213249,0.000046506673,0.00008639349,0.0012712054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99752545,0.0004922311,0.00043188437,0.0008178383,0.00028008182,0.0004525087],"domain_scores_gemma":[0.9982856,0.00045955586,0.0002229256,0.0002962753,0.0005672774,0.00016837759],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002481697,0.00032420078,0.00036222537,0.00012619537,0.0010545964,0.0003169266,0.00022503655,0.00019257436,0.0000056319614],"category_scores_gemma":[0.000030777144,0.00033449728,0.0001437862,0.00067198114,0.00007481824,0.0007503292,0.000009826422,0.00068345625,0.0000018121627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016394873,0.00014479895,0.00000993372,0.000019586745,0.00004621428,0.000010453693,0.0001812927,0.9622833,0.000029132949,0.000079221376,0.000018654262,0.037160974],"study_design_scores_gemma":[0.0019505277,0.00031046986,0.00004217038,0.000055644316,0.000025922576,0.00014117802,0.00016232734,0.9966399,0.00004622307,0.0000117082955,0.00025691747,0.0003570276],"about_ca_topic_score_codex":0.000029924839,"about_ca_topic_score_gemma":0.0000031082031,"teacher_disagreement_score":0.54028493,"about_ca_system_score_codex":0.00013131837,"about_ca_system_score_gemma":0.000058068596,"threshold_uncertainty_score":0.9999107},"labels":[],"label_agreement":null},{"id":"W3197550566","doi":"10.1007/978-3-030-63591-6_25","title":"Using Shooting Approaches to Generate Initial Guesses for ODE Parameter Estimation","year":2021,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Ode; Parameterized complexity; Ordinary differential equation; Set (abstract data type); Shooting method; Computer science; Mathematical optimization; Estimation; Estimation theory; Mathematics; Differential (mechanical device); Applied mathematics; Algorithm; Differential equation; Engineering; Boundary value problem; Mathematical analysis","score_opus":0.2098970060607345,"score_gpt":0.3354747858039829,"score_spread":0.12557777974324838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197550566","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":[0.00003257796,0.00007003621,0.99205303,0.000058537975,0.0003766153,0.0013105887,0.00012599208,0.0001430282,0.005829616],"genre_scores_gemma":[0.0001680758,0.00002731291,0.99321085,0.000108097156,0.00019843015,0.00014001987,0.000050803454,0.00016579569,0.0059305998],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99691856,0.00000853728,0.0010261718,0.0009684471,0.00055331556,0.0005249611],"domain_scores_gemma":[0.99747163,0.00049040467,0.000761791,0.00036806904,0.00075564336,0.00015247274],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005085031,0.000610261,0.0007681001,0.00047083077,0.00018751065,0.00059734524,0.00066853454,0.00028146838,0.000013634781],"category_scores_gemma":[0.001335016,0.00069226517,0.00009579476,0.00019404251,0.000076549244,0.000481126,0.0005483095,0.00041574702,0.000013203096],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000062099107,0.0000804689,0.0000030336294,0.001118143,0.00006357239,0.000023342212,0.0025094529,0.040915698,0.000081553786,0.9378376,0.000075440024,0.017285481],"study_design_scores_gemma":[0.00024718084,0.00004069275,0.0000014369315,0.0005894775,0.00005181212,0.000020850832,0.00006436606,0.7590803,0.0008435464,0.23809034,0.0003781158,0.00059191766],"about_ca_topic_score_codex":0.0000022876989,"about_ca_topic_score_gemma":0.000004953604,"teacher_disagreement_score":0.71816456,"about_ca_system_score_codex":0.00039063382,"about_ca_system_score_gemma":0.0002027929,"threshold_uncertainty_score":0.99955285},"labels":[],"label_agreement":null},{"id":"W3197777288","doi":"10.1287/opre.2022.0382","title":"Inverse Optimization: Theory and Applications","year":2023,"lang":"en","type":"article","venue":"Operations Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":69,"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; University of Toronto","funders":"","keywords":"Inverse; Mathematical optimization; Computer science; Optimization problem; Inverse problem; Range (aeronautics); Mathematics; Engineering","score_opus":0.05634145700229787,"score_gpt":0.3915288568863341,"score_spread":0.33518739988403623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197777288","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":[0.000112173424,0.000044389384,0.9936185,0.0011603633,0.000046670677,0.0005562413,0.000006114287,0.0002880267,0.0041675353],"genre_scores_gemma":[0.010586845,0.00064644584,0.9683998,0.00024219186,0.00016177101,0.0014329862,0.00007349814,0.0000312986,0.018425122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984647,0.0003526457,0.000145925,0.00040222678,0.0003541309,0.0002803544],"domain_scores_gemma":[0.99839115,0.00036073782,0.000010112223,0.0005254047,0.0005902048,0.00012237637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013267815,0.00008004029,0.00007625277,0.00048200332,0.00095525174,0.0003454629,0.00044452245,0.00004863516,0.00008107711],"category_scores_gemma":[0.00038053843,0.00007928749,0.000016513533,0.0029331099,0.00018607787,0.00076467637,0.00043295516,0.00019671822,0.00045845745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014480034,0.000021874095,0.000013564633,0.0000029395935,0.0000048569905,0.0000026929326,0.0003406295,0.7102177,0.00009664799,0.27740952,0.00036834934,0.0115197385],"study_design_scores_gemma":[0.00019657018,0.000020105419,0.00010128937,0.0000037728494,9.636561e-7,0.00000662091,0.00027450596,0.9901196,0.00016074307,0.0034246724,0.005597345,0.00009383639],"about_ca_topic_score_codex":0.000009635064,"about_ca_topic_score_gemma":0.000018052717,"teacher_disagreement_score":0.27990186,"about_ca_system_score_codex":0.00005812349,"about_ca_system_score_gemma":0.0001441073,"threshold_uncertainty_score":0.73471206},"labels":[],"label_agreement":null},{"id":"W3201643026","doi":"10.1016/j.jobe.2021.103272","title":"A hierarchical decomposition approach for multi-level building design optimization","year":2021,"lang":"en","type":"article","venue":"Journal of Building Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Decomposition; Optimization problem; Dimension (graph theory); Computer science; Set (abstract data type); Decomposition method (queueing theory); Mathematics","score_opus":0.05117331093593996,"score_gpt":0.30887728798071107,"score_spread":0.2577039770447711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201643026","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":[0.00022400689,0.00020923815,0.9987876,0.000082709594,0.00044306144,0.00016523094,0.0000022348922,0.00008103657,0.00000489785],"genre_scores_gemma":[0.017087933,0.000029031593,0.98260367,0.000039698232,0.00017790796,0.000013154898,0.0000028025302,0.000032062962,0.000013718007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987012,0.00005467697,0.00045807599,0.00026009599,0.00026149873,0.0002644645],"domain_scores_gemma":[0.9986198,0.00024322895,0.00026013044,0.00018507433,0.00056171854,0.00013003372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005068809,0.00016733097,0.00026682662,0.00030944627,0.00010943048,0.0001643711,0.00037312196,0.00007902627,0.0000016181475],"category_scores_gemma":[0.00055172894,0.00017499701,0.00013941358,0.0005025048,0.000011124053,0.00087571057,0.00009641676,0.00025535765,1.4195173e-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":[0.00000980966,0.00007532226,0.0000036072756,0.000024687866,0.00003812187,0.00001996168,0.000093431045,0.96487284,0.02814939,0.0020131872,0.000012882782,0.004686775],"study_design_scores_gemma":[0.0009928602,0.000056896657,0.000045054432,0.00007968682,0.000015410755,0.00043039588,0.000009716748,0.9666085,0.03144539,0.0000889634,0.000045016885,0.00018209081],"about_ca_topic_score_codex":2.3550022e-7,"about_ca_topic_score_gemma":2.2966304e-8,"teacher_disagreement_score":0.016863925,"about_ca_system_score_codex":0.00020222609,"about_ca_system_score_gemma":0.00011259251,"threshold_uncertainty_score":0.7136171},"labels":[],"label_agreement":null},{"id":"W3202513398","doi":"10.3390/a14100286","title":"Enhanced Hyper-Cube Framework Ant Colony Optimization for Combinatorial Optimization Problems","year":2021,"lang":"en","type":"article","venue":"Algorithms","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"École de Technologie Supérieure","funders":"","keywords":"Ant colony optimization algorithms; Mathematical optimization; Computer science; Domain (mathematical analysis); Heuristic; Optimization problem; Combinatorial optimization; Metaheuristic; Engineering design process; Discrete optimization; Set (abstract data type); Continuous optimization; Sequence (biology); Truss; Algorithm; Mathematics; Engineering; Multi-swarm optimization","score_opus":0.015335681380706772,"score_gpt":0.27116537417371767,"score_spread":0.2558296927930109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202513398","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":[0.00006635335,0.0003022515,0.992705,0.00063803885,0.0041529844,0.0011537679,0.00003180916,0.0005315227,0.00041826986],"genre_scores_gemma":[0.005050231,0.00027950868,0.9926383,0.00045309332,0.0005068056,0.00042916127,0.00021163053,0.000076958444,0.00035432403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99687344,0.00016107355,0.0006215223,0.0011550343,0.0005433877,0.00064556446],"domain_scores_gemma":[0.9964329,0.000481234,0.0003549872,0.00088643114,0.0016236337,0.00022081261],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003242666,0.00040123425,0.0004825687,0.00013967215,0.00048682673,0.00038065633,0.0007294564,0.00032965798,0.00012795483],"category_scores_gemma":[0.00091208576,0.00044335963,0.00016738517,0.0014858847,0.00009334376,0.0012694068,0.00031156713,0.00029702802,0.000021358263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001650872,0.00028092935,0.000008764109,0.0000266302,0.000044178458,0.00001364065,0.0004364947,0.96907204,0.0002529345,0.011274057,0.00011648964,0.01845735],"study_design_scores_gemma":[0.0020253179,0.00020576833,0.000011076712,0.00006959896,0.00002163532,0.00004088108,0.000055704666,0.98066276,0.009362372,0.005842926,0.001156882,0.0005450673],"about_ca_topic_score_codex":0.00000795697,"about_ca_topic_score_gemma":0.0000018634288,"teacher_disagreement_score":0.017912282,"about_ca_system_score_codex":0.00027042194,"about_ca_system_score_gemma":0.00035276747,"threshold_uncertainty_score":0.9998018},"labels":[],"label_agreement":null},{"id":"W3203480234","doi":"10.1155/2021/8548639","title":"Social Network Search for Solving Engineering Optimization Problems","year":2021,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":220,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Popularity; Benchmark (surveying); Metaheuristic; Conversation; Field (mathematics); Imitation; Social network (sociolinguistics); Optimization algorithm; Optimization problem; Mathematical optimization; Artificial intelligence; Machine learning; Social media; Algorithm; Mathematics; World Wide Web","score_opus":0.05464896600811137,"score_gpt":0.3121515330919351,"score_spread":0.25750256708382374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203480234","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":[0.000120142686,0.00012426099,0.9985071,0.0005768682,0.000326409,0.00020054546,0.0000026343462,0.000103947765,0.00003808871],"genre_scores_gemma":[0.23623928,0.00005387384,0.7629427,0.0005377045,0.000103935774,0.0000312176,0.000008838762,0.00001193031,0.000070537724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986157,0.000026807227,0.00021927351,0.00055566715,0.0002721551,0.00031041665],"domain_scores_gemma":[0.9989972,0.00028678201,0.00006354443,0.000115276656,0.00045626072,0.00008092449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019780078,0.00012416825,0.000118244556,0.00007479899,0.0004957608,0.00029806545,0.0003308202,0.000036876914,0.0000027716123],"category_scores_gemma":[0.0001649142,0.00013857005,0.000039159906,0.0008915372,0.00008958267,0.00071705296,0.00023243866,0.00010557737,0.0000017898218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012196384,0.00002101485,0.0000343663,0.000012425574,0.0000014046562,0.000003697852,0.00021943812,0.9491465,0.00022241335,0.045319162,0.000020137815,0.004998209],"study_design_scores_gemma":[0.00007096037,0.000035038767,0.00026833336,0.000015378753,0.0000017026452,0.00003678294,0.000015718913,0.9930306,0.0021388575,0.0040432434,0.00019293887,0.0001504419],"about_ca_topic_score_codex":7.3106025e-7,"about_ca_topic_score_gemma":3.161859e-7,"teacher_disagreement_score":0.23611914,"about_ca_system_score_codex":0.00003249344,"about_ca_system_score_gemma":0.00010822614,"threshold_uncertainty_score":0.56507224},"labels":[],"label_agreement":null},{"id":"W3204790331","doi":"10.1109/access.2021.3117560","title":"Efficient Design Space Exploration of OpenCL Kernels for FPGA Targets Using Black Box Optimization","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Scalability; Design space exploration; Bayesian optimization; Black box; Heuristic; Surrogate model; Field-programmable gate array; Mathematical optimization; Machine learning; Artificial intelligence; Embedded system","score_opus":0.1050094427677004,"score_gpt":0.3550077767830489,"score_spread":0.2499983340153485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204790331","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":[0.0005395721,0.00007202123,0.9976248,0.00019969307,0.00064845284,0.0007352252,0.0000078007815,0.00008126016,0.00009116853],"genre_scores_gemma":[0.07910812,0.000023939845,0.9205312,0.00009072159,0.000064302636,0.000045609093,0.000010894524,0.000024596708,0.00010061572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846876,0.00012573058,0.0003487382,0.00051627384,0.00029074342,0.00024977408],"domain_scores_gemma":[0.9979595,0.00017168235,0.00032174456,0.00045614652,0.0010182284,0.00007272887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027324128,0.00016741247,0.00022896037,0.00013306609,0.00014831442,0.0002452917,0.0006228003,0.00007052002,0.000016831524],"category_scores_gemma":[0.00023154102,0.00018012662,0.00006396382,0.0008642589,0.00005219886,0.0018249288,0.00019680626,0.00006439136,0.0000037264456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013605513,0.00010360037,0.000007509852,0.000021769978,0.0000134339025,0.000004517325,0.0005658549,0.99362266,0.0038228107,0.00053889153,0.00005707747,0.0012282452],"study_design_scores_gemma":[0.00048793425,0.000026786962,0.000010097549,0.000027803044,0.000009101336,0.000002790911,0.0000461445,0.742983,0.25562656,0.00060851057,0.000021276084,0.00015003466],"about_ca_topic_score_codex":0.000008917908,"about_ca_topic_score_gemma":0.0000015807296,"teacher_disagreement_score":0.25180376,"about_ca_system_score_codex":0.00012011189,"about_ca_system_score_gemma":0.00021932628,"threshold_uncertainty_score":0.73453504},"labels":[],"label_agreement":null},{"id":"W3215534145","doi":"10.2514/1.j060718","title":"Surrogate-Assisted Differential Evolution Using Knowledge-Transfer-Based Sampling for Expensive Optimization Problems","year":2021,"lang":"en","type":"article","venue":"AIAA Journal","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Simon Fraser University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Differential evolution; Mathematical optimization; Kriging; Computer science; Optimization problem; Surrogate model; Evolutionary algorithm; Mathematics; Machine learning","score_opus":0.06345597297973568,"score_gpt":0.31948138552715544,"score_spread":0.25602541254741973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215534145","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":[0.0031352206,0.00026005818,0.9946062,0.00019179411,0.001340364,0.0003204337,0.000008005166,0.00010062683,0.00003729304],"genre_scores_gemma":[0.22201093,0.000020148413,0.7775838,0.00005289532,0.00020860972,0.00001875764,0.000025711348,0.000030722735,0.00004842582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982158,0.00018069464,0.00046923998,0.0004453272,0.0002840918,0.0004048772],"domain_scores_gemma":[0.9973021,0.000186982,0.00018537245,0.0002677092,0.0018877969,0.00017001478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021327505,0.00022182135,0.00025492703,0.00021877079,0.00065594213,0.00036857816,0.000321193,0.000109085944,0.00005299932],"category_scores_gemma":[0.0002075795,0.00022902932,0.00018059982,0.00057593244,0.00004513456,0.00087022805,0.00007114242,0.00024967597,0.0000032204834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001912013,0.00017150273,0.000058068912,0.000020357526,0.00003484604,0.0000096344775,0.00028014497,0.9800322,0.012808738,0.00087431807,0.000009368859,0.005681702],"study_design_scores_gemma":[0.0022385982,0.000076814205,0.00027317164,0.00012732836,0.000032791082,0.00021726672,0.00007859243,0.98856425,0.0073492406,0.0006852012,0.00008699006,0.00026975016],"about_ca_topic_score_codex":0.0000034281907,"about_ca_topic_score_gemma":0.000009908364,"teacher_disagreement_score":0.2188757,"about_ca_system_score_codex":0.00045807046,"about_ca_system_score_gemma":0.0006211632,"threshold_uncertainty_score":0.9339545},"labels":[],"label_agreement":null},{"id":"W337827445","doi":"10.4018/978-1-60566-798-0.ch009","title":"Statistical Analysis of Computational Intelligence Algorithms on a Multi-Objective Filter Design Problem","year":2010,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Simulated annealing; Mathematical optimization; Sorting; Finite impulse response; Algorithm; Multi-objective optimization; Particle swarm optimization; Optimization problem; Genetic algorithm; Weighting; Mathematics","score_opus":0.034234867627347516,"score_gpt":0.29741750593233646,"score_spread":0.26318263830498895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W337827445","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":[8.981394e-7,0.000025223706,0.8961939,0.00002073668,0.00029038553,0.00085515715,0.00076346693,0.00018210366,0.10166808],"genre_scores_gemma":[0.004729078,0.000002853489,0.98955697,0.00023688018,0.000056114062,0.00005396792,0.000047549536,0.000049749888,0.0052668517],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99600744,0.00011521876,0.0009409012,0.0013918126,0.0010199203,0.000524689],"domain_scores_gemma":[0.9962808,0.0006887275,0.0007772243,0.0009009967,0.0010739621,0.0002783082],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000330709,0.000724132,0.0010574046,0.0006151124,0.00017967168,0.0001338187,0.0011721776,0.0005182566,0.000087814784],"category_scores_gemma":[0.00011925463,0.0007195205,0.00038220026,0.00031524617,0.00043768578,0.0001716162,0.00044481296,0.0007497148,0.00010531942],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040737545,0.00009125702,0.0000049017153,0.000012711049,0.00087160873,0.0000562313,0.0002576278,0.23968326,0.0000063108478,0.7298891,0.00002701869,0.029059222],"study_design_scores_gemma":[0.00041623262,0.00033246685,0.00018790396,0.00008870226,0.00039565613,0.000022345655,0.0000076655815,0.6490651,0.00029838056,0.34824714,0.00019603636,0.00074237573],"about_ca_topic_score_codex":0.000041473333,"about_ca_topic_score_gemma":0.00004168104,"teacher_disagreement_score":0.40938184,"about_ca_system_score_codex":0.00042885108,"about_ca_system_score_gemma":0.0004731346,"threshold_uncertainty_score":0.9995256},"labels":[],"label_agreement":null},{"id":"W35595736","doi":"10.1007/978-3-642-37140-0_16","title":"Knowledge Transfer Strategies for Vector Evaluated Particle Swarm Optimization","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","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":"Brock University","funders":"","keywords":"Particle swarm optimization; Crossover; Computer science; Multi-swarm optimization; Inefficiency; Swarm behaviour; Mathematical optimization; Set (abstract data type); Algorithm; Artificial intelligence; Mathematics","score_opus":0.030469963994475928,"score_gpt":0.29060637780074855,"score_spread":0.2601364138062726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W35595736","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":[0.000013943979,0.00034830256,0.9947252,0.00037192728,0.0014571607,0.0014469033,0.000010700664,0.00031426054,0.0013115439],"genre_scores_gemma":[0.03767416,0.00003918405,0.96082705,0.00035984584,0.00029297892,0.00012501371,0.000016924581,0.00006571557,0.00059911865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99644995,0.000052221276,0.0005811455,0.0015778694,0.0006238041,0.0007150404],"domain_scores_gemma":[0.9970289,0.0005217475,0.00016191821,0.00097284757,0.0011165078,0.0001980855],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057097257,0.0005738064,0.0005126475,0.00046140922,0.00032638648,0.00081169803,0.0019209557,0.00030485,0.00007002491],"category_scores_gemma":[0.0001148716,0.00053921505,0.00014226524,0.0007822366,0.0005123655,0.001780729,0.0003633992,0.0004205411,0.00006367852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059351482,0.00004113097,7.486064e-7,0.000024983035,0.000010700249,0.000003420328,0.0008682767,0.84868157,0.00010905837,0.026683856,0.000008759107,0.123561576],"study_design_scores_gemma":[0.0006802052,0.00021336193,0.000012468285,0.00011993924,0.0000123304,0.000012405713,7.8127886e-7,0.9596747,0.0024247838,0.035958763,0.00027971808,0.0006105039],"about_ca_topic_score_codex":0.000008002921,"about_ca_topic_score_gemma":0.000028321821,"teacher_disagreement_score":0.122951075,"about_ca_system_score_codex":0.00040742854,"about_ca_system_score_gemma":0.0009629592,"threshold_uncertainty_score":0.9997059},"labels":[],"label_agreement":null},{"id":"W4200560384","doi":"10.1007/s00158-021-03134-4","title":"Surrogate-based optimization based on the probability of feasibility","year":2021,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 Victoria","funders":"Fundação para a Ciência e a Tecnologia","keywords":"Mathematical optimization; Computer science; Aerodynamics; Constraint (computer-aided design); Engineering design process; Surrogate model; Convergence (economics); Inviscid flow; Airfoil; Nonlinear programming; Uncertainty quantification; Optimization problem; Mathematics; Nonlinear system; Machine learning; Engineering","score_opus":0.024377238002608607,"score_gpt":0.27205613855367605,"score_spread":0.24767890055106745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200560384","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":[0.011739866,0.0000392438,0.9849062,0.0019214895,0.00026600287,0.00068740995,0.00003128396,0.00012517092,0.00028334477],"genre_scores_gemma":[0.43571502,0.0000075505545,0.5640202,0.000093446055,0.00001728257,0.00002311499,0.00008697675,0.000011542108,0.000024850307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978998,0.00034837247,0.0004243368,0.0007150267,0.0003682029,0.00024429732],"domain_scores_gemma":[0.9977548,0.00037601008,0.00026905633,0.0007694801,0.00073256984,0.00009809121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003206376,0.00026059066,0.0002579272,0.00009214565,0.00048765945,0.00009355972,0.00033136667,0.000102094535,0.00007942082],"category_scores_gemma":[0.0004225778,0.00018851728,0.000094101895,0.0008194506,0.00022847661,0.00051531626,0.0002091304,0.00016588964,8.1448894e-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":[0.000056030658,0.00008577021,0.0016823887,0.000045294804,0.000006809257,0.0000030326532,0.00015974439,0.9951694,0.000098149205,0.0019716558,0.000003007235,0.0007187368],"study_design_scores_gemma":[0.0010261931,0.000120819604,0.0051765675,0.000034715114,0.000016068761,0.000005210617,0.000059187423,0.98937404,0.0027908906,0.0011802235,0.0000017048551,0.00021437898],"about_ca_topic_score_codex":0.000005443016,"about_ca_topic_score_gemma":0.0000055161404,"teacher_disagreement_score":0.42397517,"about_ca_system_score_codex":0.000107129315,"about_ca_system_score_gemma":0.00021836217,"threshold_uncertainty_score":0.76875114},"labels":[],"label_agreement":null},{"id":"W4205737100","doi":"10.1109/smc52423.2021.9659146","title":"Reference Point-Based Particle Sub-Swarm Optimization","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Particle swarm optimization; Multi-swarm optimization; Mathematical optimization; Equidistant; Maxima and minima; Position (finance); Multi-objective optimization; Metaheuristic; Computation; Computer science; Swarm behaviour; Pareto principle; Point (geometry); Optimization problem; Algorithm; Mathematics","score_opus":0.04976894851196202,"score_gpt":0.2923497905512877,"score_spread":0.24258084203932567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205737100","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":[0.0049930774,0.00016901553,0.97636527,0.0014424201,0.0015876918,0.0002742626,0.00004543422,0.00012682118,0.014996025],"genre_scores_gemma":[0.9428476,0.00053128373,0.051312476,0.0006218368,0.00020060096,0.00008178207,0.000102402955,0.000033245426,0.004268778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99733824,0.00021191433,0.00054002955,0.0008465807,0.0007230202,0.0003401987],"domain_scores_gemma":[0.9973017,0.00016929407,0.00028126076,0.0005824992,0.0014724215,0.00019282241],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026422637,0.00029961462,0.00030324198,0.00014409646,0.00015698583,0.00073003693,0.0006275115,0.00013654733,0.00018471997],"category_scores_gemma":[0.00014528408,0.00031332963,0.00006187272,0.00036952985,0.00009745044,0.0005098548,0.00015795117,0.000248849,0.00013333594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057079655,0.00048457462,0.0004863099,0.000055069544,0.00011558426,0.00017826629,0.00038466053,0.5775074,0.0038564403,0.40857,0.0006227925,0.0076818787],"study_design_scores_gemma":[0.0010727544,0.00009466715,0.00022076203,0.00014990999,0.000012511795,0.000043517633,0.00021697322,0.98160714,0.0136025585,0.0005262867,0.0020755555,0.00037735837],"about_ca_topic_score_codex":0.0000447023,"about_ca_topic_score_gemma":0.000030796684,"teacher_disagreement_score":0.9378545,"about_ca_system_score_codex":0.00015477167,"about_ca_system_score_gemma":0.00027545434,"threshold_uncertainty_score":0.9999319},"labels":[],"label_agreement":null},{"id":"W4206267728","doi":"10.1287/opre.2021.2157","title":"Robust Learning of Consumer Preferences","year":2021,"lang":"en","type":"article","venue":"Operations Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Consumer choice; Focus (optics); Mathematical optimization; Sequence (biology); Asymptotically optimal algorithm; Artificial intelligence; Machine learning; Operations research; Marketing; Algorithm; Mathematics; Business","score_opus":0.14610335025292934,"score_gpt":0.389352057866647,"score_spread":0.24324870761371767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206267728","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":[0.005210468,0.0002800906,0.98659605,0.00036506506,0.000069986476,0.00013117252,0.000002429877,0.000041719584,0.007302999],"genre_scores_gemma":[0.33918375,0.0002758507,0.6530901,0.000018545361,0.000022734976,0.00004454259,0.000012976515,0.000007733252,0.0073437444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843305,0.00042107847,0.00017233792,0.00030704815,0.0004538307,0.00021262183],"domain_scores_gemma":[0.99742323,0.00022329559,0.000015225924,0.00036011665,0.0019147304,0.00006339356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004718124,0.000059365073,0.00009986851,0.00016266965,0.00037998133,0.00018477927,0.00037586683,0.000038828486,0.00021666457],"category_scores_gemma":[0.0010021054,0.00005706364,0.000023915793,0.0012188822,0.0001285742,0.0005131182,0.00031145304,0.00030383284,0.000078974146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020024202,0.00013544482,0.00074076344,0.000010117927,0.00001909031,0.000013488364,0.0009763007,0.9193625,0.006959445,0.04332962,0.00015867308,0.028292589],"study_design_scores_gemma":[0.00024851123,0.00005132432,0.0007223029,0.000019513112,0.0000012259292,0.00001118335,0.00048247862,0.96619374,0.029077692,0.00028367544,0.0028120538,0.000096304604],"about_ca_topic_score_codex":0.000044609747,"about_ca_topic_score_gemma":0.00007155645,"teacher_disagreement_score":0.33397326,"about_ca_system_score_codex":0.000040302602,"about_ca_system_score_gemma":0.0004551536,"threshold_uncertainty_score":0.29225475},"labels":[],"label_agreement":null},{"id":"W4206881206","doi":"","title":"APM-MOEA : An asynchronous parallel model for multi-objective evolutionary algorithms","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Asynchronous communication; Evolutionary algorithm; Evolutionary computation; Parallel computing; Algorithm; Theoretical computer science; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.03019648609902191,"score_gpt":0.2798584387207837,"score_spread":0.24966195262176177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206881206","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":[0.00043561048,0.0005607642,0.9907328,0.0015867031,0.0006297902,0.0019037792,0.00029011726,0.0009567189,0.0029037423],"genre_scores_gemma":[0.024990832,0.0002409298,0.9680517,0.00018035396,0.00009695609,0.00079528673,0.00082700537,0.0001239661,0.004693002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99206305,0.003080337,0.0008894759,0.0024219605,0.0007025222,0.0008426788],"domain_scores_gemma":[0.9859002,0.0010149578,0.0009828972,0.004145458,0.007496965,0.0004595035],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0035246909,0.0007813744,0.00070388423,0.000444365,0.0009836388,0.00064725324,0.00391697,0.0006041765,0.000032284686],"category_scores_gemma":[0.0015807704,0.00089135335,0.00039958846,0.00061611977,0.0005285521,0.0011111883,0.0031940746,0.0008366511,0.000052049556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012345804,0.007167488,0.00043596711,0.0004416094,0.0006514662,0.00002382995,0.068476535,0.5145009,0.00071243476,0.18967871,0.0022645483,0.21552303],"study_design_scores_gemma":[0.0014029755,0.0000029328542,0.000633014,0.0003779051,0.00003766215,0.000017818586,0.00008209286,0.97901,0.0018997103,0.015042599,0.00058346905,0.0009098469],"about_ca_topic_score_codex":0.0003058489,"about_ca_topic_score_gemma":0.0004896699,"teacher_disagreement_score":0.46450904,"about_ca_system_score_codex":0.00071465725,"about_ca_system_score_gemma":0.0011581826,"threshold_uncertainty_score":0.9993537},"labels":[],"label_agreement":null},{"id":"W4207001343","doi":"10.1111/itor.13116","title":"Inverse attribute‐based optimization with an application in assortment optimization","year":2022,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Regret; Set (abstract data type); Mathematical optimization; Perspective (graphical); Inverse; Range (aeronautics); Artificial intelligence; Machine learning; Mathematics","score_opus":0.05580652844780478,"score_gpt":0.3745006731160889,"score_spread":0.31869414466828416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207001343","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":[0.0007840035,0.000009416229,0.9933412,0.0038723787,0.00021896753,0.00093522755,0.000063145286,0.00008609218,0.00068954955],"genre_scores_gemma":[0.37190518,0.000020847276,0.6244787,0.00030313848,0.00005082878,0.0022407933,0.000613196,0.000028984074,0.00035831402],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959605,0.0005977188,0.0004818839,0.0007743546,0.0018230941,0.00036250084],"domain_scores_gemma":[0.9981523,0.00027579884,0.00009449356,0.00042689012,0.00094349217,0.00010701165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014797066,0.00018024155,0.00015400905,0.0016103856,0.0005683181,0.00022752876,0.0009816408,0.000067389665,0.0008143256],"category_scores_gemma":[0.00008819039,0.00020200928,0.000038095088,0.0025974794,0.00009490185,0.0019935954,0.000079068945,0.0006994512,0.000010177226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000114484326,0.0009057531,0.00092078396,0.0000026596592,0.000012712761,0.00001781854,0.00027457444,0.9920391,0.0000456142,0.0034722944,0.0000109468765,0.0021832637],"study_design_scores_gemma":[0.0018456061,0.00022723692,0.0008866504,0.00001105821,0.0000017335548,0.000020603513,0.00029369572,0.99536544,0.00016097754,0.00017870708,0.0007964088,0.00021186906],"about_ca_topic_score_codex":0.00028757428,"about_ca_topic_score_gemma":0.00047961326,"teacher_disagreement_score":0.37112117,"about_ca_system_score_codex":0.0022170374,"about_ca_system_score_gemma":0.0007286912,"threshold_uncertainty_score":0.8916294},"labels":[],"label_agreement":null},{"id":"W4211059871","doi":"10.1115/1.4044525","title":"Knowledge-Assisted Optimization for Large-Scale Design Problems: A Review and Proposition","year":2019,"lang":"en","type":"review","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Design knowledge; Scale (ratio); Industrial engineering; Engineering optimization; Management science; Optimization problem; Engineering","score_opus":0.10066591990374062,"score_gpt":0.3572525854471908,"score_spread":0.25658666554345017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211059871","genre_codex":"methods","genre_gemma":"review","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":[1.3048346e-10,0.49100292,0.50610375,0.00006100907,0.00023405156,0.0025575168,0.000006120985,0.000027807606,0.0000068099203],"genre_scores_gemma":[2.2995646e-8,0.50516367,0.49446514,0.00006696399,0.00006045837,0.00011778531,0.0000065203485,0.000036745078,0.000082699604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957109,0.0013054737,0.0016060902,0.0006081702,0.00038565253,0.00038370173],"domain_scores_gemma":[0.99475,0.0009916901,0.0022727493,0.0004675448,0.0012849917,0.00023301323],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0031895903,0.0004982545,0.0023159839,0.00035312964,0.00014377538,0.0001619699,0.00090201973,0.0003786801,0.000018060435],"category_scores_gemma":[0.00066736166,0.0003751922,0.0005196755,0.00082042575,0.000022221553,0.00082581944,0.00018546167,0.00052012905,0.00001397418],"study_design_candidate":"design_other","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":[0.000023441009,0.0004095487,3.6457724e-9,0.031702638,0.00023425181,0.000009974174,0.000053595937,0.01566746,0.000003986947,0.0010472704,0.001503216,0.94934464],"study_design_scores_gemma":[0.0014650262,0.0015061755,3.3471284e-8,0.052827623,0.0024269372,0.0010285429,0.0000043136856,0.77256733,0.000017190701,0.0009410207,0.16652505,0.0006907311],"about_ca_topic_score_codex":1.0497785e-7,"about_ca_topic_score_gemma":1.2172278e-7,"teacher_disagreement_score":0.9486539,"about_ca_system_score_codex":0.00033528067,"about_ca_system_score_gemma":0.0008239148,"threshold_uncertainty_score":0.99987},"labels":[],"label_agreement":null},{"id":"W4211161595","doi":"10.1007/978-3-540-74757-4_6","title":"Heuristic Methods","year":2015,"lang":"en","type":"book-chapter","venue":"Advances in geographic information science","topic":"Advanced Multi-Objective Optimization Algorithms","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":"Toronto Metropolitan University; Western University","funders":"","keywords":"Heuristics; Simulated annealing; Tabu search; Computer science; Metaheuristic; Heuristic; Genetic algorithm; Greedy algorithm; Hyper-heuristic; Mathematical optimization; Artificial intelligence; Machine learning; Mathematics; Algorithm","score_opus":0.020359182496856195,"score_gpt":0.3419816649500707,"score_spread":0.3216224824532145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211161595","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":[2.2065028e-7,0.0013021445,0.6042732,0.00004277196,0.0009706678,0.00029849578,0.000008407843,0.00015811643,0.392946],"genre_scores_gemma":[0.00038612462,0.0030118304,0.98874223,0.00043648577,0.000051885338,0.000054643562,0.000026354941,0.000020970576,0.007269486],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99623173,0.000045661833,0.0009698959,0.0006745364,0.0014967044,0.0005814442],"domain_scores_gemma":[0.9958989,0.00021958991,0.0008575159,0.0011439442,0.0016180748,0.0002619846],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0027130668,0.00043749253,0.0004405518,0.0029644165,0.0002915044,0.00036691135,0.002606986,0.00019654857,0.000041657517],"category_scores_gemma":[0.0007866528,0.00043256534,0.000103648636,0.0019322295,0.0011566412,0.021879671,0.0007433944,0.00057793065,0.00020698033],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036993217,0.0000076026085,0.000017878581,0.000020911499,0.0000032558585,0.0000043005634,0.00034325733,0.022154426,0.0000012185252,0.6290594,0.000025380039,0.3483587],"study_design_scores_gemma":[0.00051376584,0.00008416052,0.00008803437,0.00013452,0.0000066012585,0.000047100428,0.0000450164,0.16074984,0.000027681905,0.23822504,0.5992959,0.0007823474],"about_ca_topic_score_codex":0.0000050436024,"about_ca_topic_score_gemma":0.00000640516,"teacher_disagreement_score":0.5992705,"about_ca_system_score_codex":0.00033658737,"about_ca_system_score_gemma":0.0005943791,"threshold_uncertainty_score":0.9998126},"labels":[],"label_agreement":null},{"id":"W4221008044","doi":"10.21203/rs.3.rs-1503527/v1","title":"Quantum Multi-guide Particle Swarm Optimisation for Dynamic Multi-objective Optimisation Problems","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Brock University","funders":"","keywords":"Particle swarm optimization; Benchmark (surveying); Mathematical optimization; Computer science; Crossover; Swarm behaviour; Set (abstract data type); Pareto optimal; Pareto principle; Multi-swarm optimization; Multi-objective optimization; Algorithm; Mathematics; Artificial intelligence","score_opus":0.09261859524340452,"score_gpt":0.4164968811561475,"score_spread":0.32387828591274304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221008044","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":[0.0019368457,0.00057989237,0.98632455,0.00091706816,0.0009578008,0.008142822,0.0002886911,0.0007182591,0.00013408679],"genre_scores_gemma":[0.097384185,0.00047844948,0.8884924,0.00006605047,0.00012984661,0.0090899635,0.000731854,0.00017949883,0.0034477292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9917597,0.0013340252,0.0010131329,0.002466559,0.0019623325,0.0014642219],"domain_scores_gemma":[0.9928783,0.0011602159,0.00057390775,0.002013084,0.0029736396,0.00040082727],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0037227261,0.0006473658,0.0006492076,0.0008606035,0.001363812,0.00071108213,0.002321257,0.00042532326,0.00008936593],"category_scores_gemma":[0.0023136274,0.00071363564,0.00037219984,0.001542777,0.00026606108,0.0010528942,0.0038554713,0.0020623861,0.000060280978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059379705,0.00084984547,0.00017015784,0.0004669543,0.00010000976,0.000015464051,0.0048801205,0.98004985,0.0006684169,0.002255496,0.00016473126,0.010319547],"study_design_scores_gemma":[0.0024434356,0.00041500045,0.0014169319,0.00019973956,0.000017503977,0.0000073028496,0.0013571779,0.9881979,0.0015023724,0.002309171,0.0013874487,0.0007459715],"about_ca_topic_score_codex":0.00035682527,"about_ca_topic_score_gemma":0.00013206966,"teacher_disagreement_score":0.09783211,"about_ca_system_score_codex":0.0033778541,"about_ca_system_score_gemma":0.0012979377,"threshold_uncertainty_score":0.9999363},"labels":[],"label_agreement":null},{"id":"W4221068686","doi":"10.3390/a15040102","title":"Dynamic Line Scan Thermography Parameter Design via Gaussian Process Emulation","year":2022,"lang":"en","type":"article","venue":"Algorithms","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Vlaamse regering; Universiteit Antwerpen; Fonds Wetenschappelijk Onderzoek","keywords":"Emulation; Computer science; Gaussian process; Surrogate model; Process (computing); Set (abstract data type); Thermography; Gaussian; Machine learning; Artificial intelligence; Simulation","score_opus":0.016238384567365757,"score_gpt":0.27751105129380016,"score_spread":0.2612726667264344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221068686","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":[0.000613879,0.00015152025,0.99685633,0.00047450382,0.00055787957,0.0006874021,0.000013535622,0.00046501073,0.00017996428],"genre_scores_gemma":[0.33003452,0.000010178446,0.6689861,0.00034323125,0.00003965914,0.00029820128,0.00003374014,0.000042988566,0.0002113901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974408,0.00027776093,0.00036854733,0.000793865,0.0006465583,0.0004724689],"domain_scores_gemma":[0.99853504,0.00019711166,0.00024879907,0.0006894702,0.00018391557,0.00014569116],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039260855,0.00028985977,0.0002588617,0.00039389447,0.000611149,0.00012406394,0.0010174636,0.000060554714,0.00011684857],"category_scores_gemma":[0.000049085393,0.0002952393,0.00011678991,0.0018591125,0.00007809086,0.0007819309,0.00031763467,0.0003487518,0.000024000692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021320133,0.0002588,0.00013202094,0.000008263161,0.00005560458,0.000051027248,0.0012373931,0.7000604,0.00035611412,0.00032103484,0.00003108421,0.2974669],"study_design_scores_gemma":[0.0006052292,0.00026096954,0.0010586804,0.0000054829115,0.000011526824,0.00008322216,0.00006968335,0.9892243,0.0005398189,0.0075716386,0.00018152867,0.00038788596],"about_ca_topic_score_codex":0.000020492917,"about_ca_topic_score_gemma":0.0000029463051,"teacher_disagreement_score":0.32942066,"about_ca_system_score_codex":0.00020103475,"about_ca_system_score_gemma":0.000101332815,"threshold_uncertainty_score":0.99995},"labels":[],"label_agreement":null},{"id":"W4221140136","doi":"10.48550/arxiv.2203.06336","title":"A New and Flexible Design Construction for Orthogonal Arrays for Modern Applications","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Orthogonal array; Latin hypercube sampling; Computer science; Orthogonal basis; Orthogonal coordinates; Algorithm; Orthogonal transformation; Computer experiment; Hypercube; Theoretical computer science; Computer engineering; Engineering drawing; Computational science; Mathematics; Parallel computing; Engineering; Geometry; Simulation; Monte Carlo method; Taguchi methods","score_opus":0.09067192044786006,"score_gpt":0.22477074464857785,"score_spread":0.13409882420071778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221140136","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":[0.000073747615,0.000053385793,0.99678653,0.000085887536,0.0002792049,0.0021996053,0.00009402419,0.00025603306,0.0001715613],"genre_scores_gemma":[0.016132481,0.00007725503,0.98174894,0.00005297954,0.00008576496,0.00011267727,0.000061100116,0.000026547987,0.0017022332],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839723,0.000053454452,0.00016294325,0.0010780655,0.00006574349,0.00024253395],"domain_scores_gemma":[0.9985736,0.00021374635,0.00025427245,0.0005721229,0.00023666225,0.00014962202],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015318335,0.00023004915,0.00023485906,0.00022541001,0.00040345063,0.00008768296,0.00067314453,0.00013180955,0.000016748942],"category_scores_gemma":[0.000029756278,0.00030517645,0.0001253548,0.0003804896,0.00007528888,0.00035117782,0.00068329676,0.00022210744,0.0000020092516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043603515,0.000024042954,0.00002245732,0.000028995306,0.000037742546,0.000001442528,0.00008484469,0.729462,0.000020418809,0.25355443,0.000058817488,0.016661165],"study_design_scores_gemma":[0.00070085994,0.000044905053,0.0000095457,0.000006538933,0.000034431894,0.000004631628,0.000039795963,0.6983519,0.00007222857,0.29895324,0.0015582612,0.0002236294],"about_ca_topic_score_codex":0.000010676348,"about_ca_topic_score_gemma":0.0000033623367,"teacher_disagreement_score":0.0453988,"about_ca_system_score_codex":0.00022110883,"about_ca_system_score_gemma":0.0004341,"threshold_uncertainty_score":0.99994004},"labels":[],"label_agreement":null},{"id":"W4225124397","doi":"10.1016/j.dche.2022.100030","title":"Data-Driven Natural Gas Compressor Models for Gas Transport Network Optimization","year":2022,"lang":"en","type":"article","venue":"Digital Chemical Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gas compressor; Dimensionless quantity; Natural gas; Artificial neural network; Minification; Computer science; Surrogate model; Mathematical optimization; Compressor station; Engineering; Mathematics; Artificial intelligence; Mechanical engineering; Thermodynamics; Physics","score_opus":0.017787535980550107,"score_gpt":0.22579464947729005,"score_spread":0.20800711349673995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225124397","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":[0.00025197136,0.00011342717,0.99802864,0.00008367896,0.00035312143,0.00031713513,0.0002480767,0.0004309664,0.00017298387],"genre_scores_gemma":[0.315175,0.0000042913452,0.6830543,0.000066825894,0.0001284999,0.00011161996,0.0013751767,0.00004201166,0.000042226817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985602,0.000005342605,0.0002565759,0.0005307537,0.00027854077,0.00036859242],"domain_scores_gemma":[0.9991322,0.00011189319,0.00006611188,0.0005119944,0.00007645237,0.000101329795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006765662,0.0001915143,0.00020086592,0.000053891385,0.00010891248,0.000104766346,0.0010916404,0.000035427944,0.0000056221356],"category_scores_gemma":[0.00004841912,0.00022120611,0.00007043655,0.00038712157,0.000018213284,0.001768919,0.0005466439,0.00022212324,8.95239e-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":[0.000010600978,0.00003445804,0.0000023643856,0.000014004506,0.000018539786,0.0000042740676,0.00004586602,0.996629,0.00020619137,0.0015048265,0.00010080487,0.0014290421],"study_design_scores_gemma":[0.00044906922,0.000017498272,9.608835e-7,0.000008480014,0.000005508665,0.000017843531,0.0000047896415,0.9970532,0.00035844563,0.00036604432,0.001455009,0.0002631262],"about_ca_topic_score_codex":6.002307e-7,"about_ca_topic_score_gemma":8.174656e-8,"teacher_disagreement_score":0.3149743,"about_ca_system_score_codex":0.00011613493,"about_ca_system_score_gemma":0.00002774223,"threshold_uncertainty_score":0.9020523},"labels":[],"label_agreement":null},{"id":"W4226385637","doi":"10.1080/03155986.2022.2040274","title":"Approximating the Pareto frontier for a challenging real-world bi-objective covering problem","year":2022,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pareto principle; Mathematical optimization; Subgradient method; Heuristic; Computer science; Set (abstract data type); Constraint (computer-aided design); Frontier; Set cover problem; Decision maker; Pareto optimal; Multi-objective optimization; Efficient frontier; Operations research; Mathematics; Economics; Geography","score_opus":0.04901622433517749,"score_gpt":0.3374266635117765,"score_spread":0.28841043917659903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226385637","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":[0.00040108597,0.00009048195,0.98365825,0.0006821085,0.00028090522,0.0023827257,0.00005529689,0.000082041675,0.01236709],"genre_scores_gemma":[0.79595286,0.000085185304,0.18975483,0.00055213785,0.0002902208,0.010276268,0.00021734938,0.000032378568,0.002838774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997564,0.00017731823,0.0005945939,0.00021589463,0.0010820371,0.00036615832],"domain_scores_gemma":[0.9978075,0.0005608992,0.00019783045,0.00027044656,0.0010865309,0.00007680251],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0032445786,0.00013128645,0.0001633846,0.0004703268,0.0027075605,0.0010175533,0.00049389416,0.0000316457,0.000009237424],"category_scores_gemma":[0.00026623197,0.00010490559,0.00004071141,0.0008546749,0.00006382769,0.003485154,0.00055995584,0.00034320945,0.000009616738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026421321,0.000015590465,0.0001623571,0.00008582848,0.000031023177,4.660775e-7,0.009568158,0.5194955,0.000015118738,0.4574276,0.00072057784,0.012451385],"study_design_scores_gemma":[0.00053460005,0.0000786089,0.00028737696,0.000022410106,0.0000012977607,0.000016218637,0.005924305,0.92791116,0.000035920923,0.0005059597,0.064545065,0.00013705234],"about_ca_topic_score_codex":0.0002708469,"about_ca_topic_score_gemma":0.000018418978,"teacher_disagreement_score":0.7955518,"about_ca_system_score_codex":0.00039330064,"about_ca_system_score_gemma":0.0002948923,"threshold_uncertainty_score":0.99859077},"labels":[],"label_agreement":null},{"id":"W4229988500","doi":"10.23952/jano.1.2019.3.08","title":"Representation of the Pareto front for heterogeneous multi-objective optimization","year":2019,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft","keywords":"Representation (politics); Multi-objective optimization; Front (military); Pareto principle; Computer science; Mathematical optimization; Mathematical economics; Mathematics; Engineering; Political science; Mechanical engineering","score_opus":0.01168561794993419,"score_gpt":0.25649472857169725,"score_spread":0.24480911062176305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229988500","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":[0.0006594934,0.000060240873,0.9979388,0.00014740264,0.00036226187,0.00065479847,0.0000036701372,0.00001881449,0.00015450554],"genre_scores_gemma":[0.24696593,0.00005984442,0.7527613,0.000106271524,0.000041239575,0.000014132134,0.0000042429456,0.000015732105,0.00003132449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998677,0.00006071843,0.0005507531,0.00026233986,0.00029845984,0.00015073267],"domain_scores_gemma":[0.9979927,0.00017283903,0.00094294717,0.0002501536,0.00056962564,0.00007172318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001877654,0.00014772158,0.0003392973,0.00011962295,0.00009788469,0.000053041073,0.0003199475,0.00008129677,0.000013411333],"category_scores_gemma":[0.00012070129,0.000109192224,0.00012517234,0.0003934857,0.000051084167,0.00045916167,0.00009808566,0.000118032316,9.129012e-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":[0.00011721305,0.00008933517,0.0001443469,0.000012269044,0.000037056237,3.4365507e-7,0.00049877586,0.9947791,0.00033823907,0.00049325434,0.000011150963,0.0034789278],"study_design_scores_gemma":[0.0016165159,0.000173394,0.00030687387,0.000020788879,0.000025472125,0.000018867562,0.00009362863,0.9919469,0.005316003,0.0003264735,0.00003320071,0.000121900346],"about_ca_topic_score_codex":0.0000022356683,"about_ca_topic_score_gemma":2.0527789e-7,"teacher_disagreement_score":0.24630643,"about_ca_system_score_codex":0.00007880884,"about_ca_system_score_gemma":0.00006512684,"threshold_uncertainty_score":0.44527298},"labels":[],"label_agreement":null},{"id":"W4235948861","doi":"10.32920/14640045.v1","title":"Wireless Sensor Network Optimization: Multi-Objective Paradigm","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Optimization problem; Computer science; Software deployment; Key distribution in wireless sensor networks; Wireless network; Wireless; Multi-objective optimization; Mathematical optimization; Distributed computing; Network planning and design; Computer network; Telecommunications; Mathematics; Machine learning; Algorithm","score_opus":0.022769217665029595,"score_gpt":0.2732898342041067,"score_spread":0.2505206165390771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235948861","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":[0.000044094093,0.00049951975,0.99044865,0.00062672084,0.0034001167,0.0010654242,0.000015928123,0.001196369,0.0027031843],"genre_scores_gemma":[0.0040312074,0.0004604646,0.9915024,0.0007832674,0.0005249425,0.00023334249,0.00016515497,0.00009870232,0.0022005392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950546,0.0004175672,0.00078274525,0.002265019,0.0006410704,0.0008389716],"domain_scores_gemma":[0.99568844,0.00026804977,0.00060197536,0.0021925191,0.0009270243,0.00032198353],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032131118,0.000817764,0.0009081998,0.00022061396,0.00037422837,0.00093361875,0.0017687766,0.0006124915,0.00020405263],"category_scores_gemma":[0.00013312204,0.0008618492,0.0003413844,0.001194642,0.00014144025,0.00088385586,0.0036115455,0.0011275592,0.000049096718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057696698,0.00016770068,0.000086295,0.000032558564,0.00013968677,0.000110088695,0.00062253524,0.9914246,0.000004368874,0.003641685,0.00018427419,0.0035803886],"study_design_scores_gemma":[0.00075527007,0.00002676353,0.00024614992,0.00014096187,0.00002784963,0.000054420303,0.00013757592,0.99655783,0.000315599,0.0005289558,0.00020773915,0.0010009034],"about_ca_topic_score_codex":0.00006267702,"about_ca_topic_score_gemma":0.000037579226,"teacher_disagreement_score":0.0051331627,"about_ca_system_score_codex":0.00044450583,"about_ca_system_score_gemma":0.0006884471,"threshold_uncertainty_score":0.9993832},"labels":[],"label_agreement":null},{"id":"W4242376211","doi":"10.13052/remn.17.103-126","title":"A comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimization","year":2008,"lang":"en","type":"article","venue":"European Journal of Computational Mechanics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":138,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Convergence (economics); Mathematical optimization; Aerodynamics; Context (archaeology); Computation; Genetic algorithm; Algorithm; Gradient method; Multi-objective optimization; Meta-optimization; Pareto principle; Mathematics; Optimization problem; Computer science; Engineering","score_opus":0.038000327363495415,"score_gpt":0.2784580856916667,"score_spread":0.2404577583281713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242376211","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":[0.016039982,0.00008866819,0.9830499,0.00009852746,0.00022578964,0.00032057235,0.000004809928,0.00002365867,0.00014804152],"genre_scores_gemma":[0.4723267,0.000005904719,0.52754873,0.00007603964,0.000024614783,0.000001926607,0.0000040959417,0.000010293355,0.0000016966223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99749583,0.0004934332,0.0006705904,0.000254607,0.0009429554,0.00014256166],"domain_scores_gemma":[0.9968174,0.0001553465,0.00071716186,0.0001657277,0.0019927486,0.00015163294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010651684,0.000164877,0.00028945267,0.00039166174,0.00012999112,0.000032962675,0.00037788312,0.000019439452,0.0000075232124],"category_scores_gemma":[0.000113048736,0.0001656419,0.00005853648,0.00057927094,0.0000410751,0.00025609744,0.000089336674,0.00012575217,0.0000057440334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002556321,0.00008522612,0.0000057135194,0.000004247847,0.000028642185,0.000015662028,0.0007852287,0.987936,0.00015811302,0.0015268247,0.000017176939,0.009411564],"study_design_scores_gemma":[0.0017437778,0.00034444517,0.002780015,0.000031267995,0.000027377495,0.00015231935,0.00005060558,0.9929173,0.00023143805,0.0015588222,0.000011191577,0.00015143635],"about_ca_topic_score_codex":3.7624667e-7,"about_ca_topic_score_gemma":2.828731e-7,"teacher_disagreement_score":0.45628673,"about_ca_system_score_codex":0.00011154592,"about_ca_system_score_gemma":0.00024832768,"threshold_uncertainty_score":0.6754681},"labels":[],"label_agreement":null},{"id":"W4246419159","doi":"10.1002/0470011815.b2a15114","title":"<scp>P</scp> areto Distribution","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Biostatistics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Pareto principle; Generalization; Pareto distribution; Mathematics; Gamma distribution; Exponential function; Exponential distribution; Lomax distribution; Distribution (mathematics); Combinatorics; Multivariate statistics; Exponential family; Statistical physics; Applied mathematics; Statistics; Mathematical analysis; Physics","score_opus":0.006047229451271756,"score_gpt":0.24177336418019332,"score_spread":0.23572613472892157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246419159","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":[2.9200226e-7,0.00045565527,0.6399308,0.000022638129,0.0007404693,0.00023842587,0.0015656893,0.00023580735,0.35681027],"genre_scores_gemma":[0.000010014511,0.0024362677,0.5871455,0.00004648605,0.00035941903,0.000015709395,0.0005884432,0.00016623188,0.40923193],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99806637,0.00006551765,0.00044554734,0.0005792893,0.00047049433,0.00037276183],"domain_scores_gemma":[0.9978604,0.00038276886,0.0006743355,0.0007347387,0.0001946544,0.00015312781],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009819645,0.00038169217,0.0004368379,0.00022011626,0.000051586278,0.000040128183,0.0007804647,0.00029498787,0.00018475606],"category_scores_gemma":[0.0009665451,0.0004020566,0.00007569951,0.0005018698,0.00014935706,0.0001437537,0.00023768241,0.00024990665,0.00020490706],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.442554e-7,0.000112474416,0.00004165576,0.00006798426,0.00004664367,0.000027389588,0.0002133274,0.00036360108,0.0000024965989,0.023046117,0.91791457,0.058163125],"study_design_scores_gemma":[0.00031220968,0.000055949047,0.00021501424,0.00008490002,0.000024884845,0.0000072724733,0.000025209649,0.012045772,0.000049004102,0.0005158194,0.9864886,0.00017540583],"about_ca_topic_score_codex":0.000030357935,"about_ca_topic_score_gemma":0.000026018388,"teacher_disagreement_score":0.06857402,"about_ca_system_score_codex":0.00010953242,"about_ca_system_score_gemma":0.00016875858,"threshold_uncertainty_score":0.9998431},"labels":[],"label_agreement":null},{"id":"W4246965515","doi":"10.1115/detc2015-47525","title":"Optimization on Metamodeling-Supported Iterative Decomposition","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"U.S. Department of Energy","keywords":"Metamodeling; Mathematical optimization; Benchmark (surveying); Optimization problem; Computer science; Decomposition; Multi-objective optimization; Algorithm; Mathematics","score_opus":0.0359323896910449,"score_gpt":0.3161928727902139,"score_spread":0.280260483099169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246965515","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":[0.00013108437,0.00001130871,0.9852552,0.0005100336,0.00030466815,0.00023553958,0.0000019869597,0.00040619183,0.013144004],"genre_scores_gemma":[0.067557216,0.000004130258,0.930764,0.0008936011,0.000040143925,0.00002468959,0.000034464843,0.000013394154,0.00066833955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875534,0.00009189158,0.00022845609,0.00041841975,0.00031596795,0.00018991287],"domain_scores_gemma":[0.9987622,0.000051115516,0.00010454058,0.00035839036,0.0005544398,0.00016934804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019138033,0.00015804269,0.00014197093,0.00017771278,0.000093351475,0.00014501455,0.00030849475,0.00005352909,0.00004425128],"category_scores_gemma":[0.000090269874,0.00014090708,0.000040181534,0.00048881536,0.000022245964,0.0012049713,0.00008997848,0.00009325866,0.000113644885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010039829,0.000074000025,0.000007717843,5.96259e-7,0.000010039126,0.0000046246596,0.0003483318,0.98395735,0.00004911021,0.0122391265,0.00026352485,0.0030355174],"study_design_scores_gemma":[0.0006829198,0.00014501707,0.0000136744275,0.0000058115634,0.000004039508,0.000010278763,0.00004209086,0.9935234,0.0038392893,0.0013752625,0.00017989046,0.00017832873],"about_ca_topic_score_codex":0.000005104668,"about_ca_topic_score_gemma":0.0000012637897,"teacher_disagreement_score":0.06742613,"about_ca_system_score_codex":0.00014295506,"about_ca_system_score_gemma":0.000077200304,"threshold_uncertainty_score":0.5746024},"labels":[],"label_agreement":null},{"id":"W4248692800","doi":"10.22215/etd/2019-13663","title":"The Evaluation of Multi-Objective Evolutionary Algorithms for a Maritime Domain Awareness Problem","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fishing; Enforcement; Set (abstract data type); Domain (mathematical analysis); Evolutionary algorithm; Business; Computer science; Fishery; Political science; Artificial intelligence; Mathematics; Biology; Law","score_opus":0.03557473894808107,"score_gpt":0.34251585006560636,"score_spread":0.3069411111175253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248692800","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":[0.00011660716,0.0012091077,0.98752147,0.00009156379,0.0017609328,0.0055285986,0.00009786615,0.00016206363,0.003511807],"genre_scores_gemma":[0.0030211413,0.00011873124,0.9783707,0.0000339522,0.00010256351,0.0026105978,0.0010901779,0.00009000938,0.014562078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958817,0.00038510837,0.00081087317,0.0010649627,0.001367439,0.00048993994],"domain_scores_gemma":[0.9918287,0.0007838264,0.00094681984,0.0009842982,0.005373714,0.00008267835],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017430093,0.00048927515,0.00053402793,0.0003091553,0.0005500856,0.00013506091,0.0012974858,0.00037900088,0.00002423974],"category_scores_gemma":[0.00041420577,0.00039110554,0.00029161584,0.0007241789,0.0000914917,0.00073454424,0.00015031778,0.00029078717,0.000025637864],"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":[0.00045824828,0.0011881064,0.00016001001,0.000822457,0.00096345204,0.0000029035405,0.0075445175,0.15387057,0.0008052138,0.07250044,0.0013659712,0.7603181],"study_design_scores_gemma":[0.0020109967,0.00015077116,0.0018391107,0.0001534837,0.000092722825,0.000005780326,0.0014594491,0.9776993,0.001267623,0.014230521,0.00057135936,0.0005189278],"about_ca_topic_score_codex":0.00007698731,"about_ca_topic_score_gemma":0.00024085198,"teacher_disagreement_score":0.8238287,"about_ca_system_score_codex":0.00062240555,"about_ca_system_score_gemma":0.0018477094,"threshold_uncertainty_score":0.9998541},"labels":[],"label_agreement":null},{"id":"W4248816089","doi":"10.22215/etd/2018-13493","title":"High Fidelity and Efficient Computations of Dynamic Loads for Multidisciplinary Design Optimization of Flexible Transport Aircraft","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; York University","funders":"","keywords":"Metamodeling; Kriging; Airframe; Multidisciplinary design optimization; Reduction (mathematics); Computer science; Mathematical optimization; Modal; Process (computing); High fidelity; Set (abstract data type); Surrogate model; Computation; Engineering; Algorithm; Aerospace engineering; Multidisciplinary approach; Mathematics","score_opus":0.01508972893667291,"score_gpt":0.29727930398372315,"score_spread":0.2821895750470502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248816089","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":[0.002767993,0.00011720013,0.99455893,0.000029694798,0.00060277997,0.001564749,0.00010546277,0.00013839758,0.00011479195],"genre_scores_gemma":[0.06533793,0.000033495653,0.9324985,0.000008279579,0.000015604899,0.00011485039,0.0009028661,0.000038257895,0.0010502018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778765,0.00006472582,0.00085215736,0.0007163085,0.00033921332,0.0002399476],"domain_scores_gemma":[0.99694955,0.00028092548,0.00071058806,0.00046524592,0.0015060814,0.000087613145],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032444973,0.0003269987,0.00054804253,0.00039012352,0.00017026198,0.000020568927,0.00043026218,0.00023299913,0.00002636792],"category_scores_gemma":[0.000059509046,0.00033186117,0.00013031505,0.00058826146,0.00012954675,0.00027099389,0.000051111645,0.00010626686,0.0000012130104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010358477,0.00024320875,0.000008432816,0.00022350338,0.000052654683,6.086272e-7,0.001599726,0.99202144,0.00020668196,0.0015192373,0.000016468766,0.0040044403],"study_design_scores_gemma":[0.00096837693,0.0003077072,0.0018899939,0.00012496351,0.00006296706,0.0000018792276,0.00030750452,0.9900075,0.0052025076,0.0008215534,0.0000022391177,0.0003027894],"about_ca_topic_score_codex":0.000050105537,"about_ca_topic_score_gemma":0.000025727952,"teacher_disagreement_score":0.06256994,"about_ca_system_score_codex":0.00008474366,"about_ca_system_score_gemma":0.00026699333,"threshold_uncertainty_score":0.99991333},"labels":[],"label_agreement":null},{"id":"W4249469179","doi":"10.23952/jnva.5.2021.6.02","title":"An iteratively regularized stochastic gradient method for estimating a random parameter in a stochastic PDE. A variational inequality approach","year":2021,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universidad Nacional de Educación a Distancia; Ministerio de Ciencia e Innovación; Agencia Estatal de Investigación; National Science Foundation","keywords":"Mathematics; Applied mathematics; Variational inequality; Mathematical optimization; Inequality; Mathematical analysis","score_opus":0.026457147458199107,"score_gpt":0.33167249065134585,"score_spread":0.30521534319314675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249469179","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":[0.0011607726,0.000046580728,0.9980093,0.00039351144,0.00010456139,0.00020345161,0.00006787339,0.000010978134,0.0000029608914],"genre_scores_gemma":[0.042802658,0.00000138588,0.9566828,0.00014646185,0.00018365716,0.000029034341,0.0001284456,0.000009427192,0.000016112584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973854,0.000538758,0.0009273426,0.0004339968,0.0005134951,0.00020102682],"domain_scores_gemma":[0.99568915,0.0018059427,0.00075368705,0.00021023312,0.0013915704,0.0001494259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019981817,0.00019019876,0.00065681915,0.0006222059,0.00015905012,0.00021423219,0.00024804534,0.000076886936,0.000014818321],"category_scores_gemma":[0.0020412905,0.00016626957,0.00028211914,0.0015451276,0.000026117417,0.0008011893,0.000065483,0.00021755866,2.0530585e-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":[0.00014577401,0.00046845025,0.000104007755,0.000009442873,0.0006467305,0.0000061364863,0.0011986393,0.9893891,0.00015205954,0.0059869452,0.0000011438402,0.0018915228],"study_design_scores_gemma":[0.003732653,0.00010036133,0.0034767783,0.000017469541,0.00039802506,0.000074426556,0.00006650633,0.98058605,0.000010026094,0.011361492,0.0000019135289,0.00017432337],"about_ca_topic_score_codex":0.0000142267245,"about_ca_topic_score_gemma":0.000011486324,"teacher_disagreement_score":0.041641887,"about_ca_system_score_codex":0.000116837684,"about_ca_system_score_gemma":0.0003404189,"threshold_uncertainty_score":0.67802763},"labels":[],"label_agreement":null},{"id":"W4249796148","doi":"10.1115/detc2004-57194","title":"An Efficient Pareto Set Identification Approach for Multi-Objective Optimization on Black-Box Functions","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mathematical optimization; Pareto principle; Computer science; Robustness (evolution); Black box; Multi-objective optimization; Set (abstract data type); Computation; Identification (biology); Convergence (economics); Algorithm; Mathematics; Artificial intelligence","score_opus":0.033186627233410836,"score_gpt":0.3035175838474078,"score_spread":0.270330956613997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249796148","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":[0.00036406904,0.0000067715473,0.99615806,0.00012912112,0.00038741072,0.0015915585,0.000042804084,0.0006378636,0.00068235584],"genre_scores_gemma":[0.16610837,0.00000380864,0.83255345,0.00019246228,0.000065662476,0.00034954614,0.00025886754,0.00003418103,0.00043365854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977318,0.00008581003,0.00040032977,0.0010574101,0.00035534654,0.00036931486],"domain_scores_gemma":[0.9979816,0.00006875319,0.0002259698,0.00086993154,0.0006813274,0.0001723845],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003225623,0.00028000478,0.00020296904,0.0003186946,0.00046541533,0.0002579566,0.00056898745,0.000120894496,0.0000101715395],"category_scores_gemma":[0.0001555763,0.0002712777,0.000100808815,0.00089403027,0.00009762395,0.00087794306,0.00007019703,0.00014072374,0.00005096722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021097554,0.00074575236,0.000009954624,0.000007058018,0.00001757372,5.06028e-7,0.0010519337,0.9841736,0.0002015531,0.012153262,0.000028461052,0.0015892334],"study_design_scores_gemma":[0.0017791351,0.00023274065,0.0003469498,0.000007445204,0.000013116456,0.0000049062487,0.0006611958,0.99207175,0.0043238737,0.00019553283,0.00002780024,0.0003355471],"about_ca_topic_score_codex":0.000018644354,"about_ca_topic_score_gemma":0.0000073184574,"teacher_disagreement_score":0.1657443,"about_ca_system_score_codex":0.0004481707,"about_ca_system_score_gemma":0.00011794566,"threshold_uncertainty_score":0.99997395},"labels":[],"label_agreement":null},{"id":"W4251911158","doi":"10.32920/14640045","title":"Wireless Sensor Network Optimization: Multi-Objective Paradigm","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Optimization problem; Computer science; Software deployment; Key distribution in wireless sensor networks; Wireless network; Wireless; Distributed computing; Mathematical optimization; Computer network; Telecommunications; Mathematics","score_opus":0.022769217665029595,"score_gpt":0.2732898342041067,"score_spread":0.2505206165390771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251911158","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":[0.000044094093,0.00049951975,0.99044865,0.00062672084,0.0034001167,0.0010654242,0.000015928123,0.001196369,0.0027031843],"genre_scores_gemma":[0.0040312074,0.0004604646,0.9915024,0.0007832674,0.0005249425,0.00023334249,0.00016515497,0.00009870232,0.0022005392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950546,0.0004175672,0.00078274525,0.002265019,0.0006410704,0.0008389716],"domain_scores_gemma":[0.99568844,0.00026804977,0.00060197536,0.0021925191,0.0009270243,0.00032198353],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032131118,0.000817764,0.0009081998,0.00022061396,0.00037422837,0.00093361875,0.0017687766,0.0006124915,0.00020405263],"category_scores_gemma":[0.00013312204,0.0008618492,0.0003413844,0.001194642,0.00014144025,0.00088385586,0.0036115455,0.0011275592,0.000049096718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057696698,0.00016770068,0.000086295,0.000032558564,0.00013968677,0.000110088695,0.00062253524,0.9914246,0.000004368874,0.003641685,0.00018427419,0.0035803886],"study_design_scores_gemma":[0.00075527007,0.00002676353,0.00024614992,0.00014096187,0.00002784963,0.000054420303,0.00013757592,0.99655783,0.000315599,0.0005289558,0.00020773915,0.0010009034],"about_ca_topic_score_codex":0.00006267702,"about_ca_topic_score_gemma":0.000037579226,"teacher_disagreement_score":0.0051331627,"about_ca_system_score_codex":0.00044450583,"about_ca_system_score_gemma":0.0006884471,"threshold_uncertainty_score":0.9993832},"labels":[],"label_agreement":null},{"id":"W4252074383","doi":"10.1109/ccece.2018.8447703","title":"IIR Filter Design Using Multiobjective Teaching-Learning-Based Optimization","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Passband; Stopband; Infinite impulse response; Chebyshev filter; Minimax; Finite impulse response; Computer science; Minification; Low-pass filter; 2D Filters; High-pass filter; Filter (signal processing); Digital filter; Filter design; Control theory (sociology); Mathematical optimization; Algorithm; Mathematics; Band-pass filter; Artificial intelligence; Electronic engineering; Engineering; Computer vision","score_opus":0.03145083055524149,"score_gpt":0.2945292099536109,"score_spread":0.2630783793983694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252074383","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":[0.00007462738,0.0000067239675,0.9959431,0.00010136169,0.0003343269,0.00041293408,9.086943e-7,0.00067677046,0.0024492296],"genre_scores_gemma":[0.07611224,9.266761e-7,0.9224275,0.00050293707,0.00013239718,0.000018895227,0.0000048613606,0.000032483564,0.000767783],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798816,0.00041834236,0.00026757963,0.0006515007,0.00030247288,0.00037196925],"domain_scores_gemma":[0.99845934,0.00023821308,0.00019023832,0.0004484885,0.000544627,0.000119078795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004379433,0.00024396661,0.00018512837,0.00026310538,0.00061341043,0.00019600826,0.00049868214,0.000098740864,0.00023691179],"category_scores_gemma":[0.0004675609,0.0002320164,0.000063133644,0.00052732095,0.00012771209,0.0011185575,0.00016492812,0.0002711326,0.00007560836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014507029,0.000075021344,0.00006117257,0.0000017938913,0.00001162549,0.0000030287242,0.000563481,0.9934989,0.0003317632,0.0004332735,0.000048484155,0.0049569146],"study_design_scores_gemma":[0.0007996657,0.00022364064,0.00006813823,0.000015531472,0.0000068066447,0.000008925547,0.000037939673,0.9897959,0.008324338,0.00012268861,0.00028747725,0.0003089374],"about_ca_topic_score_codex":0.000033926673,"about_ca_topic_score_gemma":0.0000025116074,"teacher_disagreement_score":0.076037616,"about_ca_system_score_codex":0.00021997174,"about_ca_system_score_gemma":0.0001449896,"threshold_uncertainty_score":0.94613546},"labels":[],"label_agreement":null},{"id":"W4255052596","doi":"10.22215/etd/2011-09468","title":"Improved placement of local solver launch points for large-scale global optimization","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Solver; Scale (ratio); Computer science; Aerospace engineering; Geography; Engineering; Cartography; Programming language","score_opus":0.0107878867576591,"score_gpt":0.2734182411692607,"score_spread":0.2626303544116016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255052596","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":[0.000024219133,0.00006320434,0.9912314,0.00001859986,0.0011570203,0.0011972908,0.00012680881,0.00016742559,0.006014042],"genre_scores_gemma":[0.0011725014,0.000028048262,0.98799163,0.0001114417,0.000047371053,0.0001911389,0.0009887618,0.00004038177,0.009428718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99781877,0.000040062467,0.00059940835,0.00077970023,0.00034390046,0.00041816555],"domain_scores_gemma":[0.99763453,0.000049087503,0.0005613723,0.00058482285,0.0010494472,0.00012072058],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022104318,0.00037575347,0.00044020815,0.00013312847,0.00011614605,0.000051402487,0.0007188876,0.00032612326,0.00021092044],"category_scores_gemma":[0.000051271523,0.00036978966,0.00018384852,0.00040403826,0.00002871678,0.000522203,0.00012563802,0.0001246794,0.000010464847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012975752,0.0026275043,0.00010136133,0.0010437185,0.00058325956,0.000005530238,0.0047406205,0.84698546,0.00032503426,0.07037061,0.0029804027,0.0689389],"study_design_scores_gemma":[0.001762663,0.00020676834,0.00006266447,0.000047247664,0.0000334731,0.0000015916011,0.0006477258,0.9899475,0.0058829584,0.00066589215,0.00033600724,0.0004054885],"about_ca_topic_score_codex":0.000038290684,"about_ca_topic_score_gemma":0.0002663409,"teacher_disagreement_score":0.14296204,"about_ca_system_score_codex":0.00021901635,"about_ca_system_score_gemma":0.00026415478,"threshold_uncertainty_score":0.9998754},"labels":[],"label_agreement":null},{"id":"W425598545","doi":"","title":"Optimal Advertising Campaign Generation For Multiple Brands Using Multi-Objective Genetic Algorithm","year":2005,"lang":"en","type":"article","venue":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Greedy algorithm; Genetic algorithm; Mathematical optimization; Heuristic; Computer science; Variety (cybernetics); Pareto optimal; Set (abstract data type); Encoding (memory); Key (lock); Optimization problem; Pareto principle; Algorithm; Multi-objective optimization; Greedy randomized adaptive search procedure; Mathematics; Artificial intelligence","score_opus":0.05859566639916588,"score_gpt":0.28887644868629303,"score_spread":0.23028078228712714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W425598545","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":[0.0917247,0.00017128648,0.9060938,0.0007269523,0.000080083555,0.0008040734,0.00021385864,0.00006927963,0.00011601219],"genre_scores_gemma":[0.21663085,0.0003435524,0.7815539,0.00001280035,0.000061403916,3.316804e-8,0.000043646178,0.00001708891,0.0013367309],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971728,0.00047568808,0.00024159196,0.0007447904,0.0007643091,0.0006008749],"domain_scores_gemma":[0.99625945,0.00053648395,0.00045226823,0.0007725145,0.0017448475,0.00023442841],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0008064318,0.00027948545,0.00054960395,0.00079293194,0.0015317625,0.000025845338,0.002091455,0.00029197478,0.00009765101],"category_scores_gemma":[0.00010983162,0.00036227092,0.00036230957,0.001313427,0.0012156698,0.0011654558,0.0010585147,0.00055964454,0.0000058719443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008644995,0.0015024153,0.001158246,0.00015700488,0.00051783706,0.000080327314,0.042976588,0.82933736,0.013061986,0.000332046,0.0005992042,0.10941248],"study_design_scores_gemma":[0.004490467,0.00034557722,0.0023654788,0.00010417098,0.000095881376,0.000012546955,0.028606212,0.9607597,0.00081136497,0.000017884815,0.0020582792,0.00033243676],"about_ca_topic_score_codex":0.0026029178,"about_ca_topic_score_gemma":0.0023570836,"teacher_disagreement_score":0.13142233,"about_ca_system_score_codex":0.00048657856,"about_ca_system_score_gemma":0.00048786867,"threshold_uncertainty_score":0.99988294},"labels":[],"label_agreement":null},{"id":"W4280531198","doi":"10.1007/s40747-022-00759-w","title":"Machine learning-based framework to cover optimal Pareto-front in many-objective optimization","year":2022,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Multi-objective optimization; Mathematical optimization; Heuristic; Computer science; Latin hypercube sampling; Optimization problem; Pareto principle; Population; Set (abstract data type); Set cover problem; Machine learning; Artificial intelligence; Mathematics; Monte Carlo method","score_opus":0.027320001216155188,"score_gpt":0.2797006613151728,"score_spread":0.2523806600990176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280531198","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":[0.00023449349,0.00036907365,0.99500495,0.00026026205,0.0014653571,0.001544763,0.000037705453,0.0003834253,0.0006999852],"genre_scores_gemma":[0.5888915,0.000016387574,0.40817043,0.00065914297,0.00011763633,0.00082769565,0.00013594949,0.000080319696,0.0011009345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959459,0.0007636353,0.0007766862,0.0010584365,0.0008309475,0.0006244239],"domain_scores_gemma":[0.9979291,0.00037118306,0.00035560143,0.0008048853,0.000304736,0.00023447345],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006754316,0.00040759516,0.0005282618,0.00064293016,0.00051804876,0.00024430835,0.0013181823,0.000092512746,0.0005957697],"category_scores_gemma":[0.00031165374,0.00045712967,0.0001313887,0.0014427636,0.000053226886,0.00033939557,0.00079098967,0.00072543544,0.00021292435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065982495,0.00021881596,0.0010285741,0.000019732726,0.000026230817,0.000039039493,0.0019282523,0.9920592,0.00001938923,0.0035727809,0.00028107758,0.00074097613],"study_design_scores_gemma":[0.00043811416,0.00038366788,0.00030313362,0.000059221424,0.000006073341,0.000025605797,0.0005521648,0.98235834,0.00012535062,0.000098015036,0.015152903,0.0004973874],"about_ca_topic_score_codex":0.00025040345,"about_ca_topic_score_gemma":0.000016144075,"teacher_disagreement_score":0.588657,"about_ca_system_score_codex":0.0014054204,"about_ca_system_score_gemma":0.00014777663,"threshold_uncertainty_score":0.99978805},"labels":[],"label_agreement":null},{"id":"W4280532181","doi":"10.18280/isi.270205","title":"An Intelligent Multi-Objective Evolutionary Model for Establishing Security in Cyber-Physical Systems","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Evolutionary algorithm; Computer science; Scheduling (production processes); Process (computing); Element (criminal law); MATLAB; Mathematical optimization; Optimization problem; Distributed computing; Artificial intelligence; Mathematics; Algorithm","score_opus":0.020095358873116365,"score_gpt":0.27182224639085156,"score_spread":0.2517268875177352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280532181","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":[0.015072141,0.00007120258,0.9822907,0.000020483894,0.0005407903,0.0012845452,0.00013079344,0.00030242442,0.00028695463],"genre_scores_gemma":[0.8392297,0.000004944605,0.15936989,0.00008331616,0.000046625155,0.0010470361,0.00017208671,0.000017203958,0.000029214243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787974,0.00020683055,0.0006367524,0.00037012802,0.00047464622,0.00043188725],"domain_scores_gemma":[0.9983662,0.00016909731,0.00038878198,0.00045716876,0.0005024948,0.00011627812],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006872545,0.00024074152,0.00028648213,0.00053432264,0.00067073345,0.00038693816,0.00076894794,0.0000691628,0.0000027535025],"category_scores_gemma":[0.00028949176,0.0002749923,0.000086785076,0.0009578406,0.00007456531,0.010606736,0.0003595007,0.0003191718,0.000006915358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003097705,0.00018603296,0.00014543156,0.000056277204,0.000011538604,0.0000015719723,0.03740849,0.9321852,0.000028709768,0.020958886,0.000037899063,0.008949018],"study_design_scores_gemma":[0.0006775479,0.00014751208,0.000383079,0.000027453973,0.0000046349987,0.000026990401,0.004425823,0.9842607,0.000087910055,0.009463135,0.00018214888,0.00031306414],"about_ca_topic_score_codex":0.00017121006,"about_ca_topic_score_gemma":0.00001858275,"teacher_disagreement_score":0.82415754,"about_ca_system_score_codex":0.0020232825,"about_ca_system_score_gemma":0.00025726788,"threshold_uncertainty_score":0.9999702},"labels":[],"label_agreement":null},{"id":"W4281289910","doi":"10.1115/1.4054631","title":"Reinforcement Learning-Based Sequential Batch-Sampling for Bayesian Optimal Experimental Design","year":2022,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Computer science; Leverage (statistics); Bayesian optimization; Artificial intelligence; Bayesian probability; Time budget; Machine learning; Suite","score_opus":0.06422167137727451,"score_gpt":0.3170696949748516,"score_spread":0.2528480235975771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281289910","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":[0.000026173739,0.00008398646,0.9980997,0.00029059153,0.00080368517,0.0006164431,0.0000013476184,0.000067688416,0.000010383902],"genre_scores_gemma":[0.2074425,0.00000325584,0.7919686,0.00027003806,0.00011959129,0.0000848118,0.0000026858029,0.000028804603,0.00007975068],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99703294,0.00062325445,0.0007435114,0.0003548933,0.00083719974,0.00040818643],"domain_scores_gemma":[0.9978181,0.00069405005,0.00070851424,0.00024637248,0.00030407694,0.0002288504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00218923,0.00022558997,0.00035698953,0.00024217454,0.0005525864,0.00012823753,0.00096981047,0.00006330623,0.00017608877],"category_scores_gemma":[0.00033068925,0.00022421473,0.00026335323,0.00035073265,0.000026721873,0.00050413737,0.00025374073,0.0005129034,0.000002525966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044764183,0.00018004513,1.9430385e-7,0.0000037289765,0.000041713723,0.000051136984,0.00017321756,0.9778241,0.017362732,0.0018635233,0.00018570993,0.001866239],"study_design_scores_gemma":[0.002342265,0.0034773368,3.0324057e-7,0.000013789422,0.000019007226,0.00012985768,0.00014494172,0.891776,0.100364015,0.0007481099,0.0007597041,0.00022466149],"about_ca_topic_score_codex":0.0000011440211,"about_ca_topic_score_gemma":3.3680347e-8,"teacher_disagreement_score":0.20741633,"about_ca_system_score_codex":0.0005668037,"about_ca_system_score_gemma":0.00042998293,"threshold_uncertainty_score":0.9143211},"labels":[],"label_agreement":null},{"id":"W4283272765","doi":"10.2514/6.2022-4037","title":"A mixed-categorical data-driven approach for prediction and optimization of hybrid discontinuous composites performance","year":2022,"lang":"en","type":"article","venue":"AIAA AVIATION 2022 Forum","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Polytechnique Montréal","funders":"","keywords":"Categorical variable; Surrogate model; Interpolation (computer graphics); Solver; Computer science; Bayesian optimization; Mathematical optimization; Process (computing); Optimization problem; Algorithm; Machine learning; Artificial intelligence; Mathematics","score_opus":0.020506587431729967,"score_gpt":0.2370151774739157,"score_spread":0.21650859004218573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283272765","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":[0.00178024,0.00009076938,0.99615777,0.0002998877,0.0003191561,0.00072201615,0.00043631124,0.00011963971,0.000074237396],"genre_scores_gemma":[0.4259365,0.00003809631,0.571228,0.00006821796,0.0000313859,0.00024303584,0.002323626,0.00001804898,0.00011305376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984245,0.00009153901,0.0003596522,0.00055135216,0.00035038876,0.00022260418],"domain_scores_gemma":[0.9988419,0.00009116246,0.0003237462,0.0005267286,0.00016182223,0.000054639837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029442238,0.00014069652,0.00019712675,0.0001714963,0.00048526918,0.0000578673,0.00061158667,0.000023387254,0.000012281379],"category_scores_gemma":[0.00005797947,0.00015296625,0.00003712431,0.0004514794,0.000046333553,0.0013150816,0.000849585,0.00013199457,5.77404e-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":[0.000029121436,0.00015410193,0.0016300367,0.000021570339,0.000022057471,4.974659e-7,0.00014188678,0.985614,0.00013098968,0.0033523676,0.00058229273,0.008321093],"study_design_scores_gemma":[0.0008038582,0.00024976037,0.0008888845,0.0000024481772,0.000017823195,0.000035347308,0.00016778533,0.99667346,0.00037231104,0.00021444361,0.00042705392,0.00014684715],"about_ca_topic_score_codex":0.0000050480667,"about_ca_topic_score_gemma":5.8727886e-7,"teacher_disagreement_score":0.42492968,"about_ca_system_score_codex":0.00010812265,"about_ca_system_score_gemma":0.00005360327,"threshold_uncertainty_score":0.6237783},"labels":[],"label_agreement":null},{"id":"W4283692374","doi":"10.1016/j.cma.2022.115223","title":"An effective multi-objective artificial hummingbird algorithm with dynamic elimination-based crowding distance for solving engineering design problems","year":2022,"lang":"en","type":"article","venue":"Computer Methods in Applied Mechanics and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":149,"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":"Mathematical optimization; Sorting; Benchmark (surveying); Computer science; Convergence (economics); Multi-objective optimization; Pareto principle; Algorithm; Hummingbird; Search-based software engineering; Metaheuristic; Population; Mathematics; Software; Software design; Software development","score_opus":0.01611159263480726,"score_gpt":0.28487867853895754,"score_spread":0.26876708590415027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283692374","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":[0.00004457434,0.00009969966,0.99711263,0.000014099725,0.00051354803,0.0018044381,0.000010008701,0.000399674,0.0000013459437],"genre_scores_gemma":[0.04712197,0.000003817773,0.95082754,0.000034162964,0.00004929352,0.0018613861,0.000012078733,0.0000884703,0.000001272352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977652,0.00010968168,0.00038404186,0.0009482748,0.00024854735,0.00054425764],"domain_scores_gemma":[0.9982787,0.00093808887,0.00016393082,0.0003821974,0.0001098308,0.0001272932],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015735102,0.0004024009,0.00042191683,0.00050989306,0.00035111277,0.00018616638,0.0005055654,0.0000789508,9.3228147e-7],"category_scores_gemma":[0.00005555437,0.00045643945,0.000052623873,0.0008843871,0.000012544317,0.0003069586,0.00024208812,0.00043526702,1.5713127e-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":[0.000016287602,0.000074097756,4.784784e-7,0.00004988618,0.000019928928,0.000005144315,0.000715026,0.7486404,0.011113846,0.013541511,1.2447772e-7,0.22582322],"study_design_scores_gemma":[0.0011195877,0.00032534794,0.00003438636,0.000055636945,0.000013269004,0.000011351884,0.0000837896,0.99050665,0.0059734643,0.0012690204,0.000042840074,0.00056464475],"about_ca_topic_score_codex":0.0000028556535,"about_ca_topic_score_gemma":0.0000017761618,"teacher_disagreement_score":0.24186622,"about_ca_system_score_codex":0.00049845205,"about_ca_system_score_gemma":0.000053283093,"threshold_uncertainty_score":0.99978876},"labels":[],"label_agreement":null},{"id":"W4283835456","doi":"10.5281/zenodo.6801308","title":"Regularized Infill Criteria for Multi-objective Bayesian Optimization with Application to Aircraft Design","year":2022,"lang":"en","type":"paratext","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Horizon 2020 Framework Programme; Office National d'études et de Recherches Aérospatiales; European Commission","keywords":"Bayesian optimization; Infill; Computer science; Bayesian probability; Engineering; Artificial intelligence; Structural engineering","score_opus":0.03771061288443587,"score_gpt":0.29096153915103806,"score_spread":0.25325092626660217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283835456","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":[0.0000010056037,0.00005355229,0.98049724,0.00051009294,0.00042818618,0.0052097198,0.00081008393,0.0010905341,0.011399598],"genre_scores_gemma":[0.0002783858,0.000080603786,0.9733851,0.00045380567,0.00023864853,0.00001854168,0.008728793,0.0027549008,0.014061235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9950728,0.0010006519,0.0006040513,0.0018039994,0.0007719649,0.00074657094],"domain_scores_gemma":[0.9950062,0.00011743791,0.0005494522,0.0014511668,0.0024935973,0.0003821322],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0010217205,0.0005673486,0.0005405988,0.0009551934,0.0036758925,0.0015194932,0.003093042,0.00023147618,0.007828141],"category_scores_gemma":[0.00070803927,0.00062435586,0.000118547985,0.002231538,0.00014890736,0.00091317913,0.002249117,0.0005706462,0.003481283],"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":[0.00038834952,0.00033555497,6.5535545e-8,0.00011418367,0.0001388356,0.000007433851,0.0018224679,0.7928529,0.0007919231,0.0010366074,0.15932082,0.043190908],"study_design_scores_gemma":[0.001383503,0.00063946255,0.0000042273,0.0000406305,0.00002384468,0.000060392595,0.00010925525,0.49397352,0.00035334725,0.000038647933,0.5028326,0.0005405576],"about_ca_topic_score_codex":0.000016782527,"about_ca_topic_score_gemma":4.5783523e-7,"teacher_disagreement_score":0.3435118,"about_ca_system_score_codex":0.001052001,"about_ca_system_score_gemma":0.000057180496,"threshold_uncertainty_score":0.9996208},"labels":[],"label_agreement":null},{"id":"W4285288632","doi":"10.1109/tmtt.2022.3181127","title":"A Novel Surrogate-Based Approach to Yield Estimation and Optimization of Microwave Structures Using Combined Quadratic Mappings and Matrix Transfer Functions","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Surrogate model; Mathematical optimization; Quadratic equation; Transfer function; Yield (engineering); Set (abstract data type); Matrix (chemical analysis); Function (biology); Optimization problem; Algorithm; Computer science; Quadratic programming; Mathematics; Engineering","score_opus":0.018153227364804667,"score_gpt":0.25231180198815306,"score_spread":0.23415857462334838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285288632","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":[0.011497814,0.00004383915,0.9872558,0.00007236418,0.000068804424,0.000759815,0.00006687985,0.00019346351,0.00004120094],"genre_scores_gemma":[0.5055163,0.000006941739,0.4942762,0.00008842208,0.0000028372963,0.000063507876,0.000004856733,0.00001415488,0.000026813193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987449,0.00017817448,0.00030184624,0.00045150094,0.00015434394,0.00016923649],"domain_scores_gemma":[0.9992222,0.00026403455,0.00008617455,0.0002406895,0.00010634872,0.00008055354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000411865,0.000220895,0.0002432163,0.00045943022,0.00051659474,0.00007584526,0.0001470985,0.00007548872,0.0000141378305],"category_scores_gemma":[0.000014436048,0.00023583266,0.00004791631,0.0004675764,0.00015083795,0.0003260698,0.000012117306,0.00022862159,1.3324716e-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":[0.00018704985,0.0001960452,0.0000012637867,0.000053360138,0.000029633464,6.448324e-7,0.0009032586,0.6427515,0.33681774,0.008004597,0.000002018678,0.011052878],"study_design_scores_gemma":[0.00054993056,0.00036473785,0.000004455686,0.00004314482,0.00005577834,0.0000873082,0.00032103102,0.5534585,0.44172263,0.003109392,0.000005891096,0.0002771621],"about_ca_topic_score_codex":0.000023382927,"about_ca_topic_score_gemma":0.00000443558,"teacher_disagreement_score":0.49401847,"about_ca_system_score_codex":0.00006782911,"about_ca_system_score_gemma":0.000052401345,"threshold_uncertainty_score":0.96169764},"labels":[],"label_agreement":null},{"id":"W4286569388","doi":"10.1109/compel53829.2022.9829984","title":"Multi-Objective Optimization of EE-core Transformers using Geometric Programming","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Xerox (Canada)","funders":"","keywords":"Geometric programming; Mathematical optimization; Transformer; Monomial; Computer science; Multi-objective optimization; Pareto optimal; Optimality criterion; Mathematics; Engineering; Discrete mathematics","score_opus":0.052099471536464236,"score_gpt":0.31889171392387183,"score_spread":0.2667922423874076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286569388","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":[0.0003236111,0.0002474933,0.995288,0.000037533948,0.0010629921,0.0018520124,0.000043214884,0.00044765658,0.0006974786],"genre_scores_gemma":[0.016430328,0.00012563948,0.9827283,0.000049075603,0.000038740312,0.00019499777,0.00009569458,0.000072765615,0.0002644385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99628335,0.0001464676,0.00085272454,0.001333797,0.0008402384,0.0005434166],"domain_scores_gemma":[0.9972546,0.0001613351,0.00083703746,0.00084923534,0.000744653,0.00015313238],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004852694,0.0005411799,0.00068071036,0.001730571,0.00029627726,0.00014298869,0.0014021454,0.00028000452,0.00020363588],"category_scores_gemma":[0.00020784745,0.00058258546,0.00032989765,0.003641025,0.00012810157,0.0007527532,0.0016095367,0.0008493612,0.0000020447528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010844537,0.00025321275,0.00015006856,0.000095219926,0.0000959221,0.000008938447,0.0009082622,0.9554065,0.0000644524,0.0002253734,0.0000022915217,0.042778935],"study_design_scores_gemma":[0.0008421205,0.00009696288,0.0001719706,0.000052080417,0.000045589895,0.000015089173,0.00042514314,0.9967498,0.000826526,0.00009597851,0.00008238901,0.0005963268],"about_ca_topic_score_codex":0.00024077002,"about_ca_topic_score_gemma":0.00000871128,"teacher_disagreement_score":0.04218261,"about_ca_system_score_codex":0.0007837203,"about_ca_system_score_gemma":0.0005253226,"threshold_uncertainty_score":0.9996626},"labels":[],"label_agreement":null},{"id":"W4288828694","doi":"10.1016/b978-0-323-85159-6.50284-0","title":"Adaptive least-squares surrogate modeling for reaction systems","year":2022,"lang":"en","type":"book-chapter","venue":"Computer-aided chemical engineering/Computer aided chemical engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Latin hypercube sampling; Mathematical optimization; Surrogate model; Convergence (economics); Sampling (signal processing); Computer science; Adaptive sampling; Optimization problem; Least-squares function approximation; Function (biology); Algorithm; Mathematics; Statistics","score_opus":0.017931534410239914,"score_gpt":0.2089007512247176,"score_spread":0.19096921681447768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288828694","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":[0.00015327217,0.0006785913,0.9889376,0.00008455468,0.004693463,0.0015934736,0.00013512207,0.0032631417,0.0004607643],"genre_scores_gemma":[0.011472076,0.000083361854,0.9799324,0.00014342091,0.0046579507,0.00069328997,0.00070890365,0.0008897299,0.0014188283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9918778,0.000032673313,0.0020263128,0.0030273816,0.0013035176,0.0017323158],"domain_scores_gemma":[0.9949703,0.00095225783,0.00063706114,0.0018673574,0.0006929104,0.00088009913],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00042848845,0.0022120061,0.0021908735,0.0009048419,0.00021229952,0.000456946,0.002781188,0.0011113259,0.000026827156],"category_scores_gemma":[0.00013050844,0.0027023014,0.0010507576,0.00050530024,0.000082666586,0.0011606275,0.0023099235,0.0025883853,0.000036826743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043705026,0.00008619928,7.293389e-8,0.0004978106,0.00042243398,0.000077482364,0.00017552648,0.9415335,0.014010304,0.03681251,0.00027572227,0.0060647293],"study_design_scores_gemma":[0.0017674275,0.00020689578,2.0612886e-7,0.0006850767,0.000113789145,0.00028334215,0.0000030773663,0.97887796,0.005369142,0.0005610144,0.009538459,0.0025935818],"about_ca_topic_score_codex":0.000014559995,"about_ca_topic_score_gemma":1.3398832e-7,"teacher_disagreement_score":0.037344486,"about_ca_system_score_codex":0.0020059668,"about_ca_system_score_gemma":0.00017548498,"threshold_uncertainty_score":0.9997127},"labels":[],"label_agreement":null},{"id":"W4289870909","doi":"10.1287/stsy.2022.0096","title":"Convergence Rates of Epsilon-Greedy Global Optimization Under Radial Basis Function Interpolation","year":2022,"lang":"en","type":"article","venue":"Stochastic Systems","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Rate of convergence; Interpolation (computer graphics); Convergence (economics); Mathematical optimization; Sampling (signal processing); Function (biology); Applied mathematics; Basis (linear algebra); Global optimization; Space (punctuation); Computer science; Filter (signal processing); Key (lock)","score_opus":0.018187653732222572,"score_gpt":0.26487037483204023,"score_spread":0.24668272109981765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289870909","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":[0.00032856336,0.0002544688,0.9944026,0.00007064966,0.0039997157,0.00055184733,0.000036882448,0.00014348792,0.00021176829],"genre_scores_gemma":[0.9175052,0.0000024953033,0.08213161,0.00004822069,0.00006891247,0.000093767034,0.000038339076,0.000014779398,0.00009669316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821323,0.00019555536,0.00041652346,0.00044738382,0.00050434464,0.00022293736],"domain_scores_gemma":[0.9987464,0.000112150636,0.00040544613,0.00038084766,0.00027619692,0.000078952704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030658668,0.00016940758,0.00023771031,0.00012567604,0.0002981753,0.00006183357,0.0004418601,0.00004795549,0.00016779195],"category_scores_gemma":[0.00008613105,0.0001935502,0.00006424778,0.00085171027,0.00006351042,0.00047819846,0.00024340319,0.00010664419,0.000018696745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037415124,0.00004574321,0.0001688539,0.0000114074055,0.000029557743,0.0000011831642,0.0002929244,0.98924965,0.00008327637,0.009312882,0.00009552505,0.000671567],"study_design_scores_gemma":[0.00060792564,0.00019792657,0.0005581179,0.000020454949,0.000015755197,0.00003909654,0.0007592198,0.99700856,0.000008421564,0.00058308087,0.000021180393,0.00018024757],"about_ca_topic_score_codex":0.00020394672,"about_ca_topic_score_gemma":0.000006321898,"teacher_disagreement_score":0.9171766,"about_ca_system_score_codex":0.00031603326,"about_ca_system_score_gemma":0.00013190426,"threshold_uncertainty_score":0.7892748},"labels":[],"label_agreement":null},{"id":"W4290466112","doi":"10.3390/a15080279","title":"High-Fidelity Surrogate Based Multi-Objective Optimization Algorithm","year":2022,"lang":"en","type":"article","venue":"Algorithms","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Pareto principle; Surrogate model; Mathematical optimization; Multi-objective optimization; Engineering design process; Benchmark (surveying); Computer science; Weighting; Evolutionary algorithm; Algorithm; Optimization problem; Mathematics; Engineering","score_opus":0.016092029970673504,"score_gpt":0.2590570821470061,"score_spread":0.24296505217633263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290466112","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":[0.0000668104,0.00014731342,0.9948519,0.00053172343,0.0021154364,0.0008787324,0.00018891715,0.001040486,0.00017866454],"genre_scores_gemma":[0.009486853,0.000021171016,0.9878802,0.0011377651,0.00014180536,0.00041611513,0.00021478666,0.00008466887,0.0006166521],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99516183,0.00065653486,0.0007088317,0.0014981595,0.0011360003,0.0008386627],"domain_scores_gemma":[0.9969772,0.00030016145,0.00046892193,0.001288742,0.0006657777,0.0002991999],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00094211934,0.00052951265,0.0005358066,0.0005107572,0.0013065285,0.00021925863,0.0016695762,0.00011746756,0.00041529664],"category_scores_gemma":[0.00018026544,0.000604451,0.00021789277,0.002333947,0.00014402834,0.0013058424,0.0011072828,0.0006999238,0.00006745919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011588274,0.00042778096,0.00004897513,0.000004860566,0.000044366465,0.00010385173,0.0002909625,0.9235477,0.00003897782,0.0006533299,0.00013547322,0.07469215],"study_design_scores_gemma":[0.0027397254,0.0002307758,0.000491447,0.0000071829436,0.000019142566,0.00006676509,0.00014593339,0.99300325,0.0009885894,0.0004893606,0.0011188677,0.0006989606],"about_ca_topic_score_codex":0.00033573576,"about_ca_topic_score_gemma":0.000010241459,"teacher_disagreement_score":0.07399319,"about_ca_system_score_codex":0.00084407756,"about_ca_system_score_gemma":0.0003848512,"threshold_uncertainty_score":0.9999936},"labels":[],"label_agreement":null},{"id":"W4290755172","doi":"10.1016/j.swevo.2022.101145","title":"ACDB-EA: Adaptive convergence-diversity balanced evolutionary algorithm for many-objective optimization","year":2022,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Science and Technology Program of Zhejiang Province; National Natural Science Foundation of China","keywords":"Convergence (economics); Computer science; Evolutionary algorithm; Mathematical optimization; Benchmark (surveying); Similarity (geometry); Pareto principle; Selection (genetic algorithm); Cosine similarity; Population; Evolutionary computation; Algorithm; Mathematics; Artificial intelligence; Cluster analysis","score_opus":0.015022912938113494,"score_gpt":0.24065138581094397,"score_spread":0.22562847287283047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290755172","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":[0.0005528623,0.00039968255,0.9956776,0.00037808027,0.0011708128,0.0010232656,0.00020613191,0.00036022888,0.00023133529],"genre_scores_gemma":[0.16710782,0.000057649417,0.83153766,0.0002455915,0.000121556164,0.000264767,0.00037427217,0.000023823708,0.00026685483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975933,0.00023622163,0.0003486893,0.0008660063,0.00056316674,0.0003926499],"domain_scores_gemma":[0.998455,0.00025823814,0.0002930034,0.0002281371,0.00062671513,0.00013889249],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00027299454,0.00027964244,0.00025899324,0.00032467453,0.00258895,0.000047208345,0.00042362453,0.00007975991,0.00003915026],"category_scores_gemma":[0.000045079014,0.0003435551,0.00010995923,0.0008276978,0.00014046724,0.0012678488,0.0011076267,0.00023065215,0.000008300723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007131323,0.00015655103,0.00027615426,0.000008707389,0.000051345673,0.000006721712,0.00083681365,0.9751617,0.000009461017,0.0042360434,0.0014528789,0.017732337],"study_design_scores_gemma":[0.0015924202,0.00041277037,0.0069364826,0.000007930759,0.00002265633,0.00007276961,0.0006048309,0.97631824,0.00001782285,0.013294803,0.0003283216,0.00039094978],"about_ca_topic_score_codex":0.00004960534,"about_ca_topic_score_gemma":0.0000010511935,"teacher_disagreement_score":0.16655496,"about_ca_system_score_codex":0.00082188763,"about_ca_system_score_gemma":0.00023033938,"threshold_uncertainty_score":0.99990165},"labels":[],"label_agreement":null},{"id":"W4291270603","doi":"10.1007/978-3-031-14714-2_6","title":"Adaptive Function Value Warping for Surrogate Model Assisted Evolutionary Optimization","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Surrogate model; Image warping; Mathematical optimization; Evolutionary algorithm; Function (biology); Bellman equation; Sensitivity (control systems); Black box; Algorithm; Artificial intelligence; Machine learning; Mathematics","score_opus":0.027269641510260293,"score_gpt":0.2562967161073696,"score_spread":0.2290270745971093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291270603","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":[7.458608e-7,0.0002652473,0.99449164,0.00034607388,0.002145578,0.0011697845,0.000041507585,0.00035643278,0.0011829877],"genre_scores_gemma":[0.0030932191,0.00004406053,0.99506867,0.0007732639,0.00022620984,0.000121538826,0.0000759641,0.000069876114,0.000527175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954885,0.00007071747,0.0006232197,0.00203603,0.0011105721,0.00067094585],"domain_scores_gemma":[0.9968832,0.000579461,0.0005577239,0.0010278444,0.0007912608,0.00016047854],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007517888,0.00059881073,0.00051861774,0.0011012888,0.0008974214,0.00025689576,0.0018840227,0.00028039873,0.00003021575],"category_scores_gemma":[0.00017697676,0.00065534597,0.00020031529,0.0010153833,0.00038747024,0.0017505136,0.0012669278,0.00072490313,0.000006188551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027484377,0.000033457138,0.0000014352596,0.000013240914,0.000015734007,0.000007292216,0.00020005897,0.8640445,0.000014209658,0.03036807,0.000009699036,0.10526484],"study_design_scores_gemma":[0.0005630794,0.00024902975,0.000014727782,0.00009374011,0.000017430017,0.000033321987,4.4043034e-7,0.9197772,0.000041997122,0.07830168,0.00026846092,0.0006388553],"about_ca_topic_score_codex":0.000012031435,"about_ca_topic_score_gemma":0.0000099017425,"teacher_disagreement_score":0.10462599,"about_ca_system_score_codex":0.0015676145,"about_ca_system_score_gemma":0.0010040207,"threshold_uncertainty_score":0.9995898},"labels":[],"label_agreement":null},{"id":"W4292512861","doi":"10.1007/s41019-022-00193-5","title":"Dimensionality Reduction in Surrogate Modeling: A Review of Combined Methods","year":2022,"lang":"en","type":"review","venue":"Data Science and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Surrogate model; Dimensionality reduction; Curse of dimensionality; Computer science; Uncertainty quantification; Reduction (mathematics); Artificial intelligence; Mathematical optimization; Machine learning; Mathematics","score_opus":0.15616597191542345,"score_gpt":0.42200037778948857,"score_spread":0.26583440587406515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292512861","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":[1.0454513e-7,0.5995379,0.3999264,0.000013009159,0.00023053953,0.00022608094,0.000023816368,0.000028641885,0.000013490781],"genre_scores_gemma":[3.9805718e-7,0.6631464,0.33672702,0.000008227668,0.0000067469587,0.000025043375,0.000076947275,0.000007222803,0.0000020075693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997844,0.000119315526,0.00054035947,0.0008019376,0.00046324093,0.00023116457],"domain_scores_gemma":[0.9982416,0.00014676098,0.000192007,0.0012294033,0.00011161102,0.00007863948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045665177,0.00019889968,0.00074347004,0.00040436746,0.00009510049,0.000040689123,0.0018236525,0.00003803099,0.000006311624],"category_scores_gemma":[0.0011956708,0.0001837983,0.000041941013,0.0029628177,0.00007560166,0.0015298553,0.002122661,0.00027726564,6.277223e-7],"study_design_candidate":"design_other","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":[4.3214956e-7,0.000022997214,4.1355218e-8,0.01286721,0.0000073337383,0.0000029257649,0.00002650431,0.013502077,0.0000061101464,0.0014247376,0.000011405977,0.9721282],"study_design_scores_gemma":[0.000053728207,0.000009819757,3.0124104e-7,0.008015121,0.000019973935,0.000033764376,0.0000037825089,0.81958485,0.0000012296235,0.000022396902,0.17208537,0.00016965649],"about_ca_topic_score_codex":0.000012166797,"about_ca_topic_score_gemma":1.6263637e-7,"teacher_disagreement_score":0.9719586,"about_ca_system_score_codex":0.00016579531,"about_ca_system_score_gemma":0.00039924847,"threshold_uncertainty_score":0.7495077},"labels":[],"label_agreement":null},{"id":"W4292646494","doi":"","title":"Configuration of a dynamic MOLS algorithm for Bi-objective flowshop scheduling","year":2019,"lang":"en","type":"other","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Scheduling (production processes); Processor scheduling; Dynamic priority scheduling; Algorithm; Parallel computing; Mathematical optimization; Mathematics; Schedule; Operating system","score_opus":0.008833880944187689,"score_gpt":0.24371462329778404,"score_spread":0.23488074235359635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292646494","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":[0.000006773339,0.00058189576,0.87844646,0.0005490176,0.0003707379,0.0014247418,0.00014035792,0.00038658132,0.11809343],"genre_scores_gemma":[0.0006174872,0.00021888022,0.7654005,0.00004230082,0.000022006621,0.00014617201,0.00027357158,0.00020356184,0.23307548],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961285,0.0014161303,0.00056170195,0.0010536044,0.00045520964,0.0003848545],"domain_scores_gemma":[0.9930534,0.0011952944,0.0010464832,0.0020545356,0.002522944,0.0001273952],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017607975,0.00042495262,0.0005739284,0.00054555375,0.00016877202,0.00018871942,0.0014844352,0.00035221057,0.00012733291],"category_scores_gemma":[0.0009841095,0.0004692245,0.00023228502,0.00076875143,0.00022632505,0.0003411624,0.00038594156,0.00029713233,0.00006328056],"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":[0.000017437078,0.0007272338,0.000010170688,0.0002922171,0.00031884175,0.0000035921385,0.004708793,0.0015209058,0.0029593979,0.11295004,0.0028247316,0.87366664],"study_design_scores_gemma":[0.0017165935,0.0000019833403,0.000028326358,0.0011883132,0.000034976732,0.000007458469,0.00008996746,0.95947003,0.014067246,0.0010673974,0.021762956,0.00056475576],"about_ca_topic_score_codex":0.00020211142,"about_ca_topic_score_gemma":0.00021255964,"teacher_disagreement_score":0.9579491,"about_ca_system_score_codex":0.00018079078,"about_ca_system_score_gemma":0.00037273875,"threshold_uncertainty_score":0.99977595},"labels":[],"label_agreement":null},{"id":"W4293406243","doi":"","title":"A new hybrid method to solve the multi-objective optimization problem for a composite hat-stiffened panel","year":2016,"lang":"en","type":"book-chapter","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Polytechnique Montréal","funders":"","keywords":"Meta heuristic; Mathematical optimization; Heuristic; Optimization problem; Composite number; Rigidity (electromagnetism); Computer science; Mathematics; Algorithm; Engineering; Structural engineering","score_opus":0.022030631805040578,"score_gpt":0.2539166139755995,"score_spread":0.23188598217055892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293406243","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":[4.7353458e-7,0.0002516213,0.92599803,0.00919522,0.00023600059,0.0025772296,0.00015437636,0.0004456965,0.061141368],"genre_scores_gemma":[0.00003975301,0.00011597145,0.7984581,0.00036141436,0.0000524792,0.00023756639,0.00013740067,0.00011637256,0.20048097],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99522275,0.0012069866,0.00078099855,0.0015818017,0.0006133924,0.00059406634],"domain_scores_gemma":[0.9884045,0.0033863252,0.000941008,0.0025078387,0.004352084,0.00040821466],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0032020183,0.00069294043,0.0006390819,0.00038792804,0.00080372154,0.00058918213,0.0026528572,0.00026459762,0.00008044425],"category_scores_gemma":[0.0008347057,0.0005710645,0.00036229144,0.0003385116,0.00016850742,0.00066549866,0.0012052858,0.0004329346,0.000083847095],"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":[0.000047326736,0.00018063816,0.0000035645119,0.00004820015,0.00022111117,0.0000057939665,0.0055492017,0.021712484,0.00079626817,0.6438042,0.0010220314,0.3266092],"study_design_scores_gemma":[0.0030542805,0.0000064820433,0.000034646255,0.001950266,0.00012993345,0.00006618195,0.000032351116,0.85258836,0.023340365,0.03294656,0.08431203,0.0015385499],"about_ca_topic_score_codex":0.000111884925,"about_ca_topic_score_gemma":0.00018395683,"teacher_disagreement_score":0.8308759,"about_ca_system_score_codex":0.00040634244,"about_ca_system_score_gemma":0.0005366978,"threshold_uncertainty_score":0.9996741},"labels":[],"label_agreement":null},{"id":"W4294811593","doi":"10.1109/cec55065.2022.9870282","title":"Bicriterion Coevolution for the Multi-objective Travelling Salesperson Problem","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Congress on Evolutionary Computation (CEC)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer 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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convergence (economics); Mathematical optimization; Sorting; Evolutionary algorithm; Multi-objective optimization; Computer science; Pareto principle; Metric (unit); Crossover; Selection (genetic algorithm); Mathematics; Cluster analysis; Algorithm; Artificial intelligence; Engineering","score_opus":0.02872381173389856,"score_gpt":0.286230242511338,"score_spread":0.25750643077743945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294811593","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":[0.00075965846,0.0005797863,0.9895169,0.0012975291,0.004766823,0.002275553,0.0002118059,0.00046280626,0.00012911159],"genre_scores_gemma":[0.42483288,0.00010852136,0.5676479,0.0010097747,0.00042151843,0.0026789792,0.00042676923,0.00012675297,0.0027469439],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964125,0.0005058989,0.0005823087,0.0010676136,0.0008924009,0.00053928565],"domain_scores_gemma":[0.9971917,0.0010505555,0.00049234123,0.000507363,0.00063555915,0.00012246119],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.000649433,0.00038841978,0.00031106817,0.0004615577,0.002584712,0.0001550883,0.0009742047,0.0000862554,0.000057102494],"category_scores_gemma":[0.00010688428,0.00037887754,0.00020353222,0.0013986973,0.00017297994,0.00079002907,0.00027291756,0.00052855856,0.000032767643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014476065,0.00036691545,0.00006052464,0.000019064373,0.00007295305,0.000008928406,0.001079923,0.97194654,0.0007855843,0.0055167726,0.004736095,0.015261924],"study_design_scores_gemma":[0.0021458433,0.00046349497,0.0017157559,0.000023534718,0.000025596475,0.00006673388,0.00064722064,0.9855216,0.00030664064,0.0016603414,0.006967248,0.00045597422],"about_ca_topic_score_codex":0.000036076864,"about_ca_topic_score_gemma":0.00000947775,"teacher_disagreement_score":0.42407322,"about_ca_system_score_codex":0.0011401052,"about_ca_system_score_gemma":0.0002702615,"threshold_uncertainty_score":0.9998663},"labels":[],"label_agreement":null},{"id":"W4300388662","doi":"10.48550/arxiv.1305.0182","title":"Space-filling Latin Hypercube Designs based on Randomization\\n Restrictions in Factorial Experiments","year":2013,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Orthogonality; Latin hypercube sampling; Minimax; Class (philosophy); Space (punctuation); Factorial; Computer science; Mathematics; Factorial experiment; Combinatorics; Discrete mathematics; Algorithm; Mathematical optimization; Geometry; Statistics; Artificial intelligence; Mathematical analysis","score_opus":0.12179743036777486,"score_gpt":0.22985694994546127,"score_spread":0.1080595195776864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300388662","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":[0.022244846,0.000032653807,0.96950537,0.0000762887,0.0038530997,0.0022962678,0.000034711167,0.00026325893,0.0016935129],"genre_scores_gemma":[0.9158845,0.00039536535,0.0815531,0.000093822884,0.00037536482,0.000016614667,0.00005618108,0.00008610492,0.0015389454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99397266,0.0010855409,0.00084414723,0.0027840761,0.00039183174,0.00092173007],"domain_scores_gemma":[0.9948912,0.0011229897,0.0008843311,0.0018436373,0.00078205904,0.0004757764],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047073257,0.0009841474,0.0009761538,0.0016041708,0.00067677203,0.00039352497,0.0017352988,0.00074863556,0.00027846743],"category_scores_gemma":[0.00057886954,0.0012306808,0.0004019059,0.003109646,0.00025654954,0.0013682868,0.0008970623,0.0013536098,0.00028765528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000489677,0.00062892045,0.0017568065,0.00002120932,0.00006379962,0.00011643559,0.0008223099,0.9818097,0.00014806722,0.013837399,0.000039637278,0.00026606902],"study_design_scores_gemma":[0.012301065,0.00017017357,0.00085518137,0.0002502184,0.000062918916,0.0000014182948,0.00024351355,0.9806993,0.00074597704,0.0034437077,0.00013635145,0.0010901807],"about_ca_topic_score_codex":0.0006404744,"about_ca_topic_score_gemma":0.000043963482,"teacher_disagreement_score":0.8936396,"about_ca_system_score_codex":0.0019491795,"about_ca_system_score_gemma":0.0007901542,"threshold_uncertainty_score":0.9990143},"labels":[],"label_agreement":null},{"id":"W4306377333","doi":"10.3390/electronics11203317","title":"A Large Scale Evolutionary Algorithm Based on Determinantal Point Processes for Large Scale Multi-Objective Optimization Problems","year":2022,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Evolutionary algorithm; Mathematical optimization; Evolutionary computation; Differential evolution; Multi-objective optimization; Population; Optimization problem; Sorting; Computer science; Particle swarm optimization; Scale (ratio); Algorithm; Mathematics","score_opus":0.008294595748993338,"score_gpt":0.2500965505629103,"score_spread":0.24180195481391697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306377333","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":[0.00012026347,0.0010126353,0.99540955,0.0003020109,0.0003287193,0.0019007181,0.0004023376,0.00042922754,0.000094538314],"genre_scores_gemma":[0.017365389,0.000084664745,0.97877294,0.00064236997,0.00008584447,0.002071253,0.00036212386,0.00009249981,0.0005228966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966621,0.00019216491,0.00041234074,0.0010878617,0.0005651567,0.0010803788],"domain_scores_gemma":[0.9981795,0.00020948536,0.00031480237,0.00057456957,0.00058239704,0.00013927517],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005534927,0.000379609,0.00032958903,0.0003364968,0.0013891887,0.00010070121,0.00080242864,0.00009944816,0.00004384099],"category_scores_gemma":[0.0001602377,0.0004193311,0.00014902715,0.0014426913,0.000040927083,0.00080226234,0.00037978482,0.0004607382,0.00000781378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007102528,0.0017569745,0.00012103682,0.000048504302,0.000021874212,0.0000053446756,0.0014642621,0.990943,0.000059807764,0.0003695016,0.00021113288,0.0049274946],"study_design_scores_gemma":[0.0034713838,0.0011337877,0.00009292394,0.000023481214,0.000016537106,0.000039702485,0.00028372943,0.9870372,0.0009773235,0.00052011426,0.00589691,0.000506953],"about_ca_topic_score_codex":0.0000020511861,"about_ca_topic_score_gemma":0.000046372625,"teacher_disagreement_score":0.017245125,"about_ca_system_score_codex":0.0012766147,"about_ca_system_score_gemma":0.0009817497,"threshold_uncertainty_score":0.9999109},"labels":[],"label_agreement":null},{"id":"W4309231192","doi":"10.1007/s10479-022-05051-1","title":"Theory, computation, and practice of multiobjective optimisation","year":2022,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Dalhousie University","funders":"","keywords":"Theory of computation; Computation; Computer science; Mathematical optimization; Multi-objective optimization; Mathematical economics; Mathematics; Algorithm","score_opus":0.15642541301236748,"score_gpt":0.4730439202187418,"score_spread":0.3166185072063743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309231192","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":[0.003762915,0.00022164322,0.9900854,0.0027654872,0.00005337499,0.00048658825,0.00002261217,0.000025314348,0.0025766403],"genre_scores_gemma":[0.59180653,0.00010299983,0.40756777,0.00012853791,0.000011198388,0.00009923083,0.000013404142,0.00000813693,0.00026217184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969598,0.0015761781,0.00029688003,0.00029338032,0.0006976895,0.00017604078],"domain_scores_gemma":[0.99499625,0.0015559279,0.00008315506,0.0002813209,0.003028128,0.00005522506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033327362,0.00007240698,0.00013009651,0.00043515113,0.00060222804,0.00006262018,0.00034887763,0.00002296695,0.000039635604],"category_scores_gemma":[0.002644317,0.00007755129,0.000027890203,0.0011820613,0.00020998668,0.0010317358,0.0004886177,0.00022883575,0.0000027225412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005170589,0.00027189424,0.000016657848,0.000007780975,0.000031077823,0.0000019915672,0.0052075023,0.85670286,0.0014577765,0.11752149,0.0001390881,0.018590195],"study_design_scores_gemma":[0.00049579644,0.0004769642,0.0006802368,0.000007852172,0.0000034088723,0.000030587722,0.00426658,0.97968817,0.008062981,0.0054052696,0.0007660051,0.00011613152],"about_ca_topic_score_codex":0.000093955365,"about_ca_topic_score_gemma":0.0000044013227,"teacher_disagreement_score":0.58804363,"about_ca_system_score_codex":0.00003879579,"about_ca_system_score_gemma":0.00021110498,"threshold_uncertainty_score":0.4631912},"labels":[],"label_agreement":null},{"id":"W4310150506","doi":"10.1016/j.engappai.2022.105521","title":"A qualitative systematic review of metaheuristics applied to tension/compression spring design problem: Current situation, recommendations, and research direction","year":2022,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Benchmarking; Benchmark (surveying); Computer science; Metaheuristic; Heuristics; Field (mathematics); Industrial engineering; Algorithm; Data mining; Management science; Mathematics","score_opus":0.1018654061451459,"score_gpt":0.40995978419979284,"score_spread":0.30809437805464696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310150506","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":[0.000016431432,0.0033104424,0.99358106,0.00032414772,0.000074155505,0.0025891778,0.000009712828,0.000079182406,0.000015666143],"genre_scores_gemma":[0.058766413,0.0013931714,0.93639207,0.000020087866,0.000016536398,0.0033733586,0.000009643411,0.000018035635,0.000010695916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977734,0.00033579607,0.0008319524,0.00038383045,0.00050780934,0.00016720923],"domain_scores_gemma":[0.99725044,0.0010569912,0.00031345992,0.00050945714,0.00079462386,0.00007500368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027610953,0.00012590217,0.00032654963,0.00046475203,0.0002787278,0.000027125427,0.00047956774,0.000019823538,0.000006551498],"category_scores_gemma":[0.00084152695,0.00013415547,0.000035109744,0.0020915933,0.00004261355,0.00013906595,0.0003765776,0.00023580652,0.000006262876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011648984,0.00034684694,6.522882e-7,0.026553096,0.00004188354,2.4090312e-7,0.007492228,0.7162124,0.010435171,0.18495308,0.00008428207,0.0538685],"study_design_scores_gemma":[0.000040011233,0.0001197773,0.0000058366313,0.010812893,0.00005110233,0.000004565947,0.0016720997,0.94598055,0.030390773,0.010221916,0.00038347437,0.00031698248],"about_ca_topic_score_codex":0.0000141564315,"about_ca_topic_score_gemma":5.7826816e-7,"teacher_disagreement_score":0.22976819,"about_ca_system_score_codex":0.00013372934,"about_ca_system_score_gemma":0.00006211478,"threshold_uncertainty_score":0.54707015},"labels":[],"label_agreement":null},{"id":"W4311129318","doi":"10.1080/0305215x.2022.2147518","title":"Improved multi-objective structural optimization with adaptive repair-based constraint handling","year":2022,"lang":"en","type":"article","venue":"Engineering Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Mathematical optimization; Metaheuristic; Benchmark (surveying); Multi-objective optimization; Truss; Optimization problem; Population; Convergence (economics); Constraint (computer-aided design); Evolutionary algorithm; Pareto principle; Heuristics; Computer science; Engineering optimization; Mathematics; Engineering","score_opus":0.008933355860759627,"score_gpt":0.21428033340807276,"score_spread":0.20534697754731313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311129318","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":[0.00011864088,0.000065254964,0.9969817,0.000078667355,0.00047527783,0.000808273,0.000028018421,0.0013926694,0.000051545463],"genre_scores_gemma":[0.11174369,0.0000031941167,0.887631,0.000114579205,0.000040858922,0.00024436484,0.00010212967,0.00006753486,0.000052652333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783915,0.00011533706,0.00037987327,0.0007918513,0.0004347851,0.00043901097],"domain_scores_gemma":[0.9985528,0.0001316717,0.00027217527,0.0005022876,0.00040408853,0.00013696452],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024693744,0.0003769246,0.00028716394,0.00040795698,0.0005706336,0.0001279156,0.00044376295,0.0000720525,0.000071684895],"category_scores_gemma":[0.00015060633,0.00040024635,0.00009766854,0.001302487,0.000059341186,0.00090338424,0.000214796,0.00039852376,0.0000010391011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048304708,0.00005327191,0.000029831946,0.000009086917,0.00004999869,0.0000140749735,0.00042658186,0.99817115,0.000092136106,0.00066805666,0.0000041986486,0.00043329227],"study_design_scores_gemma":[0.0020113424,0.0003224068,0.000049513303,0.000020498941,0.000019850551,0.00004237456,0.00016640128,0.9962902,0.000534962,0.0000038843236,0.00002245757,0.00051608775],"about_ca_topic_score_codex":0.000018143952,"about_ca_topic_score_gemma":0.0000017386905,"teacher_disagreement_score":0.111625046,"about_ca_system_score_codex":0.00062968966,"about_ca_system_score_gemma":0.0002312612,"threshold_uncertainty_score":0.99984497},"labels":[],"label_agreement":null},{"id":"W4312198775","doi":"10.1109/ieem55944.2022.9989719","title":"A Systematic Assessment of Genetic Algorithm (GA) in Optimizing Machine Learning Model: A Case Study from Building Science","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Hyperparameter; Bayesian optimization; Hyperparameter optimization; Genetic algorithm; Computer science; Algorithm; Metaheuristic; Machine learning; Artificial intelligence; Range (aeronautics); Materials science; Support vector machine","score_opus":0.038690728808870874,"score_gpt":0.29058298424018253,"score_spread":0.25189225543131166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312198775","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":[0.10140447,0.000035504727,0.8969689,0.000015917663,0.00081871613,0.00058525056,0.000019209316,0.000091720336,0.00006032152],"genre_scores_gemma":[0.7735383,0.000024271365,0.22619244,0.0000034293205,0.000024249832,0.0001761308,0.0000030118663,0.000018595709,0.000019603873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763703,0.0000743803,0.0006462825,0.0005385316,0.00084159354,0.00026215537],"domain_scores_gemma":[0.9991052,0.00015767227,0.00024713506,0.00030884534,0.000103254955,0.00007790411],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009344924,0.00024232884,0.00038488244,0.0011065081,0.00013127783,0.000108827604,0.0007857114,0.000035419183,0.000011539847],"category_scores_gemma":[0.00010772576,0.00028013866,0.000048792477,0.0009096516,0.000026833104,0.00030274483,0.00061375945,0.00061061676,1.7217482e-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":[0.0000048504103,0.00012443063,0.00014097008,0.00010896128,0.00009932439,0.00025706933,0.00050798286,0.992679,0.0011563476,0.0032236131,3.60361e-7,0.0016971154],"study_design_scores_gemma":[0.0011315416,0.00016362523,0.00010472169,0.00051454594,0.000023887924,0.000047979058,0.00082538667,0.99679464,0.00013456715,0.000025279907,0.0000017137054,0.00023208407],"about_ca_topic_score_codex":0.00016626023,"about_ca_topic_score_gemma":0.0000013506751,"teacher_disagreement_score":0.6721338,"about_ca_system_score_codex":0.00038345146,"about_ca_system_score_gemma":0.00006936853,"threshold_uncertainty_score":0.9999651},"labels":[],"label_agreement":null},{"id":"W4312328538","doi":"10.1109/iros47612.2022.9982270","title":"Adaptive Sampling of Latent Phenomena using Heterogeneous Robot Teams (ASLaP-HR)","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Army Research Office; Office of Naval Research; Association of Research Libraries","keywords":"Observable; Sampling (signal processing); Computer science; Robot; Field (mathematics); Spatial analysis; Artificial intelligence; Data mining; Measure (data warehouse); Machine learning; Computer vision; Mathematics; Statistics; Physics","score_opus":0.10178456093365242,"score_gpt":0.3176104215629301,"score_spread":0.21582586062927772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312328538","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":[0.009324158,0.00033879766,0.9848834,0.00012102144,0.0035378542,0.0006226344,0.00010797742,0.00009136822,0.00097277685],"genre_scores_gemma":[0.9793066,0.0001794397,0.01896689,0.00013692556,0.00017640338,0.0001455785,0.000033120974,0.00003797307,0.00101711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965746,0.00024281135,0.000843976,0.00086789456,0.0010843164,0.00038640955],"domain_scores_gemma":[0.99806476,0.00013549918,0.00065698416,0.00047915083,0.00049452676,0.00016908307],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047010783,0.0003639213,0.00047545784,0.0004304587,0.00040811597,0.0002341041,0.0011016455,0.000069113106,0.0002770726],"category_scores_gemma":[0.00004041339,0.00036412777,0.0001416329,0.00037276556,0.000110391054,0.00035396038,0.00063005363,0.00039966244,0.000014183496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057320805,0.00019734872,0.00016533841,0.000017899214,0.00014052485,0.000022587694,0.0010215365,0.94365275,0.0045946618,0.04508744,0.000018997334,0.005023608],"study_design_scores_gemma":[0.00040476464,0.00036835103,0.000055979293,0.00010413002,0.000014098651,0.00012698177,0.00088782,0.99459743,0.0020266841,0.00070128293,0.00033174167,0.0003807588],"about_ca_topic_score_codex":0.00028263128,"about_ca_topic_score_gemma":0.0000096306185,"teacher_disagreement_score":0.9699824,"about_ca_system_score_codex":0.00062930596,"about_ca_system_score_gemma":0.00016023155,"threshold_uncertainty_score":0.9998811},"labels":[],"label_agreement":null},{"id":"W4312424858","doi":"10.1109/iscas48785.2022.9937568","title":"Variation-Aware Analog Circuit Sizing in Carbon Nanotube","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Carbon nanotube field-effect transistor; Sizing; Computer science; Process variation; Electronic circuit; Electronic engineering; Transistor; Spice; Analogue electronics; Carbon nanotube; Circuit design; Integrated circuit design; Process (computing); Field-effect transistor; Materials science; Electrical engineering; Nanotechnology; Engineering; Embedded system; Voltage","score_opus":0.021991945027683504,"score_gpt":0.2541065315056993,"score_spread":0.23211458647801578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312424858","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":[0.12594274,0.0009558514,0.7829792,0.0035014576,0.03132344,0.0029118808,0.00043444647,0.000761047,0.051189966],"genre_scores_gemma":[0.9975257,0.000045813456,0.00015726937,0.00032620164,0.00025327713,0.00031664482,0.000033263357,0.000033366636,0.0013084676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99674785,0.00031932053,0.00062899844,0.0008884413,0.0010568232,0.00035854455],"domain_scores_gemma":[0.9986495,0.0002477121,0.0003351694,0.00044304968,0.00020788019,0.000116732284],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007065901,0.0002724549,0.00032760281,0.00048714512,0.0003048461,0.000281938,0.0009294042,0.00007665402,0.000054484804],"category_scores_gemma":[0.00005414728,0.0003015431,0.000078094934,0.00073924294,0.0000307145,0.00047819296,0.00028211388,0.00039799872,0.000011041562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025623072,0.0005798884,0.008232124,0.00006194828,0.00021165946,0.00038727556,0.008030794,0.8598595,0.01746422,0.09646562,0.00019497755,0.0084863445],"study_design_scores_gemma":[0.0012814383,0.00016789329,0.005332922,0.000063716696,0.000008479552,0.00024434816,0.0005158126,0.9885493,0.00017642476,0.00046745283,0.002676826,0.0005154167],"about_ca_topic_score_codex":0.00039604455,"about_ca_topic_score_gemma":0.000032522137,"teacher_disagreement_score":0.871583,"about_ca_system_score_codex":0.00083805865,"about_ca_system_score_gemma":0.00009137589,"threshold_uncertainty_score":0.9999437},"labels":[],"label_agreement":null},{"id":"W4312930523","doi":"10.1115/detc2022-87995","title":"Reinforcement Learning Based Sequential Batch-Sampling for Bayesian Optimal Experimental Design","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Reinforcement learning; Leverage (statistics); Bayesian optimization; Artificial intelligence; Suite; Bayesian probability; Machine learning; Time budget; Task (project management)","score_opus":0.048870433704315944,"score_gpt":0.31115947917202547,"score_spread":0.26228904546770954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312930523","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":[0.000038500755,0.000021925645,0.9979005,0.00014703462,0.0003808425,0.00074702007,0.0000016536642,0.00034760192,0.00041491655],"genre_scores_gemma":[0.22601123,4.3259413e-7,0.772183,0.0003191946,0.000039504786,0.0004674692,0.000026550102,0.000022354532,0.00093023904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829537,0.00013468292,0.00027488565,0.0005211869,0.0003866947,0.00038720205],"domain_scores_gemma":[0.9992034,0.00018899138,0.0001307796,0.00028575107,0.00008749723,0.00010360343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041615448,0.00018209263,0.00015474144,0.00014797958,0.0008902648,0.00012992398,0.00055899477,0.000027074506,0.00051814277],"category_scores_gemma":[0.00004976392,0.00020343577,0.00009835872,0.00031215357,0.000027558126,0.00045525047,0.0004193509,0.00017709253,0.000005134755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004131772,0.000058287653,0.0000030640356,0.000002428785,0.000011385506,0.000004861066,0.00041525543,0.9919907,0.003121256,0.0026614333,0.00007216794,0.0016178295],"study_design_scores_gemma":[0.0012424251,0.0004779953,0.0000012540411,0.0000024354247,0.000003497972,0.0000090064,0.00041866986,0.9521381,0.04314327,0.000052218686,0.0022591816,0.0002519495],"about_ca_topic_score_codex":0.000008776398,"about_ca_topic_score_gemma":1.7777388e-7,"teacher_disagreement_score":0.22597274,"about_ca_system_score_codex":0.00038330109,"about_ca_system_score_gemma":0.00015371823,"threshold_uncertainty_score":0.829587},"labels":[],"label_agreement":null},{"id":"W4312940312","doi":"10.23952/jnva.6.2022.6.07","title":"A nonmonotone gradient method for constrained multiobjective optimization problems","year":2022,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Mathematical optimization; Multi-objective optimization; Computer science; Gradient method; Proximal Gradient Methods; Constrained optimization; Mathematics; Gradient descent; Artificial intelligence; Artificial neural network","score_opus":0.013278285704088925,"score_gpt":0.2868655736015706,"score_spread":0.2735872878974817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312940312","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":[0.00010660593,0.00006426451,0.99853796,0.0007987233,0.0001332879,0.00023052965,0.00008145803,0.000015036564,0.000032123193],"genre_scores_gemma":[0.01058476,0.000021028809,0.9889558,0.00017388896,0.00009376769,0.000037644913,0.00004821157,0.000007915212,0.00007700235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985351,0.00017121511,0.0005099568,0.00024934887,0.000398149,0.00013622668],"domain_scores_gemma":[0.9978441,0.0003883194,0.00069745927,0.000120049,0.0008604259,0.000089672176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009628369,0.00011787301,0.0003241625,0.00064916746,0.0003480487,0.00006809693,0.00024595173,0.000028537806,0.000050999304],"category_scores_gemma":[0.00019017648,0.00010900057,0.00024773754,0.0013511974,0.000022999653,0.00042133784,0.00010639942,0.00015539875,2.110671e-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":[0.000038557202,0.00018893857,0.00014524715,0.0000036610707,0.00074094406,0.0000025559184,0.00062277215,0.99183446,0.00007539658,0.0036187018,0.00000897488,0.0027197825],"study_design_scores_gemma":[0.001274019,0.00022961189,0.0006856211,0.0000022704407,0.0003357022,0.000063464504,0.00012517523,0.9954556,0.00003169501,0.0012990762,0.0003765294,0.00012118876],"about_ca_topic_score_codex":0.000012377969,"about_ca_topic_score_gemma":0.0000032805315,"teacher_disagreement_score":0.010478155,"about_ca_system_score_codex":0.0001440893,"about_ca_system_score_gemma":0.00016151922,"threshold_uncertainty_score":0.44449142},"labels":[],"label_agreement":null},{"id":"W4313256689","doi":"10.1007/s00158-022-03435-2","title":"Variable functioning and its application to large scale steel frame design optimization","year":2022,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Ontario Tech University","funders":"University of Technology Sydney; National Science Foundation","keywords":"Mathematical optimization; Particle swarm optimization; Heuristics; Multi-swarm optimization; Variable (mathematics); Engineering design process; Engineering optimization; Frame (networking); Process (computing); Metaheuristic; Computer science; Differential evolution; Optimization problem; Continuous optimization; Convergence (economics); Mathematics; Engineering","score_opus":0.01009886240194589,"score_gpt":0.2498449909690287,"score_spread":0.23974612856708283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313256689","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":[0.0020460526,0.00020068584,0.9954033,0.0003592607,0.0004068981,0.0011690453,0.00003861492,0.00027064516,0.00010553539],"genre_scores_gemma":[0.09045729,0.000037401234,0.90846545,0.00015710083,0.000063249696,0.00030248272,0.00014229032,0.000031211468,0.00034350104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795616,0.00017962714,0.00033287558,0.00083995255,0.00033031803,0.00036106945],"domain_scores_gemma":[0.998989,0.00008264835,0.000191892,0.0003047395,0.0002394036,0.00019232961],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00032138717,0.00026471127,0.00021969511,0.0002496796,0.001994085,0.000158785,0.0002954471,0.000075148855,0.000079570804],"category_scores_gemma":[0.000053645785,0.00027643592,0.000026816622,0.0010165952,0.000030270021,0.0012908435,0.0009795516,0.00021089373,0.0000026128182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048275055,0.000029062034,0.00013405363,0.000016105008,0.000009979672,0.0000011521591,0.0013826066,0.9923937,0.00057022396,0.0035790266,0.00001358404,0.001822243],"study_design_scores_gemma":[0.00085967977,0.00019568665,0.00086890155,0.000008908409,0.000020581503,0.00005112368,0.00047260238,0.9961335,0.00012805768,0.0008314735,0.000078809084,0.00035065296],"about_ca_topic_score_codex":0.00000589353,"about_ca_topic_score_gemma":0.000001255062,"teacher_disagreement_score":0.08841124,"about_ca_system_score_codex":0.00014964274,"about_ca_system_score_gemma":0.000056054072,"threshold_uncertainty_score":0.99996877},"labels":[],"label_agreement":null},{"id":"W4313299845","doi":"10.1287/moor.2022.1344","title":"Error Analysis of Surrogate Models Constructed Through Operations on Submodels","year":2022,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Black box; Chen; Mathematics; Function (biology); Mathematical optimization; Surrogate model; Computer science; Algorithm; Artificial intelligence","score_opus":0.1705759330340063,"score_gpt":0.416556545346156,"score_spread":0.24598061231214968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313299845","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":[0.037145317,0.000031389616,0.95919925,0.0003210975,0.00005107023,0.0006405224,0.00022320605,0.000048729744,0.0023394355],"genre_scores_gemma":[0.5128616,0.0000146347265,0.48645332,0.000020528674,0.000004275812,0.00017168288,0.00005312824,0.000012619789,0.00040820733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968437,0.00044253707,0.00067616074,0.00040905108,0.0013306017,0.00029797197],"domain_scores_gemma":[0.9967446,0.00046493643,0.00007844862,0.001087738,0.0015561398,0.00006816487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011952239,0.00014368084,0.00043456018,0.0011300199,0.00078784337,0.0001037838,0.0010906814,0.00004572105,0.00029912597],"category_scores_gemma":[0.000352392,0.00014216335,0.00012327849,0.004956095,0.00024835352,0.00076613156,0.00062897516,0.00037981643,0.000007476451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000038518615,0.0003497831,0.0000026919167,0.000009652283,0.00013690013,0.0000012560022,0.0035645429,0.653452,0.00093235594,0.3414443,0.000018180011,0.00008451486],"study_design_scores_gemma":[0.00032464424,0.00016057206,0.000011062493,0.000009321041,0.00003959747,0.000003651738,0.0015432818,0.9868362,0.003530399,0.0074069775,0.000012915007,0.00012136218],"about_ca_topic_score_codex":0.00014848277,"about_ca_topic_score_gemma":0.000074037045,"teacher_disagreement_score":0.4757163,"about_ca_system_score_codex":0.00015802117,"about_ca_system_score_gemma":0.0003432021,"threshold_uncertainty_score":0.6059534},"labels":[],"label_agreement":null},{"id":"W4317633855","doi":"10.2514/6.2023-2097","title":"Thermal Packaging Optimization of a UAM Nacelle Using a Dynamic Acceleration Methodology","year":2023,"lang":"en","type":"article","venue":"AIAA SCITECH 2023 Forum","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Nacelle; Acceleration; Component (thermodynamics); Computer science; Finite element method; Multidisciplinary design optimization; Software; Mechanical engineering; Engineering; Structural engineering; Physics; Thermodynamics","score_opus":0.056703414930786976,"score_gpt":0.33732921558385237,"score_spread":0.2806258006530654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317633855","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":[0.0059485137,0.000047393332,0.9917402,0.0007142728,0.0005288663,0.00034872693,0.000007983628,0.00042187588,0.00024216318],"genre_scores_gemma":[0.09853805,0.000032826963,0.900751,0.00012524326,0.000024824367,0.000029518682,0.000030429856,0.000032912187,0.00043518338],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981459,0.00020987558,0.0003805026,0.00050264783,0.00030585233,0.00045524497],"domain_scores_gemma":[0.9986701,0.00024748844,0.00026731569,0.0004922561,0.00025693965,0.00006588412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006657568,0.00018464864,0.0002576729,0.0005531997,0.0002510582,0.00007839316,0.000549205,0.00011651313,0.000037875947],"category_scores_gemma":[0.00023452999,0.00019217008,0.00008287856,0.0025712578,0.00007742185,0.0009568157,0.00046368112,0.00015131528,0.000034183005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006230482,0.000021045087,0.00011157557,0.000010294447,0.000017224267,0.0000059048825,0.00033395545,0.9584315,0.027125053,0.0012085177,0.00004318986,0.012685503],"study_design_scores_gemma":[0.00038614272,0.000050239334,0.00020498341,0.00002317783,0.000007333308,0.000008294305,0.00024870684,0.988417,0.009311375,0.0010946866,0.000059754486,0.00018828221],"about_ca_topic_score_codex":0.00003390858,"about_ca_topic_score_gemma":0.000013763771,"teacher_disagreement_score":0.092589535,"about_ca_system_score_codex":0.00011982465,"about_ca_system_score_gemma":0.00009901599,"threshold_uncertainty_score":0.7836468},"labels":[],"label_agreement":null},{"id":"W4319597796","doi":"10.3390/a16020092","title":"A Hybrid Direct Search and Model-Based Derivative-Free Optimization Method with Dynamic Decision Processing and Application in Solid-Tank Design","year":2023,"lang":"en","type":"article","venue":"Algorithms","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Kelowna General Hospital; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Benchmark (surveying); Computer science; Robustness (evolution); Mathematical optimization; Derivative (finance); Mathematics","score_opus":0.01851464791214632,"score_gpt":0.3200928443406451,"score_spread":0.3015781964284988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319597796","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":[0.00071281247,0.000116947536,0.9977547,0.00023949883,0.000023660645,0.00079299597,0.000007278452,0.00032339533,0.00002869209],"genre_scores_gemma":[0.061473507,0.00008674833,0.938078,0.00007239935,0.000009449853,0.00019261408,0.00001896568,0.000039258477,0.000029082446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788874,0.00017660137,0.00028738944,0.000877305,0.0004011422,0.000368841],"domain_scores_gemma":[0.99856347,0.0004477564,0.00011189436,0.00047460216,0.00028210043,0.00012020276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083019765,0.0002472323,0.00028437338,0.00052439113,0.0002466663,0.00019254207,0.00040214777,0.00006867917,7.2675505e-7],"category_scores_gemma":[0.00016699033,0.00022793443,0.000018175147,0.0016086027,0.00010474821,0.0008992864,0.00026284007,0.0001711991,0.0000020977077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002389749,0.0000272706,0.000036850957,0.000013560559,0.000004722743,0.000012286617,0.00029372566,0.7294079,0.00016464914,0.000036635913,0.000002023912,0.26997647],"study_design_scores_gemma":[0.0013693838,0.00009503284,0.0004653612,0.00007272161,0.0000074340965,0.000026916894,0.000037857015,0.9940026,0.0015999045,0.0020388553,0.0000025514519,0.0002814176],"about_ca_topic_score_codex":0.000023431092,"about_ca_topic_score_gemma":0.000014398679,"teacher_disagreement_score":0.26969507,"about_ca_system_score_codex":0.00013906934,"about_ca_system_score_gemma":0.00017302437,"threshold_uncertainty_score":0.9294896},"labels":[],"label_agreement":null},{"id":"W4320916811","doi":"10.1201/9781003399759-88","title":"Comparison of metaheuristic algorithms and constraint handling approaches for multi-objective optimization of a tanker","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Metaheuristic; Computer science; Mathematical optimization; Constraint (computer-aided design); Parallel metaheuristic; Algorithm; Engineering; Mathematics; Meta-optimization; Mechanical engineering","score_opus":0.13241466009339742,"score_gpt":0.33189028492600703,"score_spread":0.1994756248326096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320916811","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":[7.716601e-7,0.00034206526,0.98831165,0.000026451205,0.0002737756,0.0014176873,0.00014781237,0.00017416061,0.009305658],"genre_scores_gemma":[0.0011831038,0.00010596032,0.9741631,0.0000124943745,0.000037047255,0.00007339585,0.0000799286,0.000084256106,0.024260677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99746317,0.000038771195,0.0009763651,0.00087147625,0.00038130785,0.0002688967],"domain_scores_gemma":[0.9968448,0.0006470442,0.0010850913,0.00048657882,0.0008300584,0.00010639682],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003638075,0.00046571635,0.001140817,0.00048538198,0.0001100797,0.00005024456,0.00042801854,0.0003180348,0.000016321828],"category_scores_gemma":[0.00028496943,0.00045221086,0.00020602495,0.0001780302,0.00042398853,0.00027382007,0.00031208506,0.00024237426,0.0000024033964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003365356,0.00015722973,0.000024404424,0.00032788597,0.0004938466,0.0000032538142,0.0014523884,0.6826171,0.000023863924,0.27358586,0.000028577882,0.04125193],"study_design_scores_gemma":[0.001144365,0.00019391414,0.000016987522,0.0001630505,0.0001212799,0.000006830373,0.00019814524,0.9933605,0.000829207,0.0034730039,0.00007278077,0.00041991367],"about_ca_topic_score_codex":0.000009915745,"about_ca_topic_score_gemma":0.0000086722575,"teacher_disagreement_score":0.3107434,"about_ca_system_score_codex":0.00007749563,"about_ca_system_score_gemma":0.00015733627,"threshold_uncertainty_score":0.999793},"labels":[],"label_agreement":null},{"id":"W4321214511","doi":"10.1016/j.swevo.2023.101262","title":"Cooperative coevolutionary multi-guide particle swarm optimization algorithm for large-scale multi-objective optimization problems","year":2023,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Metaheuristic; Multi-swarm optimization; Particle swarm optimization; Benchmark (surveying); Mathematical optimization; Imperialist competitive algorithm; Optimization problem; Metric (unit); Scalability; Heuristic; Parallel metaheuristic; Continuous optimization; Scale (ratio); Algorithm; Artificial intelligence; Mathematics","score_opus":0.02114357018829575,"score_gpt":0.28732133859146136,"score_spread":0.2661777684031656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321214511","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":[0.0003379506,0.0004269791,0.9945415,0.0004854031,0.00072821183,0.002058529,0.00020617638,0.0011605961,0.00005465176],"genre_scores_gemma":[0.0127046285,0.00029191523,0.98368645,0.00019772604,0.00014607416,0.0006165913,0.0013162853,0.000068676505,0.000971672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966105,0.00025726706,0.0007314056,0.0011910686,0.0004725707,0.00073718326],"domain_scores_gemma":[0.9972316,0.0003554756,0.0003448726,0.00032630487,0.0014955634,0.00024615225],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005756352,0.00044644024,0.00039118217,0.0004216365,0.0012652001,0.00017837535,0.00033987898,0.0002102329,0.000010305805],"category_scores_gemma":[0.00020338316,0.00048810936,0.00012471793,0.001949429,0.0001525343,0.0021504522,0.00032125934,0.00020888244,0.000047386668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020920848,0.00038967712,0.00022021512,0.000029899362,0.000052889955,0.0000045927454,0.0017620675,0.9897999,0.000048058424,0.00054260466,0.000763068,0.0063661025],"study_design_scores_gemma":[0.0040908707,0.00024446825,0.0028278837,0.000047254656,0.000029860354,0.000034659584,0.0007286619,0.9904424,0.0001729413,0.00049390015,0.0003119734,0.0005751184],"about_ca_topic_score_codex":0.000028478416,"about_ca_topic_score_gemma":0.000014145391,"teacher_disagreement_score":0.012366679,"about_ca_system_score_codex":0.00042132873,"about_ca_system_score_gemma":0.00026078682,"threshold_uncertainty_score":0.99975705},"labels":[],"label_agreement":null},{"id":"W4321435904","doi":"10.1007/s00521-023-08332-3","title":"Multi-objective fitness-dependent optimizer algorithm","year":2023,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fanshawe College","funders":"","keywords":"Computer science; Benchmark (surveying); Sorting; Evolutionary algorithm; Mathematical optimization; Test suite; Particle swarm optimization; Algorithm; Genetic algorithm; Fitness function; Variety (cybernetics); Domain (mathematical analysis); Computation; Test case; Machine learning; Artificial intelligence; Mathematics","score_opus":0.022267228978825993,"score_gpt":0.3027546914269665,"score_spread":0.2804874624481405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321435904","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":[0.0008666855,0.00007287213,0.9965259,0.0004358122,0.00017872256,0.00051285315,0.000014019485,0.0011421599,0.00025098064],"genre_scores_gemma":[0.111373685,0.000054070697,0.8870656,0.00029807445,0.00018328307,0.00017932297,0.00002942468,0.000030777745,0.0007857722],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984136,0.0000627293,0.00025315004,0.0006983094,0.00021508413,0.00035712856],"domain_scores_gemma":[0.9988596,0.00024066896,0.0001231012,0.00045242973,0.00018457127,0.00013965972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018185463,0.0002000233,0.00018736882,0.00018166778,0.0005906993,0.00016791074,0.00048201508,0.00006434626,0.0000034588313],"category_scores_gemma":[0.000035385052,0.00019993029,0.000052744173,0.0011055383,0.000085456624,0.00029232888,0.000504892,0.0002223908,0.00009935296],"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":[0.000002013834,0.00011673254,0.00020553214,0.00001252758,0.000025756415,0.000013318487,0.0006955279,0.25156096,0.0005572611,0.0036041944,0.00015985723,0.7430463],"study_design_scores_gemma":[0.00052929117,0.000021548749,0.0030133415,0.0000070233577,0.0000052336836,0.000032535187,0.00010220517,0.9944696,0.00031595485,0.0005726212,0.00070327276,0.00022736045],"about_ca_topic_score_codex":0.000014324292,"about_ca_topic_score_gemma":0.0000015091217,"teacher_disagreement_score":0.74290866,"about_ca_system_score_codex":0.000043579723,"about_ca_system_score_gemma":0.000028573464,"threshold_uncertainty_score":0.81529206},"labels":[],"label_agreement":null},{"id":"W4321599248","doi":"10.1007/s43069-022-00180-6","title":"A General Mathematical Framework for Constrained Mixed-variable Blackbox Optimization Problems with Meta and Categorical Variables","year":2023,"lang":"en","type":"article","venue":"Operations Research Forum","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Institut de Valorisation des Données; Hydro-Québec; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Categorical variable; Computer science; Notation; Mathematical optimization; Optimization problem; Variable (mathematics); Continuous optimization; Context (archaeology); Flexibility (engineering); Artificial intelligence; Machine learning; Algorithm; Mathematics; Multi-swarm optimization","score_opus":0.07086614826535843,"score_gpt":0.35872397314201365,"score_spread":0.28785782487665523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321599248","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":[0.000052054434,0.000049450897,0.9928799,0.004550017,0.00006631016,0.0015666158,0.000037594866,0.00025315073,0.0005448931],"genre_scores_gemma":[0.004531019,0.00006228236,0.99125034,0.000087469714,0.000053701337,0.0016505776,0.0000946369,0.000037552476,0.0022323974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974763,0.0002178597,0.00031890502,0.00066235085,0.00057156355,0.0007530208],"domain_scores_gemma":[0.9974421,0.00081914465,0.000034955385,0.0005255126,0.0009577597,0.00022049095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012272967,0.0002054589,0.00030955486,0.000383231,0.0010042855,0.000726713,0.00045662237,0.00013957158,0.0000815658],"category_scores_gemma":[0.0011414414,0.00015901063,0.00004865419,0.0022750916,0.00028678204,0.00093127583,0.00033274223,0.00031335902,0.000028128768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005084732,0.00005112502,0.000003966135,0.000015929467,0.000078728015,0.0000031041943,0.0001472444,0.5126109,0.00005606578,0.48638257,0.0003176774,0.00032758267],"study_design_scores_gemma":[0.00051551475,0.00020647173,0.000002671257,0.0000203797,0.000029347413,0.000036776728,0.00021047369,0.92769784,0.00017960698,0.07007028,0.0008418868,0.00018874099],"about_ca_topic_score_codex":0.000024972174,"about_ca_topic_score_gemma":0.000021213822,"teacher_disagreement_score":0.4163123,"about_ca_system_score_codex":0.00008961015,"about_ca_system_score_gemma":0.00038171426,"threshold_uncertainty_score":0.77242535},"labels":[],"label_agreement":null},{"id":"W4353034571","doi":"10.1016/j.ijheatfluidflow.2023.109129","title":"Optimization of a double-intake squirrel cage fan using OpenFoam and metamodels","year":2023,"lang":"en","type":"article","venue":"International Journal of Heat and Fluid Flow","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Trimming; Latin hypercube sampling; Kriging; Impeller; Squirrel-cage rotor; Computer science; Rotor (electric); Optimal design; Computational fluid dynamics; Sampling (signal processing); Coupling (piping); Engineering design process; Automotive engineering; Mechanical engineering; Mathematics; Aerospace engineering; Engineering; Voltage; Electrical engineering","score_opus":0.032926945430452464,"score_gpt":0.3111025548807021,"score_spread":0.27817560945024966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353034571","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":[0.021519396,0.00026652426,0.97685057,0.00044737107,0.00067104027,0.000091341935,0.000009771893,0.000021157115,0.00012285371],"genre_scores_gemma":[0.35994974,0.0006601426,0.6390249,0.00011175416,0.00015137947,0.0000021493202,0.000007526898,0.000015420386,0.000076962664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883324,0.000035035446,0.0004277013,0.00017161698,0.00041033694,0.00012205059],"domain_scores_gemma":[0.9988957,0.00007075672,0.00016585957,0.00010689207,0.0006720302,0.00008872902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032964328,0.00011247795,0.00021307405,0.0004064303,0.00006384641,0.00012075755,0.0003660813,0.000044198678,0.000013300384],"category_scores_gemma":[0.000047636968,0.00010025173,0.00005842628,0.00027884124,0.00006120392,0.0011620298,0.00019892934,0.00010056078,0.0000011214187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007459035,0.000034025703,0.00022689675,0.000006634783,0.00009137442,0.000045331788,0.0006518673,0.9861262,0.0040738815,0.0014708458,0.000038244405,0.007160146],"study_design_scores_gemma":[0.001500244,0.00006506725,0.0002866445,0.00005711471,0.00001389304,0.00027861845,0.00006489724,0.99517256,0.0019847888,0.0003867463,0.000094201816,0.00009522261],"about_ca_topic_score_codex":0.000020472537,"about_ca_topic_score_gemma":0.0000018897126,"teacher_disagreement_score":0.33843035,"about_ca_system_score_codex":0.000054717646,"about_ca_system_score_gemma":0.00007315872,"threshold_uncertainty_score":0.4088147},"labels":[],"label_agreement":null},{"id":"W4361285183","doi":"10.1002/qre.3331","title":"Is designed data collection still relevant in the big data era? – A discussion","year":2023,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Robustness (evolution); Data collection; Variance (accounting); Data science; Big data; Research design; Management science; Statistics; Data mining; Mathematics; Engineering","score_opus":0.09944997944038245,"score_gpt":0.34847735036604677,"score_spread":0.24902737092566432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361285183","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":[0.0049496586,0.00001869362,0.9800563,0.013337783,0.001058018,0.0002377499,0.00011823587,0.00018071638,0.000042885997],"genre_scores_gemma":[0.59811157,0.0004639523,0.3982577,0.0007680531,0.00042056452,0.00007967354,0.0011552466,0.00003348099,0.00070975866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825567,0.0001466779,0.00034580097,0.0006652902,0.000417064,0.00016947057],"domain_scores_gemma":[0.9978338,0.00053025165,0.00007331997,0.0014299911,0.000090582376,0.000042079126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022641183,0.0001215098,0.00012579892,0.00015884753,0.00008490474,0.00013436523,0.0017971268,0.00006015566,0.0000054225447],"category_scores_gemma":[0.0017776982,0.00007900716,0.000019908626,0.0007401859,0.000036423662,0.00079351827,0.0011167962,0.00022414968,0.0000092965465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002121163,0.0012913634,0.014684266,0.0004437358,0.0002119701,0.00007526186,0.023311239,0.686403,0.0024115846,0.026670743,0.014434664,0.2298501],"study_design_scores_gemma":[0.00030770566,0.000014242793,0.049378846,0.00002548184,0.000002387215,0.0000066051784,0.00007552216,0.9425597,0.000033918168,0.00087423617,0.0066002933,0.00012103098],"about_ca_topic_score_codex":0.00009188912,"about_ca_topic_score_gemma":0.000018140383,"teacher_disagreement_score":0.5931619,"about_ca_system_score_codex":0.00008234441,"about_ca_system_score_gemma":0.000058890273,"threshold_uncertainty_score":0.33395386},"labels":[],"label_agreement":null},{"id":"W4362504276","doi":"10.1007/978-3-030-54621-2_737-1","title":"Inverse Optimization","year":2022,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science","score_opus":0.011426362640778473,"score_gpt":0.22846590064887615,"score_spread":0.21703953800809767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362504276","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":[5.994264e-8,0.00012663,0.5666336,0.00008665012,0.0007196452,0.0004305634,0.00002712744,0.0002059894,0.43176973],"genre_scores_gemma":[0.0000035192788,0.005419197,0.79380405,0.0001250943,0.0001311509,0.00005149964,0.0005063451,0.00011632945,0.1998428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968998,0.000076588185,0.0008904544,0.00094981334,0.0008649737,0.00031837667],"domain_scores_gemma":[0.99683565,0.0001442185,0.0012043572,0.0010899233,0.0005731488,0.0001527174],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024957597,0.0005483091,0.00059672305,0.0007113001,0.00024070611,0.00006188193,0.0011132702,0.0003367713,0.004059327],"category_scores_gemma":[0.00016692473,0.0006563343,0.00021264084,0.00039674755,0.000121404744,0.0013798423,0.00067412626,0.00048496205,0.000036649217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009935157,0.00004458406,0.000002928934,0.000026423731,0.000043121476,0.000014292073,0.00022788902,0.9348626,5.252424e-7,0.059683975,0.0010875383,0.0039961734],"study_design_scores_gemma":[0.0005540458,0.00011555535,0.0000013660838,0.00004391011,0.00004712717,0.000014771559,0.0000103679495,0.9073703,0.000007811542,0.0013329721,0.089903265,0.00059846474],"about_ca_topic_score_codex":0.00000802622,"about_ca_topic_score_gemma":0.0000022683787,"teacher_disagreement_score":0.23192693,"about_ca_system_score_codex":0.00036563448,"about_ca_system_score_gemma":0.0003471693,"threshold_uncertainty_score":0.9995888},"labels":[],"label_agreement":null},{"id":"W4362504304","doi":"10.1007/978-3-030-54621-2_736-1","title":"Data-Driven Inverse Optimization","year":2022,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Inverse; Computer science; Mathematics; Geometry","score_opus":0.02618374193444844,"score_gpt":0.2597484799584814,"score_spread":0.23356473802403294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362504304","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":[5.7281678e-8,0.00016801713,0.66077495,0.000116189454,0.00086751074,0.0005649993,0.00025312,0.00024922792,0.33700594],"genre_scores_gemma":[0.000002593541,0.008958727,0.8876108,0.00012783901,0.0001864528,0.00003852225,0.0041212975,0.00013691769,0.09881685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99619937,0.00009715505,0.0009974231,0.0013903432,0.00097163225,0.0003440769],"domain_scores_gemma":[0.9951635,0.00017531287,0.0013796801,0.0025602013,0.0005543529,0.00016695916],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003123734,0.0005874927,0.0006620146,0.00067096134,0.0002626873,0.00008261218,0.0027058444,0.0003420332,0.003109467],"category_scores_gemma":[0.00024174544,0.0006990672,0.00014221312,0.0004145078,0.00014190098,0.0024789406,0.0021674358,0.00053952663,0.000040075727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010631769,0.000054530847,0.0000033887789,0.000030156236,0.00006436451,0.000018071585,0.00016647908,0.9679977,3.854662e-7,0.02411692,0.002651374,0.0048859743],"study_design_scores_gemma":[0.00051967497,0.000085056054,0.0000012254535,0.00004380076,0.00006618034,0.000013803328,0.000009972334,0.8706274,0.000001892341,0.00049689115,0.12756471,0.0005693618],"about_ca_topic_score_codex":0.000009928414,"about_ca_topic_score_gemma":0.000006946004,"teacher_disagreement_score":0.2381891,"about_ca_system_score_codex":0.00032125745,"about_ca_system_score_gemma":0.00045858376,"threshold_uncertainty_score":0.99954605},"labels":[],"label_agreement":null},{"id":"W4362704594","doi":"10.4018/ijoris.321119","title":"A Computational Comparison of Three Nature-Inspired, Population-Based Metaheuristic Algorithms for Modelling-to-Generate Alternatives","year":2023,"lang":"en","type":"article","venue":"International Journal of Operations Research and Information Systems","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Benchmark (surveying); Mathematical optimization; Construct (python library); Set (abstract data type); Population; Computer science; Metaheuristic; Algorithm; Mathematics","score_opus":0.1021787136612811,"score_gpt":0.42633645130629017,"score_spread":0.32415773764500905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362704594","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":[0.014608303,0.00011044945,0.9823924,0.0010067219,0.0009898292,0.0006377869,0.0001503335,0.00003745744,0.000066773944],"genre_scores_gemma":[0.7682512,0.00002473166,0.2312069,0.00006734487,0.00015400385,0.00007382644,0.00017657294,0.0000090254625,0.000036406047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996704,0.0001251631,0.0012141784,0.00017908195,0.0015517911,0.00022577874],"domain_scores_gemma":[0.99003935,0.0005878834,0.00037099305,0.00017539256,0.008664119,0.00016226806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015269351,0.0001418338,0.00030626598,0.0019919001,0.0002826804,0.0006282089,0.0007844139,0.00008604005,0.0000031396858],"category_scores_gemma":[0.0007008532,0.0001272589,0.0000816609,0.0009803864,0.000060362036,0.0031942732,0.00012333425,0.0002846867,0.000015018952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005152873,0.00005875395,0.00049270375,0.000023318626,0.0000879236,0.0000015688223,0.0005922298,0.94853365,0.000030244755,0.046547554,0.00018044766,0.0034000715],"study_design_scores_gemma":[0.0010009037,0.00028593367,0.0011349443,0.00008680771,0.0000044094722,0.000016489237,0.00022465987,0.99473876,0.00029071927,0.0010217504,0.001078527,0.00011611652],"about_ca_topic_score_codex":0.000082723425,"about_ca_topic_score_gemma":0.000009118256,"teacher_disagreement_score":0.7536429,"about_ca_system_score_codex":0.00017716628,"about_ca_system_score_gemma":0.000329204,"threshold_uncertainty_score":0.60578334},"labels":[],"label_agreement":null},{"id":"W4366085886","doi":"10.1016/j.apenergy.2023.121132","title":"Robust metamodel-based simulation-optimization approaches for designing hybrid renewable energy systems","year":2023,"lang":"en","type":"article","venue":"Applied Energy","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Metamodeling; Computer science; Renewable energy; Reliability (semiconductor); Mathematics; Power (physics); Engineering","score_opus":0.06428875591203222,"score_gpt":0.2419704108847037,"score_spread":0.17768165497267147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366085886","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":[0.0000027769145,0.00007745024,0.996251,0.00004548715,0.0003310579,0.0002935999,0.000009447775,0.00099762,0.001991565],"genre_scores_gemma":[0.29494607,0.000013622708,0.70243734,0.00017428983,0.00013948296,0.00091145036,0.00031673777,0.000072957286,0.0009880726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977942,0.00007284677,0.00042473985,0.00084399455,0.00035200472,0.0005122324],"domain_scores_gemma":[0.99822015,0.00052827544,0.00025888128,0.00063734304,0.00022185023,0.00013348374],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031233797,0.00030821163,0.00033422108,0.00040358398,0.0003786061,0.00022942363,0.00058124395,0.00010635122,0.0000045580678],"category_scores_gemma":[0.000055492572,0.000327284,0.00009534263,0.0012564971,0.000044413242,0.00045277178,0.00012904443,0.00005611292,0.0000059995086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015084454,0.000042808617,8.113588e-7,0.000015817439,0.000032707463,0.0000018947403,0.000031267973,0.958364,0.00008494266,0.036501214,0.00029823257,0.004611205],"study_design_scores_gemma":[0.0008100204,0.000032277843,7.0945174e-7,0.00001077344,0.00001718064,0.0000012844612,0.000034152497,0.989065,0.005298462,0.0020759096,0.002276177,0.00037804907],"about_ca_topic_score_codex":0.00013834215,"about_ca_topic_score_gemma":0.000011297285,"teacher_disagreement_score":0.2949433,"about_ca_system_score_codex":0.00014037658,"about_ca_system_score_gemma":0.00012607808,"threshold_uncertainty_score":0.9999179},"labels":[],"label_agreement":null},{"id":"W4376277151","doi":"10.1287/ijoc.2022.0090","title":"Learning for Spatial Branching: An Algorithm Selection Approach","year":2023,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Branching (polymer chemistry); Algorithm; Christian ministry; Artificial intelligence; Machine learning; Context (archaeology)","score_opus":0.021761022975442072,"score_gpt":0.2951156799378986,"score_spread":0.27335465696245653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376277151","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":[0.0040897126,0.0000043494047,0.993479,0.00007286705,0.00095052714,0.0002535706,7.4094714e-7,0.0006510469,0.00049814215],"genre_scores_gemma":[0.1452649,0.0000072837056,0.85336035,0.00021557699,0.0009203367,0.000007509389,0.000016814196,0.00003322274,0.00017401848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814385,0.00007069763,0.00047607507,0.00034438726,0.00043647684,0.00052851887],"domain_scores_gemma":[0.99862444,0.00021692221,0.0004248639,0.00016795768,0.00037242452,0.00019340788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010917936,0.00021284194,0.0002234025,0.00049734046,0.0011653295,0.00059828284,0.0005623228,0.00008633602,0.000002650867],"category_scores_gemma":[0.00026066924,0.00018962797,0.00011896065,0.00096060976,0.000022864726,0.0015895219,0.00014297251,0.0006905284,0.000027518536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061037454,0.000022077036,0.00006740605,0.0000037812808,0.000010671239,0.0000028811237,0.00070604536,0.5257746,0.000023515662,0.0003804554,0.000022656463,0.4729798],"study_design_scores_gemma":[0.0009484152,0.0005297323,0.00093743665,0.000027819991,0.0000031967372,0.00024492474,0.00016384463,0.99433637,0.00024636625,0.00079144276,0.0015199905,0.0002504719],"about_ca_topic_score_codex":0.0000056910376,"about_ca_topic_score_gemma":6.203169e-7,"teacher_disagreement_score":0.47272933,"about_ca_system_score_codex":0.00016746926,"about_ca_system_score_gemma":0.00010717522,"threshold_uncertainty_score":0.896289},"labels":[],"label_agreement":null},{"id":"W4376618927","doi":"10.1007/s12652-023-04630-9","title":"Deep reinforcement learning-based framework for constrained any-objective optimization","year":2023,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Advanced Multi-Objective Optimization Algorithms","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":"University of Victoria","funders":"","keywords":"Reinforcement learning; Mathematical optimization; Optimization problem; Computer science; Test functions for optimization; Multi-objective optimization; Continuous optimization; Vector optimization; Constrained optimization; Bilevel optimization; Random optimization; Metaheuristic; Multi-swarm optimization; Artificial intelligence; Mathematics","score_opus":0.030893696484508786,"score_gpt":0.30807106873541024,"score_spread":0.27717737225090144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376618927","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":[0.0006251955,0.000074473624,0.9980664,0.00017759358,0.00053841365,0.00033671796,3.368855e-7,0.00012979135,0.000051075887],"genre_scores_gemma":[0.3987183,0.000060315888,0.6008866,0.0001661958,0.000117215226,0.0000043907144,0.0000041079793,0.000014965757,0.000027927646],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998167,0.00008442176,0.00072180916,0.00031216396,0.00036264744,0.00035195006],"domain_scores_gemma":[0.9970424,0.0008450196,0.00084936136,0.00017002142,0.00096462807,0.00012855415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008317451,0.00019867481,0.00032795334,0.00045973473,0.00047323346,0.00022896525,0.00043256264,0.00008190491,0.000016074986],"category_scores_gemma":[0.0007952521,0.00019019509,0.00013483432,0.0007201102,0.00009545629,0.00043397126,0.00014392451,0.00033993195,0.0000049902774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040697112,0.000034872588,0.00004317349,0.000020387786,0.000036541234,0.0000112238495,0.0018846083,0.964571,0.000033695535,0.016084045,0.000008485577,0.017231246],"study_design_scores_gemma":[0.00055043685,0.0006333881,0.000053345255,0.00017298573,0.000016500344,0.00002634762,0.00083296513,0.9901472,0.0016464016,0.0056482414,0.00007151687,0.00020068877],"about_ca_topic_score_codex":0.0000017708943,"about_ca_topic_score_gemma":4.920826e-7,"teacher_disagreement_score":0.3980931,"about_ca_system_score_codex":0.0001178124,"about_ca_system_score_gemma":0.00011361779,"threshold_uncertainty_score":0.77559304},"labels":[],"label_agreement":null},{"id":"W4376640789","doi":"10.1115/1.4062548","title":"A Dimension Selection-Based Constrained Multi-Objective Optimization Algorithm Using a Combination of Artificial Intelligence Methods","year":2023,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Benchmark (surveying); Mathematical optimization; Dimension (graph theory); Computer science; Artificial neural network; Selection (genetic algorithm); Engineering optimization; Optimization problem; Algorithm; Engineering design process; Mathematics; Artificial intelligence; Engineering","score_opus":0.08441302958133033,"score_gpt":0.36912135011791164,"score_spread":0.2847083205365813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376640789","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":[0.0000694649,0.000020393783,0.99867374,0.00012405815,0.0006193432,0.00038749824,0.0000026956293,0.000099870645,0.0000029591245],"genre_scores_gemma":[0.019358344,0.000016342314,0.9804923,0.000048927188,0.000046198118,0.0000067009005,0.0000021070398,0.000022847664,0.000006244247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967651,0.0010506016,0.0010153163,0.00033073654,0.0005682258,0.00027003643],"domain_scores_gemma":[0.99553263,0.0012404429,0.0010899665,0.00019640512,0.0017982621,0.00014228265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027838862,0.00020703215,0.0004449934,0.00075388944,0.0001618639,0.000064016596,0.00042308285,0.00015636184,0.000017497554],"category_scores_gemma":[0.0016524264,0.00019582981,0.00017112806,0.0024795404,0.00007773854,0.0006473213,0.00008541711,0.00031276338,0.0000034015818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055652352,0.00024493586,8.2091873e-7,0.000006807316,0.000037696962,0.000014223477,0.00013564479,0.87050974,0.029221248,0.0021085634,0.0000041035346,0.09766056],"study_design_scores_gemma":[0.00053276354,0.0005195798,0.000006902993,0.00006632497,0.00003006487,0.000053632073,0.00009628524,0.82485217,0.167786,0.005904138,0.0000010230111,0.00015113401],"about_ca_topic_score_codex":0.0000056451577,"about_ca_topic_score_gemma":4.2895843e-7,"teacher_disagreement_score":0.13856477,"about_ca_system_score_codex":0.00025266674,"about_ca_system_score_gemma":0.0004483975,"threshold_uncertainty_score":0.79857075},"labels":[],"label_agreement":null},{"id":"W4377090272","doi":"10.1016/j.swevo.2023.101325","title":"Flow measurement data quality improvement-oriented optimal flow sensor configuration","year":2023,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Professional Engineers Ontario","funders":"National Natural Science Foundation of China","keywords":"Computer science; Observability; Initialization; Mathematical optimization; Redundancy (engineering); Evolutionary algorithm; Population; Artificial intelligence; Mathematics","score_opus":0.05776581992754409,"score_gpt":0.31222624011525335,"score_spread":0.25446042018770926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377090272","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":[0.0036015082,0.000111195055,0.99326587,0.0009939079,0.00076021353,0.00046610853,0.00008479934,0.00058245135,0.00013391927],"genre_scores_gemma":[0.17669845,0.000077754004,0.8208764,0.00018219156,0.00019311138,0.000045634297,0.0017265971,0.000022321117,0.00017752538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997555,0.00016399453,0.00043742332,0.00079052674,0.0007434603,0.0003095554],"domain_scores_gemma":[0.9984132,0.000106817875,0.00018636968,0.0005146115,0.00065703277,0.00012193955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007770351,0.00020286163,0.00018423305,0.00020596285,0.00042166206,0.00010436915,0.00034322927,0.00007036866,0.000008000749],"category_scores_gemma":[0.00020426449,0.00021564253,0.000035227746,0.0007878283,0.000071255396,0.0014131926,0.00040906784,0.000121095465,0.00008631569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026335312,0.000081270395,0.00007808779,0.000025921094,0.000035833495,0.0000060361476,0.00042038577,0.8948729,0.0009045134,0.0009886248,0.0024103657,0.10014974],"study_design_scores_gemma":[0.0009313918,0.00009089665,0.009961103,0.000015810672,0.000009394846,0.000008861771,0.0001821168,0.9863256,0.00021563267,0.00091335754,0.001098432,0.00024737406],"about_ca_topic_score_codex":0.000026665532,"about_ca_topic_score_gemma":0.000005321758,"teacher_disagreement_score":0.17309694,"about_ca_system_score_codex":0.0002024555,"about_ca_system_score_gemma":0.00014783531,"threshold_uncertainty_score":0.8793647},"labels":[],"label_agreement":null},{"id":"W4377864823","doi":"10.48550/arxiv.2305.12871","title":"MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Safran Electronics (Canada)","funders":"","keywords":"Morphing; Computer science; Polygon mesh; Kriging; Artificial neural network; Machine learning; Artificial intelligence; Gaussian process; Deep learning; Parameterized complexity; Graph; Curse of dimensionality; Algorithm; Gaussian; Mathematical optimization; Theoretical computer science; Mathematics","score_opus":0.08206313325169935,"score_gpt":0.27648527676735,"score_spread":0.19442214351565068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377864823","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":[0.008431993,0.000006864051,0.98923975,0.00010980912,0.0002513753,0.0013751296,0.000048331804,0.00046284168,0.00007388399],"genre_scores_gemma":[0.62575203,0.000008328797,0.37376693,0.000019538269,0.000036399266,0.00002147129,0.00006704095,0.000048453174,0.0002798201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99625385,0.0005429872,0.0004535276,0.0019580699,0.00027612495,0.00051545387],"domain_scores_gemma":[0.9952621,0.0018505404,0.0009068751,0.0010988858,0.0006601878,0.00022145055],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011640276,0.000539787,0.00094026834,0.0008452821,0.00025787624,0.000093432594,0.0014899137,0.00035227454,0.0000078069215],"category_scores_gemma":[0.0010800271,0.0005197383,0.0004617761,0.0026830123,0.00015038624,0.000453858,0.0012503342,0.000999211,0.0000063465272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011253079,0.00033000656,0.00046563035,0.00042643564,0.000097793534,0.000019578987,0.00029060524,0.9922221,0.00051031925,0.0045412458,0.0000025981444,0.0009811571],"study_design_scores_gemma":[0.0016352184,0.00020531705,0.00029463734,0.00025000976,0.00009360176,0.0000014239001,0.000040214443,0.95331633,0.0013980011,0.04225614,0.000012959062,0.0004961723],"about_ca_topic_score_codex":0.00010154841,"about_ca_topic_score_gemma":0.0000052687,"teacher_disagreement_score":0.61732,"about_ca_system_score_codex":0.0003787803,"about_ca_system_score_gemma":0.00035532264,"threshold_uncertainty_score":0.9997254},"labels":[],"label_agreement":null},{"id":"W4378446702","doi":"10.1016/j.advengsoft.2023.103571","title":"SMT 2.0: A Surrogate Modeling Toolbox with a focus on hierarchical and mixed variables Gaussian processes","year":2023,"lang":"en","type":"article","venue":"Advances in Engineering Software","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":117,"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":"Office National d'études et de Recherches Aérospatiales; HORIZON EUROPE Framework Programme; University of California, San Diego; Glenn Research Center; European Commission; Polytechnique Montréal; National Aeronautics and Space Administration","keywords":"Computer science; Kriging; Surrogate model; Toolbox; Python (programming language); MIT License; Data mining; Machine learning; Software; Artificial intelligence; Programming language","score_opus":0.0080770724748144,"score_gpt":0.23167608308100854,"score_spread":0.22359901060619414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378446702","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":[0.0039548962,0.0005709153,0.99398005,0.00011949268,0.000154055,0.00020023559,0.00000671727,0.0009621026,0.000051541054],"genre_scores_gemma":[0.34766376,0.00040042674,0.65169096,0.000026627114,0.00003318313,0.000110911344,0.000006671988,0.00003926405,0.00002819689],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855125,0.000021117436,0.00020544598,0.0005571788,0.00024847288,0.00041653027],"domain_scores_gemma":[0.99906504,0.00041241353,0.0000465763,0.0002912911,0.000084928244,0.000099777484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013591861,0.0002390042,0.00022898911,0.0003228543,0.000085919775,0.00008054657,0.00032551173,0.00005746353,9.947076e-7],"category_scores_gemma":[0.0005503623,0.00021380896,0.000017481107,0.001532228,0.00003532827,0.0010577644,0.00011142138,0.00023808978,0.000004973016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001269185,0.000020438496,0.0004062721,0.000118988166,0.0000066458474,0.000053376385,0.00031445583,0.97535247,0.000008763766,0.0022779952,0.0000010132597,0.021426897],"study_design_scores_gemma":[0.0006021693,0.00009930351,0.00032667362,0.0003377523,0.0000028045026,0.000020674499,0.000035400415,0.99476683,0.00026818918,0.0030263225,0.00019696142,0.0003169218],"about_ca_topic_score_codex":0.0000057056113,"about_ca_topic_score_gemma":0.000023110239,"teacher_disagreement_score":0.34370887,"about_ca_system_score_codex":0.000059535952,"about_ca_system_score_gemma":0.00006196083,"threshold_uncertainty_score":0.8718876},"labels":[],"label_agreement":null},{"id":"W4378942269","doi":"10.48550/arxiv.2305.18352","title":"Multi-Objective Genetic Algorithm for Multi-View Feature Selection","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Jane ja Aatos Erkon Säätiö; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; Itä-Suomen Yliopisto; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Interpretability; Computer science; Feature selection; Benchmark (surveying); Feature (linguistics); Artificial intelligence; Machine learning; Selection (genetic algorithm); Data mining; Generalization; Generalizability theory; Genetic algorithm; Mathematics","score_opus":0.09182659539403061,"score_gpt":0.2447307976027236,"score_spread":0.152904202208693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378942269","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":[0.00023655444,0.00017049754,0.99420214,0.00007604405,0.0017986685,0.0020482948,0.00014982809,0.001284938,0.000033009448],"genre_scores_gemma":[0.007107331,0.00054138753,0.9808625,0.00010979046,0.00019980676,0.000048334354,0.000089214875,0.00012011128,0.010921514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995963,0.00024012827,0.00033297593,0.0025607827,0.00016682417,0.00073628506],"domain_scores_gemma":[0.99677515,0.0002185337,0.00053281634,0.0011436648,0.0010532705,0.0002765635],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026873586,0.0007104826,0.000650133,0.0006408462,0.00046982898,0.00019975033,0.0017916025,0.00066951,0.000009278365],"category_scores_gemma":[0.00016165906,0.0008597993,0.00044930665,0.0017192148,0.00013828141,0.00060696906,0.0016767555,0.00096476945,0.00013127118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019392304,0.00028329535,0.00030142066,0.000115705145,0.00027567623,0.000137408,0.00038116495,0.97083586,0.000040464285,0.0014316745,0.00030521964,0.025872717],"study_design_scores_gemma":[0.0019522673,0.00009645023,0.0030884543,0.000111370886,0.00010471252,0.000014257678,0.000086179425,0.9905446,0.00021583878,0.0023605009,0.0005378256,0.0008875312],"about_ca_topic_score_codex":0.00011537098,"about_ca_topic_score_gemma":0.0001296419,"teacher_disagreement_score":0.024985185,"about_ca_system_score_codex":0.00087845087,"about_ca_system_score_gemma":0.0004011678,"threshold_uncertainty_score":0.9993853},"labels":[],"label_agreement":null},{"id":"W4379260268","doi":"10.5267/j.dsl.2023.4.006","title":"MaOMFO: Many-objective moth flame optimizer using reference-point based non-dominated sorting mechanism for global optimization problems","year":2023,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multi-objective optimization; Mathematical optimization; Sorting; Optimization problem; Benchmark (surveying); Multi-swarm optimization; Particle swarm optimization; Computer science; Evolutionary algorithm; Convergence (economics); Pareto principle; Set (abstract data type); Derivative-free optimization; Metaheuristic; Mathematics; Algorithm","score_opus":0.034199022540272364,"score_gpt":0.3175204760569047,"score_spread":0.28332145351663235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379260268","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":[0.011742948,0.0000028062868,0.98411274,0.0010632909,0.000989377,0.0014123552,0.000026243948,0.0005107654,0.00013949885],"genre_scores_gemma":[0.105690666,0.0000069353505,0.89219654,0.0018240369,0.00004918226,0.00014630191,0.000020840622,0.00003737343,0.000028142711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946656,0.00009544947,0.00081931613,0.0017845535,0.0015007815,0.0011342659],"domain_scores_gemma":[0.99658173,0.00046172115,0.0005918077,0.0009837042,0.0010838542,0.00029718783],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019945982,0.00044133666,0.00044031153,0.00095460756,0.001075381,0.0007601646,0.0019956685,0.00013901171,0.000016114629],"category_scores_gemma":[0.0010938457,0.00041934074,0.00015843593,0.0067760604,0.00038180267,0.0028914255,0.00066629733,0.00022221362,0.000043446144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003124106,0.0000380197,0.000032462496,0.000007972939,0.000007623358,0.000020220961,0.00029452294,0.97064036,0.02112256,0.002961597,0.00006834869,0.0047750752],"study_design_scores_gemma":[0.0016216235,0.00008149604,0.00016953076,0.00010003522,0.000011438439,0.000021128919,0.00013493504,0.9818861,0.009997361,0.005464954,0.00001147746,0.0004999375],"about_ca_topic_score_codex":0.000029332847,"about_ca_topic_score_gemma":0.0000035587418,"teacher_disagreement_score":0.093947716,"about_ca_system_score_codex":0.00094368093,"about_ca_system_score_gemma":0.00036866503,"threshold_uncertainty_score":0.99982584},"labels":[],"label_agreement":null},{"id":"W4379386943","doi":"10.1017/jpr.2023.30","title":"Moderate deviations inequalities for Gaussian process regression","year":2023,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Gaussian process; Point process; Applied mathematics; Regression; Regression analysis; Inverse Gaussian distribution; Statistics; Large deviations theory; Gaussian; Mathematical analysis; Distribution (mathematics)","score_opus":0.04313765905341929,"score_gpt":0.3233364822857144,"score_spread":0.28019882323229506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379386943","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":[0.012699438,0.000014499001,0.9848585,0.00092617865,0.00022190428,0.00053195516,0.000007508937,0.00012385739,0.0006161673],"genre_scores_gemma":[0.5013173,0.000012344362,0.49826035,0.000099342884,0.00010790163,0.00009977702,0.000004838029,0.000014220706,0.00008390197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985653,0.00004521934,0.00056408107,0.00025849722,0.00033473133,0.00023212763],"domain_scores_gemma":[0.99813527,0.00023856941,0.0005286068,0.00029425177,0.0006998253,0.00010350641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010195092,0.00013649983,0.00026455667,0.0001757171,0.00020796894,0.00009292316,0.00047238535,0.000063666834,0.000004754995],"category_scores_gemma":[0.0003026613,0.00010478768,0.000088730085,0.000710213,0.00005127946,0.00062291714,0.000085707376,0.00016599274,0.0000062926597],"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":[0.00043080654,0.00038016564,0.0003015253,0.0003825665,0.00007698833,0.0000075311464,0.009431143,0.7230234,0.0008104484,0.21529438,0.0007442997,0.049116757],"study_design_scores_gemma":[0.0009763367,0.00010147748,0.0005727242,0.00004373587,0.00000957564,0.000009529058,0.00029885067,0.4038865,0.0062980843,0.58720696,0.0004096102,0.00018661481],"about_ca_topic_score_codex":3.9531795e-7,"about_ca_topic_score_gemma":0.0000014759836,"teacher_disagreement_score":0.4886179,"about_ca_system_score_codex":0.000117329786,"about_ca_system_score_gemma":0.00024126603,"threshold_uncertainty_score":0.42731175},"labels":[],"label_agreement":null},{"id":"W4379415007","doi":"10.1214/23-sts865a","title":"Comment: A Quarter Century of Methodological Research in Response-Adaptive Randomization","year":2023,"lang":"en","type":"article","venue":"Statistical Science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Econometrics; Randomization; Statistics; Computer science; Mathematics; History; Medicine; Randomized controlled trial; Archaeology","score_opus":0.18301209058970766,"score_gpt":0.45778046848013415,"score_spread":0.2747683778904265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379415007","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":[0.0010763076,0.000009538497,0.9955247,0.0022661272,0.00015429647,0.00035967812,0.000016818864,0.00007577759,0.0005167545],"genre_scores_gemma":[0.39743355,0.000016393213,0.6023893,0.00009836421,0.0000074785708,0.000033631084,0.0000022740162,0.0000038641588,0.000015153258],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995485,0.0019658518,0.00034761018,0.00060939224,0.0010311548,0.0005609831],"domain_scores_gemma":[0.9909456,0.00806685,0.00007350104,0.00034294813,0.00042407343,0.00014705233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012239044,0.00009539649,0.00022544105,0.0006861391,0.00020995605,0.00007108558,0.0008623017,0.00004565821,0.000023570898],"category_scores_gemma":[0.0070557827,0.0000792178,0.000021765498,0.0062461034,0.0011830708,0.0004904619,0.00046083934,0.00023910376,0.000055441407],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013434388,0.0002022061,0.00026393458,0.000012729733,0.0000047497215,0.000091823356,0.0027952688,0.011488021,0.004708337,0.93494314,0.00048924645,0.043657094],"study_design_scores_gemma":[0.0017409009,0.00029709985,0.022181954,0.000027235783,0.0000010844896,0.0000037600237,0.00042499835,0.9122732,0.0008420886,0.061810136,0.0002590722,0.00013842185],"about_ca_topic_score_codex":0.000022446613,"about_ca_topic_score_gemma":0.0000031155748,"teacher_disagreement_score":0.9007852,"about_ca_system_score_codex":0.00018288742,"about_ca_system_score_gemma":0.00022652444,"threshold_uncertainty_score":0.844694},"labels":[],"label_agreement":null},{"id":"W4379744505","doi":"10.1016/j.compind.2023.103957","title":"A similarity-assisted multi-fidelity approach to conceptual design space exploration","year":2023,"lang":"en","type":"article","venue":"Computers in Industry","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Horizon 2020; HORIZON EUROPE Framework Programme; Horizon 2020 Framework Programme; Cleansky; VINNOVA; European Commission","keywords":"Similarity (geometry); Fidelity; Metric (unit); Data mining; Computer science; Reuse; Surrogate model; Conceptual design; Software; Visualization; Machine learning; Artificial intelligence; Engineering; Image (mathematics); Human–computer interaction","score_opus":0.15164267006233353,"score_gpt":0.3277300605853636,"score_spread":0.17608739052303005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379744505","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":[0.0011947239,0.0000108903105,0.99525833,0.0009410155,0.000788829,0.0007376561,0.000004754189,0.0007426286,0.00032114433],"genre_scores_gemma":[0.064042,0.0000041794474,0.9347818,0.0006909607,0.00009038128,0.0001420042,0.00002019109,0.000025565625,0.00020294209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974388,0.00035926304,0.00041965957,0.0008924609,0.00037519386,0.0005146212],"domain_scores_gemma":[0.99842745,0.00029610685,0.00013110768,0.0007079975,0.00020157556,0.00023574797],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00064715085,0.0002743272,0.00029892803,0.0004721377,0.00016809904,0.00017334307,0.0010096411,0.000373436,0.0000030973954],"category_scores_gemma":[0.0002978224,0.0003082112,0.000054409615,0.0030802295,0.000110627916,0.0010246346,0.00065625977,0.000787428,0.00008481309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011213057,0.0002342235,0.00033841166,0.000008437288,0.000013550393,0.00003976722,0.003913906,0.9714805,0.00016155196,0.003984319,0.0033399384,0.016474208],"study_design_scores_gemma":[0.0010149361,0.000053908385,0.0047163283,0.00004122402,0.0000023193115,0.0000100932675,0.0007241042,0.9917811,0.0004841342,0.00044237796,0.00037017645,0.00035929822],"about_ca_topic_score_codex":0.000016498787,"about_ca_topic_score_gemma":0.0000029312803,"teacher_disagreement_score":0.06284728,"about_ca_system_score_codex":0.00029431024,"about_ca_system_score_gemma":0.0001548975,"threshold_uncertainty_score":0.999937},"labels":[],"label_agreement":null},{"id":"W4379805925","doi":"10.2514/6.2023-4261","title":"Efficient Acquisition Functions for Bayesian Optimization in the Presence of Hidden Constraints","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; McGill University","funders":"","keywords":"Bayesian optimization; Computer science; Bayesian probability; Classifier (UML); Artificial intelligence; Machine learning; Test functions for optimization; Optimization problem; Convergence (economics); Context (archaeology); Mathematical optimization; Algorithm; Multi-swarm optimization; Mathematics","score_opus":0.017063412263986204,"score_gpt":0.27593371855277427,"score_spread":0.25887030628878804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379805925","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":[0.000318027,0.000006345671,0.9963273,0.0007119579,0.00015948374,0.0006457302,0.000011643645,0.00013003031,0.0016894849],"genre_scores_gemma":[0.4423583,0.0000048510083,0.557137,0.00011189631,0.000017527605,0.00013712938,0.000022788046,0.0000066756083,0.0002038041],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907726,0.00007196969,0.00021618689,0.00025220824,0.00020726355,0.00017512469],"domain_scores_gemma":[0.9990109,0.00040664946,0.00008512035,0.00029618735,0.00017520208,0.000025921945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003874506,0.00007887765,0.000090778005,0.0002002509,0.000102125174,0.000039668514,0.00036894414,0.00003610402,0.000033076984],"category_scores_gemma":[0.0001606233,0.00006139078,0.000039854658,0.0012877999,0.00008569088,0.00016656012,0.00006903325,0.000048041897,0.000010642417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037523719,0.000047906113,0.000035289566,0.0000056920353,0.0000030194722,9.729988e-7,0.00058260525,0.9772701,0.00006391155,0.0141493175,0.00013805905,0.0076993303],"study_design_scores_gemma":[0.0003933659,0.000039461847,0.0009118224,0.000010710772,0.0000022960457,0.0000036168528,0.0004898894,0.9972594,0.00020894525,0.00058489665,0.00002648118,0.00006910928],"about_ca_topic_score_codex":0.000005352152,"about_ca_topic_score_gemma":0.0000035225175,"teacher_disagreement_score":0.44204026,"about_ca_system_score_codex":0.000032697124,"about_ca_system_score_gemma":0.000045895224,"threshold_uncertainty_score":0.25034437},"labels":[],"label_agreement":null},{"id":"W4384575254","doi":"10.23952/asvao.5.2023.2.12","title":"Quasi-Newton methods for multiobjective optimization problems: A systematic review","year":2023,"lang":"en","type":"review","venue":"Applied Set-Valued Analysis and Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Banaras Hindu University; National Natural Science Foundation of China; Science and Engineering Research Board; Indian Institute of Technology, Patna","keywords":"Hessian matrix; Broyden–Fletcher–Goldfarb–Shanno algorithm; Quasi-Newton method; Line search; Mathematical optimization; Convergence (economics); Mathematics; Multi-objective optimization; Optimization problem; Newton's method; Computer science; Trust region; Applied mathematics; Nonlinear system","score_opus":0.05209892652927674,"score_gpt":0.39214884851045045,"score_spread":0.34004992198117373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384575254","genre_codex":"methods","genre_gemma":"review","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":[2.4808602e-10,0.46198478,0.53117746,0.000019858606,0.00008483449,0.0063014976,0.000046441673,0.00034802017,0.00003710585],"genre_scores_gemma":[1.8778968e-8,0.50536954,0.4891805,0.000086006825,0.000034607794,0.0037868256,0.0012433918,0.00009842657,0.00020064917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.992752,0.0012264834,0.0026308158,0.0021684938,0.0005701001,0.0006520627],"domain_scores_gemma":[0.99288064,0.0013755658,0.0030320496,0.0015453891,0.000910615,0.0002557138],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0029577734,0.0011526288,0.005812606,0.0019076227,0.00055425835,0.0005002835,0.0010855017,0.0005630289,0.000017683651],"category_scores_gemma":[0.0011524727,0.000982847,0.0012203967,0.009345959,0.00009458835,0.0006510726,0.00040359978,0.00038529315,0.00001751491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021512533,0.0000792914,2.3817064e-8,0.24910176,0.0021995523,7.2071856e-7,0.00014865356,0.7117725,4.392368e-8,0.0014902016,0.000020598,0.035184495],"study_design_scores_gemma":[0.00041552255,0.0000673496,2.8355032e-8,0.052104544,0.020167964,0.0000074794125,0.000038526774,0.92473173,7.049581e-7,0.00013456981,0.00139987,0.0009317351],"about_ca_topic_score_codex":0.000015634885,"about_ca_topic_score_gemma":0.000005977121,"teacher_disagreement_score":0.21295919,"about_ca_system_score_codex":0.0004438302,"about_ca_system_score_gemma":0.00026687796,"threshold_uncertainty_score":0.9992622},"labels":[],"label_agreement":null},{"id":"W4384831640","doi":"10.1016/j.swevo.2023.101360","title":"Redefined decision variable analysis method for large-scale optimization and its application to feature selection","year":2023,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","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":"Guangdong Provincial Pearl River Talents Program; Science, Technology and Innovation Commission of Shenzhen Municipality; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Feature selection; Selection (genetic algorithm); Variable (mathematics); Scale (ratio); Feature (linguistics); Artificial intelligence; Data mining; Machine learning; Mathematics","score_opus":0.008654312035199448,"score_gpt":0.293204422974132,"score_spread":0.28455011093893257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384831640","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":[0.0008152213,0.00010068012,0.996874,0.00079227786,0.00014717107,0.0008170456,0.00003533547,0.0003843663,0.00003394378],"genre_scores_gemma":[0.02745033,0.000048762842,0.97145385,0.00015043955,0.00006427716,0.00019177263,0.00041150866,0.000016257363,0.00021277762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984927,0.000081666585,0.00024276189,0.0006917026,0.00023675844,0.00025440232],"domain_scores_gemma":[0.9987679,0.000304801,0.00012901875,0.00014292354,0.0005327115,0.00012261748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048042124,0.00016516082,0.0002115385,0.000699275,0.0005199892,0.00009020825,0.00012520466,0.00011465154,0.0000024423302],"category_scores_gemma":[0.00012629923,0.00017956336,0.000048908107,0.003998154,0.000008634729,0.0006325953,0.00013396505,0.00007597612,0.000008293753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000308986,0.000031647243,0.00013274231,0.000013671001,0.000041310275,1.8837518e-7,0.0002616923,0.9825411,0.0003044394,0.004707681,0.0005066747,0.011427956],"study_design_scores_gemma":[0.00060704246,0.00008511377,0.0058797575,0.000009216082,0.00006687928,0.000008290768,0.00005017801,0.9854588,0.00011287662,0.0069871065,0.0005397047,0.00019504283],"about_ca_topic_score_codex":0.000008493474,"about_ca_topic_score_gemma":0.000008943893,"teacher_disagreement_score":0.026635109,"about_ca_system_score_codex":0.00011204526,"about_ca_system_score_gemma":0.000047724054,"threshold_uncertainty_score":0.7322381},"labels":[],"label_agreement":null},{"id":"W4385219046","doi":"10.1145/3583133.3596345","title":"Personalized Group Itinerary Recommendation using a Knowledge-based Evolutionary Approach","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 Windsor","funders":"","keywords":"Computer science; Set (abstract data type); Space (punctuation); Normative; Recommender system; Process (computing); Group (periodic table); Artificial intelligence; Collaborative filtering; Evolutionary algorithm; Machine learning","score_opus":0.04963183026447662,"score_gpt":0.3080158847591299,"score_spread":0.2583840544946533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385219046","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":[0.00022909293,0.00003933493,0.9887761,0.00040914497,0.00029941558,0.00026889236,0.000005660316,0.0010525445,0.008919802],"genre_scores_gemma":[0.013047325,0.0000073928118,0.9847397,0.00030082936,0.00009408442,0.00005802723,0.00020010203,0.000023186882,0.0015293121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860775,0.00015856646,0.00022575496,0.0005250427,0.0001872525,0.0002956527],"domain_scores_gemma":[0.9992095,0.00014212696,0.00008826532,0.0002877059,0.00017922116,0.00009314531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002763452,0.00016607653,0.00014991926,0.00035265606,0.00027256075,0.00007340289,0.00034679315,0.0000634608,0.00010617012],"category_scores_gemma":[0.000067124456,0.00016493633,0.00007739571,0.0018130044,0.000057784375,0.00082416105,0.00019704309,0.00011649445,0.00013429737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008228725,0.0015537309,0.0009510149,0.00014562899,0.00011689766,0.000029885967,0.0025329206,0.6862819,0.004202152,0.12160172,0.014621342,0.16788052],"study_design_scores_gemma":[0.00086079387,0.00002329397,0.00044571285,0.000009028529,0.000003546625,0.0000111367735,0.00007913499,0.9943886,0.00008573789,0.0005591988,0.0033200483,0.00021379333],"about_ca_topic_score_codex":0.0000109469365,"about_ca_topic_score_gemma":7.261952e-7,"teacher_disagreement_score":0.30810666,"about_ca_system_score_codex":0.00019787859,"about_ca_system_score_gemma":0.00012857464,"threshold_uncertainty_score":0.6725908},"labels":[],"label_agreement":null},{"id":"W4385457286","doi":"10.1002/cjce.25055","title":"Global optimization of the design of intensified shell and tube heat exchanger using tube inserts","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Chongqing University; National Natural Science Foundation of China","keywords":"Trimming; Heat exchanger; Shell and tube heat exchanger; Tube (container); Baffle; Mechanical engineering; Process (computing); Multi-objective optimization; Set (abstract data type); Shell (structure); Computer science; Pareto principle; Engineering; Mathematical optimization; Mathematics","score_opus":0.023255233854076452,"score_gpt":0.22431676982389034,"score_spread":0.20106153596981388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385457286","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":[0.057445846,0.0001314116,0.9417449,0.00032701006,0.00023696343,0.00009036788,0.0000031344941,0.000010923234,0.000009413468],"genre_scores_gemma":[0.8765769,0.0000072044286,0.12331136,0.0000521275,0.000037258505,7.1362547e-7,3.12365e-7,0.000010074207,0.000004021469],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931437,0.000028013734,0.00024857314,0.00008400207,0.00015717694,0.00016786541],"domain_scores_gemma":[0.99926066,0.00009076135,0.00009630342,0.00016423465,0.0002505675,0.00013745086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024217136,0.000088049404,0.00015781842,0.000103073806,0.000042427077,0.000025153051,0.0003779632,0.000045410292,0.0000026310304],"category_scores_gemma":[0.0003269084,0.00006179613,0.00004418756,0.0007101102,0.00007282434,0.0001709018,0.00006289936,0.00011499678,1.3020808e-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":[0.000002496837,0.000001878571,0.000050654555,0.000008840117,0.000013975395,0.0000045705374,0.00028227444,0.98295534,0.016275171,0.00013819593,0.00001526725,0.00025136137],"study_design_scores_gemma":[0.0001715458,0.000010884623,0.00014730924,0.0000779666,0.000009337352,0.00009503764,0.00001420409,0.9694029,0.029904274,0.00010036538,0.0000063930124,0.000059775193],"about_ca_topic_score_codex":0.00023015167,"about_ca_topic_score_gemma":0.000013709178,"teacher_disagreement_score":0.8191311,"about_ca_system_score_codex":0.00015227766,"about_ca_system_score_gemma":0.00021399485,"threshold_uncertainty_score":0.2519973},"labels":[],"label_agreement":null},{"id":"W4385581085","doi":"10.7717/peerj-cs.1431","title":"A new metaphor-less simple algorithm based on Rao algorithms: a Fully Informed Search Algorithm (FISA)","year":2023,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Algorithm; Computer science; Benchmark (surveying); Simplicity; MATLAB; Simple (philosophy)","score_opus":0.03195693581331657,"score_gpt":0.31061099094447847,"score_spread":0.2786540551311619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385581085","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":[0.00020474687,0.000029543678,0.9923505,0.0015823686,0.0023998225,0.0009354822,0.00004910752,0.0019696194,0.0004788033],"genre_scores_gemma":[0.0016477524,0.000025046587,0.99488384,0.0018742406,0.00057440175,0.00006450271,0.000049806877,0.00006583759,0.000814603],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99058914,0.00020986963,0.0008473478,0.0024744312,0.0036956354,0.0021835582],"domain_scores_gemma":[0.9939848,0.000824581,0.00029944384,0.0023979072,0.0012715274,0.001221715],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.002324954,0.0007683376,0.00073024,0.0023325733,0.0012054883,0.0016103155,0.0055999444,0.00017800162,0.000049386566],"category_scores_gemma":[0.00029651044,0.0007373911,0.00028594743,0.012811231,0.00064715074,0.0032866725,0.0022748655,0.0006744385,0.0007618616],"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":[0.000006762221,0.00010074337,0.000025941592,0.0000119953365,0.000019056797,0.00011346292,0.00091232534,0.16031706,0.00006976654,0.00063997123,0.0027655063,0.8350174],"study_design_scores_gemma":[0.0017902777,0.00044379538,0.0016862262,0.000051270385,0.000010146928,0.000057739857,0.000093887065,0.98655343,0.0029424292,0.0010463243,0.0044363816,0.000888093],"about_ca_topic_score_codex":0.00018384917,"about_ca_topic_score_gemma":0.000011236889,"teacher_disagreement_score":0.83412933,"about_ca_system_score_codex":0.00066535024,"about_ca_system_score_gemma":0.002981523,"threshold_uncertainty_score":0.99978024},"labels":[],"label_agreement":null},{"id":"W4385723738","doi":"10.1007/s00245-023-10036-y","title":"An Adaptive Consensus Based Method for Multi-objective Optimization with Uniform Pareto Front Approximation","year":2023,"lang":"en","type":"article","venue":"Applied Mathematics & Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Banff International Research Station for Mathematical Innovation and Discovery; Ministero dell’Istruzione, dell’Università e della Ricerca; Deutsche Forschungsgemeinschaft","keywords":"Mathematical optimization; Multi-objective optimization; Convergence (economics); Pareto principle; Heuristic; Computer science; Computation; Parametric statistics; Mathematics; Metaheuristic; Algorithm","score_opus":0.03278531748220069,"score_gpt":0.3032746034102,"score_spread":0.2704892859279993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385723738","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":[0.000020970998,0.00000585151,0.9939042,0.00012593286,0.00012784368,0.0035845875,0.000049109083,0.0014444634,0.0007370402],"genre_scores_gemma":[0.0020269044,0.000009610661,0.9954289,0.00016506336,0.00004962945,0.0014709069,0.00063116854,0.00013761486,0.000080169084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99696016,0.0001032056,0.0007109697,0.001048582,0.000584728,0.00059237645],"domain_scores_gemma":[0.99638575,0.00060420344,0.00075933005,0.0009514511,0.0010927893,0.00020648299],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00089074014,0.00053099584,0.00054214936,0.0005846579,0.000504365,0.00026540773,0.0006259751,0.00023348174,0.000016044563],"category_scores_gemma":[0.00019910585,0.00050196814,0.00008946989,0.0016037733,0.000101746155,0.0007685816,0.00010245513,0.00018481427,0.000025483056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105855004,0.00033457798,0.0000017395508,0.00007155376,0.00005736036,0.0000024900799,0.0020343997,0.96700597,0.00013084992,0.027609402,0.000038522125,0.0026072545],"study_design_scores_gemma":[0.0029570856,0.0003283618,0.000007273479,0.000051200637,0.00007016109,0.0000082475035,0.0012744906,0.9893281,0.0032216639,0.0021037974,0.000011905688,0.00063771324],"about_ca_topic_score_codex":0.00000873735,"about_ca_topic_score_gemma":0.000013937354,"teacher_disagreement_score":0.025505606,"about_ca_system_score_codex":0.0003234286,"about_ca_system_score_gemma":0.00025711497,"threshold_uncertainty_score":0.9997432},"labels":[],"label_agreement":null},{"id":"W4386429413","doi":"10.1109/intermagshortpapers58606.2023.10228231","title":"Surrogate-Based Modeling of Induction Machines to Reduce the Computational Burden of Robust Multi-Objective Optimization","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Surrogate model; Computer science; Bottleneck; Multi-objective optimization; Robust optimization; Optimization problem; Engineering optimization; Mathematical optimization; Machine learning; Algorithm; Mathematics","score_opus":0.04502553625917919,"score_gpt":0.2987310368157614,"score_spread":0.2537055005565822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386429413","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":[0.0039313408,0.000008998418,0.99409163,0.00083516026,0.00025466582,0.00049385126,0.000011760968,0.00022202448,0.00015059371],"genre_scores_gemma":[0.33718854,0.000003007663,0.6625727,0.000043973363,0.000027417418,0.000026724869,0.000025438661,0.000013920929,0.000098297285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984392,0.00013933165,0.0004223922,0.00039620214,0.00041132543,0.00019155172],"domain_scores_gemma":[0.99813193,0.00023158817,0.00021364578,0.00034867867,0.0010139727,0.000060160764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041908963,0.00015859195,0.00020535514,0.00040004175,0.0001504522,0.00003587388,0.00049101183,0.00005908414,0.000014460209],"category_scores_gemma":[0.00026988942,0.00012729014,0.0000694381,0.0021794296,0.00006491967,0.00043757661,0.00017868166,0.000107035914,0.000009715331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001611206,0.000047045403,0.000027841517,0.000009382892,0.000016867818,5.122484e-7,0.0010310224,0.99476796,0.0003712313,0.00092237466,0.000018604453,0.0027710653],"study_design_scores_gemma":[0.0006044291,0.00005074722,0.00023236388,0.00002510593,0.000006129852,0.0000019825288,0.00034733483,0.99621683,0.0020793455,0.00030095817,0.000001995602,0.00013276414],"about_ca_topic_score_codex":0.00021775413,"about_ca_topic_score_gemma":0.000010216699,"teacher_disagreement_score":0.3332572,"about_ca_system_score_codex":0.00007322998,"about_ca_system_score_gemma":0.00013930276,"threshold_uncertainty_score":0.51907414},"labels":[],"label_agreement":null},{"id":"W4386447329","doi":"10.1016/j.eswa.2023.121375","title":"Sustainable group tourist trip planning: An adaptive large neighborhood search algorithm","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Tourism; Computer science; Operations research; Sustainability; Profit (economics); Environmental economics; Sustainable tourism; Heuristic; Mathematical optimization; Marketing; Business; Economics; Microeconomics; Mathematics; Artificial intelligence","score_opus":0.02209203793765084,"score_gpt":0.3019378705525617,"score_spread":0.27984583261491086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386447329","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":[0.000028255223,0.00040735485,0.99369955,0.00021594486,0.00012451457,0.0020923368,0.000034842822,0.0013069727,0.0020902331],"genre_scores_gemma":[0.20928091,0.000066452245,0.7610487,0.00030901234,0.0010748823,0.013675463,0.0004082169,0.00018987038,0.013946499],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970596,0.00016954468,0.00036617997,0.00096446427,0.000579954,0.0008602742],"domain_scores_gemma":[0.99754,0.00015422986,0.00016611688,0.0012166627,0.00057916215,0.00034380823],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000477152,0.00030097915,0.0003138108,0.0003920052,0.000882784,0.0003409505,0.0010370358,0.000112777605,0.000008139747],"category_scores_gemma":[0.000018572318,0.00027097433,0.00005107009,0.002755763,0.000077585835,0.0012122856,0.00028796858,0.00023735018,0.00016378296],"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":[0.000079235215,0.0014318788,0.0004813385,0.000100442514,0.00023377448,0.00038519534,0.020706085,0.12464291,0.00028156664,0.8110069,0.00787033,0.032780334],"study_design_scores_gemma":[0.0010257636,0.00027945955,0.00030528707,0.000028886272,0.0000047239896,0.000049685794,0.013054632,0.94531083,0.00008922126,0.00043830054,0.038972877,0.0004403229],"about_ca_topic_score_codex":0.00021701491,"about_ca_topic_score_gemma":0.0000049364617,"teacher_disagreement_score":0.8206679,"about_ca_system_score_codex":0.00027018742,"about_ca_system_score_gemma":0.0001875216,"threshold_uncertainty_score":0.99997425},"labels":[],"label_agreement":null},{"id":"W4386803120","doi":"10.23977/acss.2023.070703","title":"A Self-Organizing Multimodal Multi-Objective Coati Optimization Algorithm","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China","keywords":"Evolutionary algorithm; Multi-objective optimization; Computer science; Sorting; Pareto principle; Mathematical optimization; Optimization problem; Artificial intelligence; Machine learning; Algorithm; Mathematics","score_opus":0.0156873036427435,"score_gpt":0.2729975300320266,"score_spread":0.2573102263892831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386803120","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":[0.0001937393,0.0021262965,0.99417543,0.000053756925,0.0016117558,0.00082757586,0.000012724272,0.0009185259,0.000080181526],"genre_scores_gemma":[0.027069546,0.0014414344,0.9709019,0.00009790155,0.00023056223,0.00012804767,0.000019325646,0.00004623797,0.000065045395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970807,0.0002944585,0.00064338674,0.0010039652,0.0004087889,0.0005687463],"domain_scores_gemma":[0.9983313,0.00043672265,0.0002929111,0.0004478964,0.00032966756,0.00016150123],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057557074,0.00037293308,0.00051066704,0.0005201957,0.00026463144,0.0003433228,0.0005836539,0.00013232735,0.0000026618898],"category_scores_gemma":[0.000048756734,0.00036825988,0.000059606165,0.0018678332,0.000066063854,0.0022236484,0.00045160297,0.00022909541,0.000031171996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028469658,0.00008527059,0.00042632842,0.000056039447,0.000022173599,0.00005720651,0.0017394662,0.93177855,0.000028827591,0.00049414014,0.000014893144,0.06529428],"study_design_scores_gemma":[0.0013783803,0.00010438814,0.00034158485,0.00012030613,0.0000047754,0.000051786254,0.00018354767,0.99661213,0.00010291774,0.0001472394,0.0005140543,0.00043886714],"about_ca_topic_score_codex":0.0000306425,"about_ca_topic_score_gemma":0.000007395436,"teacher_disagreement_score":0.06485542,"about_ca_system_score_codex":0.00014855752,"about_ca_system_score_gemma":0.000062595354,"threshold_uncertainty_score":0.9998769},"labels":[],"label_agreement":null},{"id":"W4386916513","doi":"10.1080/00224065.2023.2241680","title":"Adaptive-region sequential design with quantitative and qualitative factors in application to HPC configuration","year":2023,"lang":"en","type":"article","venue":"Journal of Quality Technology","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Natural Science Foundation of China","keywords":"Constructive; Computer science; Process (computing); Adaptive design; Operations research; Point (geometry); Management science; Industrial engineering; Engineering; Mathematics; Programming language","score_opus":0.11262002623504903,"score_gpt":0.39984248294129476,"score_spread":0.28722245670624574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386916513","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":[0.05435239,0.000020665835,0.9414385,0.0036526686,0.00005813298,0.00035267,0.0000022264612,0.00010562999,0.000017095932],"genre_scores_gemma":[0.628331,0.000016601736,0.3715395,0.000060143808,0.000007916535,0.000020661711,0.0000012982503,0.000007985263,0.000014883524],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982832,0.0004547893,0.00051895273,0.0002888665,0.00026002072,0.00019418471],"domain_scores_gemma":[0.998154,0.00052105496,0.0005785495,0.00019903964,0.00048059,0.000066788256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010998988,0.00013862568,0.00031058962,0.001051396,0.0000730027,0.000032640222,0.00031828546,0.00011628976,7.2914463e-7],"category_scores_gemma":[0.0004843921,0.00011595057,0.000026318645,0.0017189949,0.00015437129,0.0005088245,0.00007855281,0.00025171897,0.0000062766603],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068876205,0.00026148823,0.0017238053,0.00003170826,0.00014458089,0.00011212415,0.06707007,0.10838121,0.027879275,0.7298172,0.000059371785,0.06383043],"study_design_scores_gemma":[0.007427405,0.009292703,0.031762607,0.00030854341,0.000048518923,0.00035781643,0.1305952,0.44159126,0.117985174,0.25864178,0.0003936747,0.0015953069],"about_ca_topic_score_codex":0.000027816,"about_ca_topic_score_gemma":0.000045300123,"teacher_disagreement_score":0.5739786,"about_ca_system_score_codex":0.00015391623,"about_ca_system_score_gemma":0.000104705665,"threshold_uncertainty_score":0.4728327},"labels":[],"label_agreement":null},{"id":"W4386983380","doi":"10.1016/j.knosys.2023.111032","title":"Neural network-enabled discovery of mapping between variables and constraints for autonomous repair-based constraint handling in multi-objective structural optimization","year":2023,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Constraint (computer-aided design); Benchmark (surveying); Sorting; Mathematical optimization; Computer science; Population; Genetic algorithm; Optimization problem; Artificial neural network; Variable (mathematics); Algorithm; Artificial intelligence; Mathematics","score_opus":0.03595841161778918,"score_gpt":0.2854779191023652,"score_spread":0.24951950748457602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386983380","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":[0.010015907,0.00027657714,0.9861069,0.000034233377,0.0009281797,0.0019232075,0.00012456425,0.00051154586,0.000078921265],"genre_scores_gemma":[0.7124524,0.0000024296107,0.28704745,0.000014160703,0.000105764426,0.00019038361,0.00010040787,0.000035653116,0.000051359275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968167,0.00041762652,0.0009869186,0.0008846119,0.00022903833,0.0006651339],"domain_scores_gemma":[0.99667263,0.0016620306,0.0005336485,0.0004534351,0.0005448915,0.00013334182],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011924641,0.0003901,0.00077166397,0.0006214776,0.0002799114,0.00021624252,0.00038109647,0.00019569165,0.000002130981],"category_scores_gemma":[0.00048330898,0.00039555738,0.0001541636,0.0015640722,0.00035374906,0.00069645554,0.00013454589,0.00021164984,0.0000016746968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025948028,0.000039720366,0.009097543,0.00031764098,0.00005488054,0.000008428185,0.00057992956,0.98693603,0.00017940387,0.0010183022,0.000011457524,0.0017307199],"study_design_scores_gemma":[0.003995482,0.00011007732,0.0031355887,0.00048591904,0.000022461816,0.0000054963953,0.0003886948,0.9909027,0.0004477827,0.000086647095,0.000014657463,0.00040449708],"about_ca_topic_score_codex":0.00006341604,"about_ca_topic_score_gemma":0.000029883033,"teacher_disagreement_score":0.7024365,"about_ca_system_score_codex":0.00030431745,"about_ca_system_score_gemma":0.0005773367,"threshold_uncertainty_score":0.9998496},"labels":[],"label_agreement":null},{"id":"W4387005540","doi":"10.1109/cec53210.2023.10254163","title":"A Comparative Study of Multi-Guide Particle Swarm Optimization Topologies in Dynamic Multi-Objective Environments","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Benchmark (surveying); Particle swarm optimization; Network topology; Computer science; Mathematical optimization; Set (abstract data type); Multi-swarm optimization; Topology optimization; Swarm behaviour; Topology (electrical circuits); Metaheuristic; Algorithm; Mathematics; Engineering; Finite element method","score_opus":0.04313637816854552,"score_gpt":0.3424501964806616,"score_spread":0.29931381831211606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387005540","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":[0.07810072,0.00001777794,0.92029506,0.000049712387,0.00013751403,0.001023688,0.0000044413864,0.0002646768,0.00010642181],"genre_scores_gemma":[0.658961,0.000023869388,0.3400117,0.000020915682,0.000002719905,0.00011845871,0.0000056172285,0.000011898489,0.0008438484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786365,0.0002504002,0.00054101355,0.00065773726,0.00031358207,0.00037358984],"domain_scores_gemma":[0.9988991,0.00021749157,0.00021267691,0.00050863426,0.000091967755,0.000070131886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028035304,0.00023235052,0.00038269057,0.00030754408,0.0001097843,0.00003790956,0.0005463988,0.00006347392,0.0000144907035],"category_scores_gemma":[0.00014587818,0.00022163571,0.00004525412,0.0015365459,0.00011644157,0.00074832974,0.00048791294,0.00014137907,0.000057188376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012447508,0.0013005168,0.0036621504,0.0000029047128,0.000031922475,0.000014697315,0.010000028,0.9833483,0.0006363727,0.00013181572,0.0000054018824,0.00085340603],"study_design_scores_gemma":[0.0029705886,0.00024414028,0.04266283,0.000008136996,0.000005436283,0.0000014381441,0.012688278,0.9359412,0.0052202004,0.00003904176,0.0000039545384,0.0002147583],"about_ca_topic_score_codex":0.00017291038,"about_ca_topic_score_gemma":0.00043007312,"teacher_disagreement_score":0.58086026,"about_ca_system_score_codex":0.00023676483,"about_ca_system_score_gemma":0.000039888746,"threshold_uncertainty_score":0.9038042},"labels":[],"label_agreement":null},{"id":"W4387006045","doi":"10.1109/cec53210.2023.10254106","title":"Multi-Objective Coordinate Search Optimization","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Wilfrid Laurier University; Brock University; Ontario Tech University","funders":"","keywords":"Mathematical optimization; Multi-objective optimization; Benchmark (surveying); Computer science; Optimization problem; Pareto principle; Set (abstract data type); Population; Evolutionary algorithm; Computation; Algorithm; Mathematics","score_opus":0.02979666514102418,"score_gpt":0.3015909265642199,"score_spread":0.2717942614231957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387006045","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":[0.00012390362,0.000011144089,0.99328566,0.00054568966,0.00035595256,0.0003507117,0.0000037345499,0.0016885793,0.0036346507],"genre_scores_gemma":[0.022076633,0.000045339617,0.9691825,0.00021141928,0.000036770896,0.000049529568,0.000019195191,0.000027926848,0.0083506685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833137,0.00010337188,0.00021072732,0.0005945278,0.00032870955,0.00043129452],"domain_scores_gemma":[0.99877,0.00013579198,0.000057261735,0.00049153884,0.00041547508,0.00012993337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031683248,0.00017303067,0.00015820307,0.0003986217,0.00024092573,0.00014575539,0.00062638364,0.000071263195,0.00007753091],"category_scores_gemma":[0.00014530972,0.00016597875,0.00005738574,0.00261715,0.000060547518,0.0010343065,0.00045154226,0.00015284789,0.00072308246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028746317,0.000044090415,0.000111931295,0.0000034431266,0.00001293879,0.000017781156,0.00048741457,0.9867319,0.00021461732,0.003400274,0.0002655158,0.008707253],"study_design_scores_gemma":[0.0006699806,0.00003654942,0.0014060485,0.0000054717652,0.0000017788003,0.000007531395,0.00015542246,0.9949832,0.0021824813,0.00015268521,0.00018493124,0.00021391979],"about_ca_topic_score_codex":0.000026720745,"about_ca_topic_score_gemma":0.000004583076,"teacher_disagreement_score":0.024103116,"about_ca_system_score_codex":0.00011714007,"about_ca_system_score_gemma":0.000078790574,"threshold_uncertainty_score":0.9294003},"labels":[],"label_agreement":null},{"id":"W4387251127","doi":"10.1109/intermag50591.2023.10265080","title":"Surrogate-Based Modeling of Induction Machines to Reduce the Computational Burden of Robust Multi-Objective Optimization","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Surrogate model; Computer science; Bottleneck; Multi-objective optimization; Process (computing); Robust optimization; Optimization problem; Mathematical optimization; Engineering optimization; Machine learning; Algorithm; Mathematics","score_opus":0.04502553625917919,"score_gpt":0.2987310368157614,"score_spread":0.2537055005565822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387251127","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":[0.0039313408,0.000008998418,0.99409163,0.00083516026,0.00025466582,0.00049385126,0.000011760968,0.00022202448,0.00015059371],"genre_scores_gemma":[0.33718854,0.000003007663,0.6625727,0.000043973363,0.000027417418,0.000026724869,0.000025438661,0.000013920929,0.000098297285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984392,0.00013933165,0.0004223922,0.00039620214,0.00041132543,0.00019155172],"domain_scores_gemma":[0.99813193,0.00023158817,0.00021364578,0.00034867867,0.0010139727,0.000060160764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041908963,0.00015859195,0.00020535514,0.00040004175,0.0001504522,0.00003587388,0.00049101183,0.00005908414,0.000014460209],"category_scores_gemma":[0.00026988942,0.00012729014,0.0000694381,0.0021794296,0.00006491967,0.00043757661,0.00017868166,0.000107035914,0.000009715331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001611206,0.000047045403,0.000027841517,0.000009382892,0.000016867818,5.122484e-7,0.0010310224,0.99476796,0.0003712313,0.00092237466,0.000018604453,0.0027710653],"study_design_scores_gemma":[0.0006044291,0.00005074722,0.00023236388,0.00002510593,0.000006129852,0.0000019825288,0.00034733483,0.99621683,0.0020793455,0.00030095817,0.000001995602,0.00013276414],"about_ca_topic_score_codex":0.00021775413,"about_ca_topic_score_gemma":0.000010216699,"teacher_disagreement_score":0.3332572,"about_ca_system_score_codex":0.00007322998,"about_ca_system_score_gemma":0.00013930276,"threshold_uncertainty_score":0.51907414},"labels":[],"label_agreement":null},{"id":"W4387260729","doi":"10.23952/jnva.7.2023.5.03","title":"Further results on quasi efficient solutions in multiobjective optimization","year":2023,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Agencia Estatal de Investigación; Ministerio de Ciencia e Innovación","keywords":"Mathematical optimization; Multi-objective optimization; Computer science; Mathematics","score_opus":0.01994990746582334,"score_gpt":0.2847101795413509,"score_spread":0.26476027207552755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387260729","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":[0.0024820543,0.000020722364,0.9953464,0.0017873676,0.00012200151,0.00007242141,0.000029030776,0.000025772075,0.0001141908],"genre_scores_gemma":[0.36323902,0.00010332148,0.63617116,0.00013538415,0.00016512196,0.0000049754176,0.000049828606,0.00001029929,0.00012092292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985522,0.00012897645,0.0005141669,0.0002333567,0.00041113142,0.00016015126],"domain_scores_gemma":[0.9984968,0.00037803617,0.00037611442,0.000145035,0.0005302445,0.000073777286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008268574,0.00010574326,0.00023587247,0.0014034982,0.00011659928,0.000061129846,0.00018072769,0.000052461903,0.000009678224],"category_scores_gemma":[0.00048950006,0.00009077615,0.00013586046,0.003435208,0.000023631426,0.0002770128,0.00006334784,0.0001586033,0.000009143431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043915774,0.00020822813,0.00025213006,0.000001079326,0.00017174704,0.000009852158,0.0007172128,0.9954101,0.000011319094,0.0018337821,0.000010998336,0.001329659],"study_design_scores_gemma":[0.0009937416,0.00009074968,0.028563354,0.000010387443,0.000064012274,0.0000059687577,0.00009813379,0.96979177,0.000011002012,0.00023871833,0.000041314215,0.00009084088],"about_ca_topic_score_codex":0.000017683999,"about_ca_topic_score_gemma":0.000014847623,"teacher_disagreement_score":0.36075696,"about_ca_system_score_codex":0.00015140303,"about_ca_system_score_gemma":0.00009336072,"threshold_uncertainty_score":0.3701744},"labels":[],"label_agreement":null},{"id":"W4388441216","doi":"10.18280/isi.280503","title":"An Improved Harris Hawks Optimization Algorithm Based on Bi-Goal Evolution and Multi-Leader Selection Strategy for Multi-Objective Optimization","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Mathematical optimization; Computer science; Optimization algorithm; Multi-objective optimization; Algorithm; Mathematics; Artificial intelligence","score_opus":0.024219127616420314,"score_gpt":0.280037891088677,"score_spread":0.2558187634722567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388441216","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":[0.0002494825,0.000018061555,0.99566376,0.000048097718,0.00049463456,0.0020079731,0.00010682483,0.0013394636,0.000071692906],"genre_scores_gemma":[0.10407704,0.00002265455,0.8940955,0.00015002897,0.0000899285,0.000589102,0.0008637947,0.000052501102,0.00005945418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997322,0.00020444237,0.00079940935,0.0006569167,0.00040660723,0.0006106307],"domain_scores_gemma":[0.9971378,0.00015090987,0.0006281566,0.00043253825,0.0014391437,0.00021147895],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00069248915,0.00046238408,0.00034599178,0.0012108808,0.0009394238,0.0007617944,0.00037070204,0.00033224004,0.000010450145],"category_scores_gemma":[0.00048188795,0.000497597,0.000097975026,0.002017968,0.0001460513,0.009349394,0.00007561881,0.00023961804,0.000019559307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006033895,0.00010244784,0.0000878952,0.000070069036,0.00002425471,6.030958e-7,0.0013832089,0.9228142,0.000109027555,0.00032428926,0.000021089932,0.075002596],"study_design_scores_gemma":[0.0030972857,0.0006595583,0.0018901024,0.00006914426,0.000022485425,0.00001222278,0.0008980344,0.99186575,0.0007675221,0.00014712705,0.000021439571,0.00054931315],"about_ca_topic_score_codex":0.00008002397,"about_ca_topic_score_gemma":0.000016844935,"teacher_disagreement_score":0.103827566,"about_ca_system_score_codex":0.00092642294,"about_ca_system_score_gemma":0.00027292734,"threshold_uncertainty_score":0.9997476},"labels":[],"label_agreement":null},{"id":"W4388808998","doi":"10.1016/j.asoc.2023.111065","title":"Adaptive surrogate assisted multi-objective optimization approach for highly nonlinear and complex engineering design problems","year":2023,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Ministry of Higher Education and Scientific Research","keywords":"Surrogate model; Mathematical optimization; Kriging; Computer science; Pareto principle; Engineering optimization; Engineering design process; Multi-objective optimization; Benchmark (surveying); Test functions for optimization; Finite element method; Optimization problem; Computation; Multi-swarm optimization; Mathematics; Algorithm; Engineering; Machine learning","score_opus":0.0551484492833532,"score_gpt":0.26404139441171676,"score_spread":0.20889294512836357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388808998","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":[0.000105135165,0.000024263192,0.9961665,0.00003866931,0.00017021799,0.0018925731,0.000010004309,0.0015139567,0.00007865653],"genre_scores_gemma":[0.053903885,0.000006238496,0.9455462,0.000066132365,0.000087911605,0.00018182956,0.000103766026,0.0000720276,0.00003202241],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99762094,0.000071333874,0.00044198465,0.0009804326,0.00027079348,0.00061450474],"domain_scores_gemma":[0.99823177,0.0006810913,0.00027604253,0.00034061,0.00032362354,0.0001468599],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006646903,0.000376715,0.00040730607,0.00033593565,0.00047961628,0.00020523398,0.00046406998,0.000128144,6.866049e-7],"category_scores_gemma":[0.0001528073,0.00041203317,0.000067172776,0.0013925933,0.00007152251,0.00034421388,0.00042861782,0.00022036345,0.000005331443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016179609,0.00007032832,0.0000086465,0.00004149903,0.000052540952,0.0000019791346,0.0010642597,0.98257446,0.0008250866,0.0011183004,0.00001647764,0.014210249],"study_design_scores_gemma":[0.0017804682,0.000070883674,0.00042801411,0.000025497495,0.00001412142,0.00001038378,0.00017584028,0.99655896,0.0003935236,0.00006523742,0.00002607213,0.00045097497],"about_ca_topic_score_codex":0.0000071714558,"about_ca_topic_score_gemma":5.1203887e-7,"teacher_disagreement_score":0.05379875,"about_ca_system_score_codex":0.00013439309,"about_ca_system_score_gemma":0.00006694665,"threshold_uncertainty_score":0.99983317},"labels":[],"label_agreement":null},{"id":"W4388896605","doi":"10.1016/j.swevo.2023.101432","title":"A constrained multi-objective evolutionary algorithm with clustering based weight vector adaptation","year":2023,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Education; CHEO Research Institute","keywords":"Cluster analysis; Mathematical optimization; Computer science; Population; Evolutionary algorithm; Classification of discontinuities; Convergence (economics); Multi-objective optimization; Set (abstract data type); Pareto principle; Optimization problem; Algorithm; Mathematics; Artificial intelligence","score_opus":0.01732310024901803,"score_gpt":0.24958132146428144,"score_spread":0.2322582212152634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388896605","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":[0.0010126546,0.00015307136,0.99599904,0.00062677416,0.00043918577,0.0005966639,0.000043307104,0.0009804601,0.00014881857],"genre_scores_gemma":[0.2779645,0.000024303035,0.7213264,0.00011123517,0.00008867386,0.000095655785,0.00024335567,0.000027580527,0.0001182865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978278,0.00016775323,0.00034529623,0.0007701771,0.00047150612,0.00041743694],"domain_scores_gemma":[0.99858123,0.00029901607,0.00020322666,0.0002285331,0.0005226953,0.00016527723],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020209717,0.00030748828,0.00024260797,0.0005256215,0.0005700085,0.000088447094,0.0002172464,0.000105969506,0.000007540526],"category_scores_gemma":[0.00005981197,0.00030555384,0.000060253555,0.001559893,0.0002011443,0.0011754133,0.00014574749,0.00018641862,0.000056704102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004696946,0.00011397892,0.00024309475,0.000025240617,0.00004216475,0.000042507665,0.0008534268,0.9547385,0.00006860353,0.0007595298,0.00019377108,0.042872217],"study_design_scores_gemma":[0.0022361446,0.0002136604,0.037595116,0.00005527662,0.000014525928,0.00008608723,0.00030830264,0.9575371,0.000043349657,0.001412652,0.00012004741,0.00037773108],"about_ca_topic_score_codex":0.000039689476,"about_ca_topic_score_gemma":0.000019982066,"teacher_disagreement_score":0.27695185,"about_ca_system_score_codex":0.0002841589,"about_ca_system_score_gemma":0.0003238435,"threshold_uncertainty_score":0.9999397},"labels":[],"label_agreement":null},{"id":"W4389444046","doi":"10.1007/978-981-19-8851-6_40-1","title":"Overcoming Constraints: The Critical Role of Penalty Functions as Constraint-Handling Methods in Structural Optimization","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Western University","funders":"","keywords":"Mathematical optimization; Constraint (computer-aided design); Robustness (evolution); Penalty method; Computer science; Optimization problem; Constrained optimization problem; Variety (cybernetics); Constrained optimization; Engineering; Mathematics; Artificial intelligence","score_opus":0.028300588328692106,"score_gpt":0.34362735257027427,"score_spread":0.3153267642415822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389444046","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":[8.7900213e-7,0.00008386228,0.8133552,0.00024161399,0.00054636796,0.00037598604,0.000033347245,0.00017583519,0.18518692],"genre_scores_gemma":[0.0012433267,0.000055475186,0.9722972,0.00015929846,0.00007730877,0.000022403236,0.000030478885,0.00005719502,0.026057329],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975975,0.00015807858,0.0007863749,0.000695699,0.00041310274,0.0003492013],"domain_scores_gemma":[0.99636054,0.0020880662,0.0003150213,0.00061465293,0.0005139973,0.00010770127],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006751139,0.000381123,0.0005104306,0.0004012873,0.00022118274,0.0001286602,0.0006726115,0.000287863,0.000828485],"category_scores_gemma":[0.0010600123,0.0003125533,0.00017902594,0.00027950227,0.00083249644,0.0004958918,0.00040010238,0.0006585554,0.00003259395],"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":[0.0000059189356,0.000008220243,0.0000049531477,0.000015110689,0.00004393828,0.00000987102,0.0002829645,0.23910898,0.0000622504,0.7238057,0.000008619051,0.036643434],"study_design_scores_gemma":[0.0003966787,0.000047374215,0.000015570273,0.00015164883,0.000031861586,0.00008613832,0.00043211557,0.92381746,0.00020281436,0.07409646,0.00034662432,0.0003752552],"about_ca_topic_score_codex":0.000032509775,"about_ca_topic_score_gemma":0.000021672708,"teacher_disagreement_score":0.6847085,"about_ca_system_score_codex":0.00015950785,"about_ca_system_score_gemma":0.0003214929,"threshold_uncertainty_score":0.99993265},"labels":[],"label_agreement":null},{"id":"W4389704887","doi":"10.1016/j.knosys.2023.111227","title":"A fuzzy-guided adaptive algorithm with hierarchy mechanism for solving dynamic multi-objective optimization problems","year":2023,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Population; Mathematical optimization; Fuzzy logic; Benchmark (surveying); Computer science; Algorithm; Evolutionary algorithm; Mathematics; Artificial intelligence","score_opus":0.031460737057492257,"score_gpt":0.2840208351324829,"score_spread":0.25256009807499064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389704887","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":[0.000039911105,0.00021071165,0.99112284,0.000106462794,0.0014983551,0.0045217327,0.00010657474,0.0019047451,0.00048866635],"genre_scores_gemma":[0.06206262,0.000018786128,0.9319294,0.000056876106,0.00015894539,0.003393322,0.00016916786,0.00018395383,0.0020269211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957526,0.00031756875,0.0007873642,0.0015423562,0.000576092,0.0010240264],"domain_scores_gemma":[0.9949716,0.0006500062,0.0005629583,0.0009425241,0.0025738704,0.00029905624],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000904803,0.00066262623,0.0007108858,0.001041102,0.00072075805,0.0003770737,0.0009808274,0.00025206996,0.0000028555382],"category_scores_gemma":[0.0002604769,0.00061019487,0.0001955633,0.0030546603,0.00013631617,0.0010230242,0.00022553689,0.00030873588,0.00010450798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033439155,0.00023568307,0.000010015857,0.00014783947,0.00012242659,0.000019770616,0.0019785103,0.98494685,0.00037729094,0.006772862,0.00010422392,0.00525107],"study_design_scores_gemma":[0.004392286,0.00055017415,0.00002300417,0.00040708602,0.000036675872,0.00003057486,0.0005398587,0.9912951,0.0010480355,0.0007905103,0.0000918285,0.00079486746],"about_ca_topic_score_codex":0.00005868998,"about_ca_topic_score_gemma":0.00007788025,"teacher_disagreement_score":0.06202271,"about_ca_system_score_codex":0.00089012366,"about_ca_system_score_gemma":0.00069727347,"threshold_uncertainty_score":0.9996349},"labels":[],"label_agreement":null},{"id":"W4390590962","doi":"10.1007/s11227-023-05790-3","title":"Prism refraction search: a novel physics-based metaheuristic algorithm","year":2024,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":44,"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":"Metaheuristic; Computer science; Algorithm; Test suite; Robustness (evolution); Suite; Benchmark (surveying); Heuristics; Prism; Test case; Machine learning; Optics","score_opus":0.033782771308699056,"score_gpt":0.2937976566970732,"score_spread":0.26001488538837414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390590962","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":[0.0015098499,0.0005197536,0.99563867,0.0009485997,0.0010622363,0.00011742601,0.0000016318785,0.000120921846,0.00008090573],"genre_scores_gemma":[0.19092873,0.000028354614,0.8082018,0.0001839147,0.00058446074,7.851073e-7,7.935958e-7,0.000024847803,0.00004630391],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982686,0.00019615421,0.00046490633,0.00021095613,0.0005789047,0.0002804512],"domain_scores_gemma":[0.99808824,0.0010309248,0.00015457736,0.0003031835,0.00032791804,0.000095171665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016616576,0.00017030833,0.0002251502,0.00019881852,0.00024676608,0.00025346281,0.0007543879,0.000044012333,0.0000058174114],"category_scores_gemma":[0.00014617998,0.00011684459,0.00014492376,0.00085396296,0.00007107369,0.0010428743,0.00015540532,0.0006786301,0.000024015964],"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":[0.000010462191,0.00012778348,0.0000074505538,0.000029879582,0.00011211989,0.00007925999,0.0024220352,0.3982857,0.0073491363,0.0010040409,0.00009583958,0.5904763],"study_design_scores_gemma":[0.0003477062,0.00009706645,0.0001102767,0.00014307686,0.000036619247,0.00042333012,0.000117130425,0.9938705,0.0032548895,0.00088182953,0.0005856684,0.0001318838],"about_ca_topic_score_codex":0.000015391168,"about_ca_topic_score_gemma":2.011324e-7,"teacher_disagreement_score":0.5955848,"about_ca_system_score_codex":0.00018153687,"about_ca_system_score_gemma":0.00023964475,"threshold_uncertainty_score":0.4764784},"labels":[],"label_agreement":null},{"id":"W4390751793","doi":"10.1145/3637065","title":"Evolutionary Optimization with a Simplified Helper Task for High-Dimensional Expensive Multiobjective Problems","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Evolutionary Learning and Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 British Columbia","funders":"Guangdong Provincial Pearl River Talents Program; National Natural Science Foundation of China","keywords":"Benchmark (surveying); Human multitasking; Task (project management); Surrogate model; Computer science; Curse of dimensionality; Dimension (graph theory); Evolutionary algorithm; Dimensionality reduction; Artificial intelligence; Machine learning; Convergence (economics); Mathematical optimization; Optimization problem; Algorithm; Mathematics; Engineering","score_opus":0.009643095064869094,"score_gpt":0.24360168410106506,"score_spread":0.23395858903619596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390751793","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":[0.00012053815,0.0006455549,0.995406,0.0011283898,0.0004184194,0.0011969344,0.00006155934,0.0009557869,0.00006685432],"genre_scores_gemma":[0.1494789,0.0002475998,0.8481442,0.0001303551,0.0000880841,0.00057573605,0.0003263638,0.000076624405,0.0009321751],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99731857,0.00018728513,0.00041578014,0.0011701531,0.00047302956,0.00043521292],"domain_scores_gemma":[0.9977592,0.0006888144,0.00015655262,0.0004011117,0.00082986295,0.00016444598],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019574953,0.00042488487,0.00029534067,0.0006258183,0.0012042271,0.00021483871,0.00027472767,0.00021174873,0.00006359908],"category_scores_gemma":[0.00016982765,0.0004040877,0.00010395743,0.0011017696,0.00017605527,0.0018200629,0.00003497979,0.00046834393,0.000015445765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015212577,0.00017042602,0.000012111749,0.000046297333,0.00011834945,0.0000069437506,0.0005087099,0.98793876,0.00007345345,0.0013690024,0.00007588744,0.009527943],"study_design_scores_gemma":[0.0013960737,0.00069464365,0.000132401,0.00018861196,0.00006620721,0.00013084426,0.00017876507,0.9952179,0.000094509,0.0009235364,0.0004688405,0.0005076769],"about_ca_topic_score_codex":0.000030390236,"about_ca_topic_score_gemma":0.0000037386194,"teacher_disagreement_score":0.14935836,"about_ca_system_score_codex":0.00041406768,"about_ca_system_score_gemma":0.00028130907,"threshold_uncertainty_score":0.9998411},"labels":[],"label_agreement":null},{"id":"W4391037431","doi":"10.1016/b978-0-32-395365-8.00019-1","title":"Multi-objective archived-based whale optimization algorithm","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Optimization algorithm; Whale; Computer science; Mathematical optimization; Meta heuristic; Grid; Selection (genetic algorithm); Heuristic; Algorithm; Engineering optimization; Optimization problem; Artificial intelligence; Mathematics; Fishery","score_opus":0.014487197367694332,"score_gpt":0.2519603506566221,"score_spread":0.23747315328892776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391037431","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":[1.830869e-8,0.0005747998,0.55021226,0.00006227116,0.00093529205,0.0008137936,0.00009019141,0.00060280686,0.44670856],"genre_scores_gemma":[0.0000027247984,0.000056630986,0.5042495,0.0002653066,0.00019647877,0.000101211845,0.00007437982,0.0001545487,0.49489918],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957459,0.00007867621,0.0008075809,0.0019323562,0.0007847764,0.00065070466],"domain_scores_gemma":[0.9968898,0.00022006419,0.00051248336,0.0014952344,0.000558602,0.00032376178],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027550166,0.0010486798,0.000838888,0.00085651514,0.00029395928,0.00042900405,0.0013650184,0.0004998529,0.00012132793],"category_scores_gemma":[0.000048359132,0.001074,0.00049399753,0.000107922,0.00030141664,0.0004035596,0.0006715547,0.0011409648,0.0006234441],"study_design_candidate":"design_other","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":[0.0000065147196,0.000038308517,2.1076382e-7,0.00006607153,0.00015042706,0.00016886355,0.00044498162,0.07485987,0.000009371217,0.0051901005,0.000030384379,0.9190349],"study_design_scores_gemma":[0.00077090773,0.00010760515,0.0000017472967,0.0005111391,0.00009179229,0.000039443938,0.000008263453,0.8232571,0.00015990796,0.00639962,0.16758576,0.0010667313],"about_ca_topic_score_codex":0.0000010574333,"about_ca_topic_score_gemma":0.000012265053,"teacher_disagreement_score":0.91796815,"about_ca_system_score_codex":0.00051666447,"about_ca_system_score_gemma":0.0005190743,"threshold_uncertainty_score":0.999171},"labels":[],"label_agreement":null},{"id":"W4391306046","doi":"10.1109/smc53992.2023.10394458","title":"Compact NSGA-II for Multi-objective Feature Selection","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Brock University; Ontario Tech University","funders":"","keywords":"Feature selection; Computer science; Metric (unit); Artificial intelligence; Selection (genetic algorithm); Evolutionary algorithm; Binary classification; Feature (linguistics); Population; Binary number; Task (project management); Data mining; Machine learning; Feature vector; Optimization problem; Compact space; Multi-objective optimization; Pattern recognition (psychology); Algorithm; Mathematics; Support vector machine; Engineering","score_opus":0.06181858722780776,"score_gpt":0.3416014521611343,"score_spread":0.27978286493332655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391306046","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":[0.000050067323,0.00005742709,0.9903348,0.0014306229,0.0027974306,0.0022426285,0.00010207367,0.0024331822,0.0005517873],"genre_scores_gemma":[0.0039480035,0.000042556494,0.96521854,0.00028155683,0.0003412659,0.00036789104,0.00015888095,0.00010878871,0.02953253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99674016,0.0001306671,0.00039213922,0.0016993956,0.0004143336,0.0006232772],"domain_scores_gemma":[0.99714047,0.00029085964,0.00042000267,0.00089493475,0.0010650145,0.00018869533],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003877281,0.0006123902,0.0006081859,0.0005068318,0.00056396966,0.0003151805,0.001308819,0.00056388276,0.00001632618],"category_scores_gemma":[0.00041726138,0.00059111946,0.00034920266,0.00087950804,0.00006355561,0.00054332195,0.0015181687,0.000959055,0.00007212198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007450463,0.00060381094,0.00045620796,0.00022302709,0.000546208,0.000014217425,0.0030805706,0.9364859,0.00039780998,0.01203771,0.030603664,0.015476403],"study_design_scores_gemma":[0.0011056461,0.00014392618,0.002831509,0.00007064971,0.000025312167,0.0000123534855,0.00006339835,0.9863873,0.0021375548,0.0044684536,0.002046531,0.0007073825],"about_ca_topic_score_codex":0.00012240764,"about_ca_topic_score_gemma":0.00018058106,"teacher_disagreement_score":0.049901415,"about_ca_system_score_codex":0.0006088451,"about_ca_system_score_gemma":0.0003650351,"threshold_uncertainty_score":0.999654},"labels":[],"label_agreement":null},{"id":"W4391308277","doi":"10.1109/smc53992.2023.10394620","title":"Ranking Center-based NSGA-II","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Brock University; Ontario Tech University","funders":"","keywords":"Benchmark (surveying); Mathematical optimization; Cluster analysis; Multi-objective optimization; Computer science; Ranking (information retrieval); Evolutionary algorithm; Evolutionary computation; Sampling (signal processing); Pareto principle; Computation; Population; Optimization problem; Set (abstract data type); Algorithm; Mathematics; Machine learning","score_opus":0.019424780803398788,"score_gpt":0.27294786421223877,"score_spread":0.25352308340884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391308277","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":[0.000560143,0.0000045296806,0.99104273,0.0010092924,0.00040096827,0.000119136195,0.0000016920824,0.0012494919,0.0056120125],"genre_scores_gemma":[0.13706468,0.0000057874604,0.8577578,0.0010925253,0.000053605807,0.000024331443,0.000012755907,0.00001824311,0.003970246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990266,0.00003393238,0.00013398277,0.0003272142,0.0002172897,0.00026098845],"domain_scores_gemma":[0.99934685,0.00008191259,0.00004296816,0.00037383277,0.00008950836,0.000064936205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015084608,0.00009982602,0.000091769354,0.00017627783,0.00019998534,0.0000653162,0.00047197033,0.000029357834,0.00006168707],"category_scores_gemma":[0.000059851078,0.000091734495,0.00004578354,0.0010337321,0.000022188267,0.00037405972,0.0002662408,0.000067319204,0.0003388461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018675888,0.00045156982,0.003017284,0.00002971254,0.00004872711,0.00016375979,0.0017342141,0.62657654,0.0022553315,0.079991154,0.008669142,0.27704388],"study_design_scores_gemma":[0.00069131586,0.00002906729,0.00071463914,0.000007835209,7.517479e-7,0.000002622866,0.000013727495,0.9894816,0.0025447218,0.0007722575,0.005605915,0.0001355147],"about_ca_topic_score_codex":0.0000045210754,"about_ca_topic_score_gemma":0.0000033379583,"teacher_disagreement_score":0.36290509,"about_ca_system_score_codex":0.00004075828,"about_ca_system_score_gemma":0.000036475896,"threshold_uncertainty_score":0.4355294},"labels":[],"label_agreement":null},{"id":"W4392704563","doi":"10.1287/ijoc.2021.0186","title":"A Dual Bounding Framework Through Cost Splitting for Binary Quadratic Optimization","year":2024,"lang":"en","type":"article","venue":"INFORMS journal on computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"HEC Montréal; Polytechnique Montréal; University of Alberta; Université de Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Bounding overwatch; Dual (grammatical number); Mathematical optimization; Binary number; Quadratic equation; Mathematics; Quadratic programming; Computer science; Arithmetic; Artificial intelligence","score_opus":0.029879238130132633,"score_gpt":0.33712413251070306,"score_spread":0.3072448943805704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392704563","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":[0.0008146057,0.00021234683,0.993101,0.00070629065,0.0035503025,0.00047389782,0.0000027832418,0.00043745642,0.00070129766],"genre_scores_gemma":[0.07984861,0.000035930163,0.9182905,0.0007655511,0.0009299408,0.000011917514,0.000006393811,0.000041189243,0.00006997061],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977257,0.000048388953,0.0007811759,0.00040715898,0.00046922063,0.00056834886],"domain_scores_gemma":[0.9976263,0.0012434691,0.00038462956,0.0002819094,0.00031714165,0.0001465553],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00094216946,0.0002836561,0.0002785173,0.0003444722,0.0009968989,0.0020315207,0.0005097084,0.00012758731,0.0000137780835],"category_scores_gemma":[0.00077513605,0.00024231683,0.00017911522,0.000992483,0.000047115074,0.0028513626,0.00021210714,0.0007741547,0.00003080098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011058229,0.000025504209,0.000014261887,0.000042323965,0.000037855803,0.00005795424,0.0024700048,0.8822748,0.000012433136,0.042834897,0.00010907181,0.07210982],"study_design_scores_gemma":[0.00041044783,0.00022382822,0.000016922508,0.0009355034,0.000010108266,0.00047430987,0.00023485385,0.9865934,0.00011671408,0.007924789,0.0027529565,0.00030615903],"about_ca_topic_score_codex":0.0000012331059,"about_ca_topic_score_gemma":1.4798395e-7,"teacher_disagreement_score":0.10431859,"about_ca_system_score_codex":0.00042133068,"about_ca_system_score_gemma":0.00023603487,"threshold_uncertainty_score":0.9990045},"labels":[],"label_agreement":null},{"id":"W4393108629","doi":"10.48550/arxiv.2504.09930","title":"Multi-objective Bayesian Optimization With Mixed-categorical Design Variables for Expensive-to-evaluate Aeronautical Applications","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Polytechnique Montréal","funders":"Office National d'études et de Recherches Aérospatiales; European Commission","keywords":"Categorical variable; Bayesian optimization; Computer science; Bayesian probability; Mathematical optimization; Artificial intelligence; Machine learning; Mathematics","score_opus":0.053877816185044725,"score_gpt":0.3173958982520774,"score_spread":0.2635180820670327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393108629","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":[0.00004222974,0.00009917722,0.9907428,0.00092722557,0.00065075984,0.006616701,0.00006933545,0.0006357211,0.00021604105],"genre_scores_gemma":[0.014455366,0.00004816034,0.9762226,0.00060713536,0.00018408289,0.0073899175,0.00018235348,0.00008589233,0.0008245317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99509895,0.0004085307,0.00075648766,0.0024098097,0.00054031226,0.0007859298],"domain_scores_gemma":[0.9942678,0.0010201563,0.00047367022,0.0017443732,0.0021015618,0.00039240863],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00059140014,0.0008009717,0.00082242436,0.0005236347,0.00054951606,0.00026196972,0.0017487952,0.00050305604,0.00001964797],"category_scores_gemma":[0.0006239809,0.00077052414,0.00018940226,0.0012664296,0.00016948498,0.000513004,0.0015075048,0.0007074004,0.00004815942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009911596,0.00036440653,0.0004089592,0.000066338886,0.00016755186,0.000006980362,0.00044721554,0.9911383,0.00006214582,0.0035399161,0.00015612194,0.003542973],"study_design_scores_gemma":[0.0015888892,0.00021119142,0.0013849661,0.00010945668,0.00014171838,0.000011598747,0.000093852424,0.9917367,0.0015169656,0.0019010797,0.00041837528,0.0008852224],"about_ca_topic_score_codex":0.000027724887,"about_ca_topic_score_gemma":0.000011139643,"teacher_disagreement_score":0.014520249,"about_ca_system_score_codex":0.0007053796,"about_ca_system_score_gemma":0.0011342244,"threshold_uncertainty_score":0.9994746},"labels":[],"label_agreement":null},{"id":"W4393403568","doi":"10.2139/ssrn.4780792","title":"Experimental Design through an Optimization Lens","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Quest University Canada","funders":"","keywords":"Lens (geology); Computer science; Optics; Physics","score_opus":0.02382728496989278,"score_gpt":0.2960992441367749,"score_spread":0.2722719591668821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393403568","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":[0.000081404214,0.0042080097,0.9936087,0.00038126932,0.00063362654,0.00016303672,6.2940376e-7,0.00033482575,0.00058849156],"genre_scores_gemma":[0.1311065,0.002222419,0.8655658,0.00016736287,0.00030556356,0.000016225991,0.000004134976,0.000045160297,0.00056683546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99752575,0.00016521447,0.00024376609,0.00041802632,0.0003053772,0.0013418748],"domain_scores_gemma":[0.9994321,0.000049945378,0.00007577791,0.00026243096,0.00010693211,0.00007283918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006925069,0.0001884707,0.00013087131,0.00013900452,0.00028045615,0.00040855224,0.00056733773,0.00006535399,0.00004344627],"category_scores_gemma":[0.000026007325,0.00017381056,0.000074291675,0.0005002197,0.000039220966,0.0048720217,0.000071647184,0.0009053102,0.0000418471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000097222855,0.00008840576,0.000001891504,0.000001527724,0.000052375268,0.000019570218,0.0009796431,0.6112487,0.0008567123,0.37323612,0.000020268853,0.013485076],"study_design_scores_gemma":[0.0003530246,0.00048188082,0.0000017623848,0.000015695816,0.000007923376,0.000978902,0.0005095356,0.9446091,0.0035565645,0.048656747,0.0005904798,0.00023838111],"about_ca_topic_score_codex":0.0000063096636,"about_ca_topic_score_gemma":0.000004797827,"teacher_disagreement_score":0.3333604,"about_ca_system_score_codex":0.0012150146,"about_ca_system_score_gemma":0.0014026947,"threshold_uncertainty_score":0.7087789},"labels":[],"label_agreement":null},{"id":"W4396566994","doi":"10.23952/jnva.8.2024.4.03","title":"An inexact nonmonotone projected gradient method for constrained multiobjective optimization","year":2024,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Multi-objective optimization; Computer science; Gradient method; Proximal Gradient Methods; Constrained optimization problem; Mathematics; Applied mathematics; Optimization problem; Gradient descent; Artificial intelligence; Artificial neural network","score_opus":0.012907212004415157,"score_gpt":0.3293124258374892,"score_spread":0.3164052138330741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396566994","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":[0.00019683172,0.00010474852,0.99867105,0.00049186364,0.00018096202,0.00021440923,0.000068745976,0.000045821973,0.000025543577],"genre_scores_gemma":[0.024687538,0.000042795044,0.9748147,0.00008118901,0.00023755961,0.000012420737,0.00007464105,0.000011349815,0.000037800313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998578,0.00013579288,0.0005196941,0.00032457107,0.000300551,0.00014139923],"domain_scores_gemma":[0.99779916,0.00046609048,0.00032082526,0.00014012106,0.0011529613,0.00012082888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076912803,0.0001492181,0.00033189091,0.00093018287,0.00012952139,0.00023797638,0.00020781596,0.00006641668,0.000020346986],"category_scores_gemma":[0.00023683933,0.00012225498,0.00023238889,0.0016851017,0.00002890924,0.0011184681,0.000029154919,0.00014430075,5.8614006e-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":[0.000041896994,0.00016227283,0.000087985994,0.000011165318,0.0011232726,0.000007796765,0.00068390433,0.9837354,0.00028957945,0.005138109,0.0000073217084,0.008711298],"study_design_scores_gemma":[0.000652418,0.00023188793,0.0010608207,0.000012855672,0.0005069109,0.000053462387,0.000063643005,0.99621165,0.00013529412,0.00079787755,0.00013271095,0.0001404633],"about_ca_topic_score_codex":0.000014346902,"about_ca_topic_score_gemma":0.000005859536,"teacher_disagreement_score":0.024490707,"about_ca_system_score_codex":0.00010246825,"about_ca_system_score_gemma":0.00024944195,"threshold_uncertainty_score":0.49854133},"labels":[],"label_agreement":null},{"id":"W4396718738","doi":"10.48550/arxiv.2405.02983","title":"CVXSADes: a stochastic algorithm for constructing optimal exact regression designs with single or multiple objectives","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Algorithm; Mathematical optimization; Regression; Mathematics; Statistics","score_opus":0.08872447866530268,"score_gpt":0.23605253591144282,"score_spread":0.14732805724614012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396718738","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":[0.003741891,0.000086815744,0.9928484,0.00002711967,0.0008194563,0.0014325535,0.00010994449,0.00077358185,0.00016022964],"genre_scores_gemma":[0.3268103,0.000015457297,0.6717649,0.000017651852,0.00010467078,0.000026616326,0.000023615306,0.00007406528,0.0011627158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964633,0.00015955674,0.00033265937,0.002212219,0.00018832828,0.00064393587],"domain_scores_gemma":[0.9967422,0.0009292003,0.0005022819,0.000992139,0.00057709287,0.00025709326],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023109748,0.00069267704,0.00062370166,0.0005666267,0.00036287203,0.0003318046,0.0012329839,0.0003507502,0.000021336986],"category_scores_gemma":[0.00026117664,0.0006036032,0.00023785332,0.0010778691,0.00032721544,0.00066069874,0.0018628055,0.0008248128,0.000021253476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020831147,0.00016348858,0.000025317404,0.0001589992,0.00022373321,0.0003868409,0.00092419726,0.97882336,0.00012267695,0.0024974113,0.000025770747,0.016439907],"study_design_scores_gemma":[0.0016447123,0.00042600813,0.000009132135,0.00085738953,0.00013838161,0.00008618225,0.0013013651,0.98916674,0.0011803717,0.004335898,0.000027551503,0.0008262733],"about_ca_topic_score_codex":0.000030463267,"about_ca_topic_score_gemma":0.000018980982,"teacher_disagreement_score":0.3230684,"about_ca_system_score_codex":0.00068798725,"about_ca_system_score_gemma":0.0006785516,"threshold_uncertainty_score":0.99964154},"labels":[],"label_agreement":null},{"id":"W4396829234","doi":"10.1007/s00158-024-03785-z","title":"High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft","year":2024,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer 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":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Office National d'études et de Recherches Aérospatiales; European Commission","keywords":"Categorical variable; Hyperparameter; Bayesian optimization; Context (archaeology); Kriging; Dimensionality reduction; Mathematical optimization; Dimension (graph theory); Computer science; Gaussian process; Engineering design process; Reduction (mathematics); Gaussian; Machine learning; Artificial intelligence; Mathematics; Engineering","score_opus":0.01160922037110698,"score_gpt":0.2675283932445456,"score_spread":0.2559191728734386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396829234","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":[0.001419876,0.00025711814,0.9922141,0.002381679,0.0004997543,0.0024654914,0.00005505039,0.00069210504,0.000014823073],"genre_scores_gemma":[0.15691406,0.00003963445,0.84151745,0.000044775745,0.00019560558,0.00064515416,0.0003581481,0.00007104351,0.00021412404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99704164,0.00009492245,0.00050577614,0.0014229703,0.0004273111,0.0005073758],"domain_scores_gemma":[0.99811625,0.00035300586,0.0001659176,0.00042703626,0.0006214156,0.00031635145],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021838483,0.0005187436,0.00036991647,0.0004232804,0.00086939614,0.0003120337,0.0004306335,0.00017504579,0.000013552717],"category_scores_gemma":[0.000091914546,0.0004081213,0.000063682506,0.0015691107,0.00012649831,0.001979404,0.0003901035,0.00018601005,0.000007581194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022449711,0.00004791731,0.0000315885,0.000257594,0.0000412299,0.000014255935,0.00092963385,0.98164,0.00019929033,0.0027250184,0.000033828324,0.0138551565],"study_design_scores_gemma":[0.0010023066,0.00057736854,0.00042764941,0.000104945764,0.00006879701,0.00014073309,0.00009666719,0.9937491,0.00064138277,0.0025446124,0.000019739,0.0006267455],"about_ca_topic_score_codex":0.000027688357,"about_ca_topic_score_gemma":0.000023347424,"teacher_disagreement_score":0.15549418,"about_ca_system_score_codex":0.0001834652,"about_ca_system_score_gemma":0.0003073961,"threshold_uncertainty_score":0.99983704},"labels":[],"label_agreement":null},{"id":"W4396877656","doi":"10.1109/tem.2024.3399773","title":"Product Concept Development and Evaluation Using Multiagent Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Engineering Management","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"New product development; Reinforcement learning; Computer science; Product (mathematics); Engineering; Knowledge management; Manufacturing engineering; Artificial intelligence; Business; Marketing; Mathematics","score_opus":0.020888549429454305,"score_gpt":0.26316011754327406,"score_spread":0.24227156811381975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396877656","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":[0.00085236557,0.00022033774,0.9966851,0.00005070991,0.00095779373,0.0006251113,3.7445707e-7,0.00047806048,0.00013013413],"genre_scores_gemma":[0.6225519,0.000039198094,0.37680045,0.000016220714,0.00001949187,0.000118699674,0.0000018004542,0.000020141535,0.0004320765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986602,0.000026494125,0.00022731621,0.00047263244,0.0003932019,0.00022013953],"domain_scores_gemma":[0.9995955,0.000030612748,0.00002954499,0.00021805869,0.0000612618,0.00006501733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028911294,0.0001869714,0.000102252845,0.00034666123,0.0001666968,0.00015621947,0.00013835658,0.00002426527,0.000018050481],"category_scores_gemma":[0.0000038569224,0.0001984546,0.00003350165,0.0004186373,0.000013311706,0.0004165998,0.000009110226,0.0001711231,0.000015793952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012135868,0.000017884247,1.5750125e-7,0.00005243155,0.00007093302,0.000008131725,0.00071924896,0.79356444,0.00016322058,0.00039523919,0.000002728117,0.20500436],"study_design_scores_gemma":[0.00027998723,0.000026180156,0.000034477318,0.00011956118,0.000032829106,0.000007578955,0.000044378918,0.98538345,0.011187995,0.000005807257,0.002663836,0.00021389956],"about_ca_topic_score_codex":0.0000028978761,"about_ca_topic_score_gemma":4.4173163e-7,"teacher_disagreement_score":0.6216996,"about_ca_system_score_codex":0.00041397728,"about_ca_system_score_gemma":0.000033156244,"threshold_uncertainty_score":0.8092744},"labels":[],"label_agreement":null},{"id":"W4396903002","doi":"10.1002/nme.7498","title":"A solution to the ill‐conditioning of gradient‐enhanced covariance matrices for Gaussian processes","year":2024,"lang":"en","type":"article","venue":"International Journal for Numerical Methods in Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Conditioning; Covariance; Gaussian; Applied mathematics; Mathematics; Covariance matrix; Statistical physics; Statistics; Physics","score_opus":0.023548986070546018,"score_gpt":0.3915172263562382,"score_spread":0.3679682402856922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396903002","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":[0.000035059933,0.0004881198,0.99372494,0.0016978162,0.0036499056,0.00030376783,0.000016672715,0.0000650004,0.000018732873],"genre_scores_gemma":[0.028387534,0.000045119887,0.970958,0.00008855874,0.00028778936,0.00017080156,0.0000027811725,0.000018050869,0.00004137993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989124,0.000040263498,0.00038247314,0.00023484288,0.00022605795,0.00020396712],"domain_scores_gemma":[0.998067,0.0011283604,0.000113662485,0.00009706569,0.00053347705,0.000060391925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006908017,0.00011538318,0.00015333119,0.00034070946,0.00007366,0.00019086793,0.0006460165,0.000034127956,0.0000035991263],"category_scores_gemma":[0.0023158381,0.00009230271,0.000091059264,0.00072506984,0.000013472089,0.00057231967,0.00007408712,0.00016395855,0.0000011160683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021970001,0.000025610594,0.000002890135,0.000073062016,0.00006120026,0.0000039801,0.0007427357,0.8611559,0.004162083,0.013266797,0.000049532457,0.12043423],"study_design_scores_gemma":[0.00024185865,0.00006952916,0.000040397157,0.00025821893,0.0000065557165,0.00006396822,0.00003422741,0.97567254,0.011540984,0.004519886,0.007432746,0.000119065066],"about_ca_topic_score_codex":0.000002617429,"about_ca_topic_score_gemma":7.0745926e-7,"teacher_disagreement_score":0.120315164,"about_ca_system_score_codex":0.0001968639,"about_ca_system_score_gemma":0.00007634504,"threshold_uncertainty_score":0.37639952},"labels":[],"label_agreement":null},{"id":"W4397005521","doi":"10.1007/s11425-023-2302-9","title":"Gradient-based algorithms for multi-objective bi-level optimization","year":2024,"lang":"en","type":"article","venue":"Science China Mathematics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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 Victoria","funders":"","keywords":"Computer science; Optimization algorithm; Algorithm; Mathematical optimization; Mathematics","score_opus":0.05965773506906244,"score_gpt":0.335359040844253,"score_spread":0.2757013057751906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4397005521","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":[0.0001178892,0.000113544644,0.99569553,0.00036434372,0.0014458663,0.0010510628,0.000052480504,0.0007482121,0.0004110653],"genre_scores_gemma":[0.007776851,0.000013026081,0.9912642,0.00009962218,0.00007123023,0.00018358316,0.00001153717,0.000046261317,0.00053365144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969866,0.000033240205,0.0004638173,0.0010865321,0.0007579842,0.0006717928],"domain_scores_gemma":[0.9979738,0.00026958252,0.00018127625,0.0007896562,0.00055972807,0.00022594219],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011759993,0.00034372043,0.0002952079,0.0007396189,0.0006459672,0.00087693793,0.0015434674,0.000085280444,0.000014218976],"category_scores_gemma":[0.00085147336,0.00029888726,0.00014942371,0.0032185898,0.00049863674,0.0021980803,0.00025648088,0.00019563579,0.000040898467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004797037,0.00051533536,0.000007833958,0.00027014175,0.000030034842,0.000017188715,0.006304179,0.84161896,0.001010668,0.116322756,0.000097446034,0.03380068],"study_design_scores_gemma":[0.0005172354,0.00010465625,0.00006158282,0.0001288518,0.00001419218,0.000023566081,0.00014611281,0.9854822,0.0058356426,0.0072162696,0.00008558489,0.00038407912],"about_ca_topic_score_codex":0.0000054372704,"about_ca_topic_score_gemma":0.0000022471,"teacher_disagreement_score":0.14386329,"about_ca_system_score_codex":0.0004139871,"about_ca_system_score_gemma":0.00066688523,"threshold_uncertainty_score":0.9999463},"labels":[],"label_agreement":null},{"id":"W4399858594","doi":"10.1007/s10589-024-00590-8","title":"Q-fully quadratic modeling and its application in a random subspace derivative-free method","year":2024,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Mathematics; Subspace topology; Derivative (finance); Quadratic equation; Applied mathematics; Quadratic model; Mathematical optimization; Combinatorics; Mathematical analysis; Statistics; Geometry","score_opus":0.01475561811600928,"score_gpt":0.30298641349624533,"score_spread":0.28823079538023605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399858594","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":[0.00009534386,0.0012893043,0.9946753,0.0021858292,0.000039684306,0.0010867633,0.00001422325,0.0003030688,0.00031048935],"genre_scores_gemma":[0.10955469,0.00021970677,0.88898915,0.00022945885,0.000045924065,0.0007782655,0.00008337042,0.00002579474,0.00007363355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998356,0.00010341164,0.00040631587,0.0007100413,0.00023583884,0.00018843383],"domain_scores_gemma":[0.99878347,0.00045738672,0.00009118685,0.0002704905,0.00028176032,0.000115699935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032031382,0.00020650125,0.00020896165,0.00038153146,0.0002319907,0.00027929235,0.00029940324,0.000079552985,0.0000066413845],"category_scores_gemma":[0.000085777036,0.00021997395,0.00003317209,0.0012930289,0.000042707878,0.0009080983,0.0001859568,0.0001633728,0.000010213377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003683585,0.00003169775,0.000010906803,0.000028169045,0.000009700486,8.495909e-7,0.00032594777,0.7675979,0.000032482527,0.2228129,0.000007846319,0.009137893],"study_design_scores_gemma":[0.00085069315,0.00001320022,0.000054,0.00003205295,0.000010021791,0.00002783178,0.000058013797,0.9654317,0.000023660226,0.032983866,0.00029319784,0.00022176452],"about_ca_topic_score_codex":0.000008170547,"about_ca_topic_score_gemma":0.0000065410463,"teacher_disagreement_score":0.19783379,"about_ca_system_score_codex":0.00008113783,"about_ca_system_score_gemma":0.00013548293,"threshold_uncertainty_score":0.89702773},"labels":[],"label_agreement":null},{"id":"W4400678945","doi":"10.1109/tro.2024.3428990","title":"Regret-Based Sampling of Pareto Fronts for Multiobjective Robot Planning Problems","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Regret; Pareto principle; Multi-objective optimization; Robot; Computer science; Sampling (signal processing); Mathematical optimization; Pareto optimal; Motion planning; Artificial intelligence; Mathematics; Machine learning; Computer vision","score_opus":0.05610988369127533,"score_gpt":0.3238587836040615,"score_spread":0.26774889991278616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400678945","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":[0.00002940425,0.00013449769,0.99688333,0.000204167,0.0014345684,0.0007512964,0.00006263534,0.00044176308,0.000058323763],"genre_scores_gemma":[0.2319015,0.000008826119,0.7676722,0.00006106194,0.000032969914,0.00012279686,0.0000055160767,0.000040193896,0.00015497643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983679,0.000046348436,0.00039538174,0.0005817334,0.00028210424,0.000326552],"domain_scores_gemma":[0.99847186,0.00060857745,0.00011239665,0.00042210773,0.00029014866,0.000094940355],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017238043,0.00024874695,0.00027596083,0.0003647926,0.00021855709,0.00010687912,0.00035897727,0.000119381126,0.000005526983],"category_scores_gemma":[0.00002578307,0.00024994832,0.00017875936,0.00065092125,0.00007183904,0.00045461638,0.0000032134942,0.00028107548,0.000009630287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021198523,0.00015195445,0.000006218123,0.000098730176,0.0000617522,0.0000034823931,0.00061435276,0.9812501,0.00068259565,0.0003982324,0.000013921108,0.01669748],"study_design_scores_gemma":[0.00063724915,0.00021561222,0.00003824593,0.00034476735,0.000038775135,0.0000052779005,0.000054810043,0.97955585,0.018290691,0.000504057,0.00005512861,0.0002595596],"about_ca_topic_score_codex":0.000011359606,"about_ca_topic_score_gemma":0.000010892926,"teacher_disagreement_score":0.2318721,"about_ca_system_score_codex":0.00020718899,"about_ca_system_score_gemma":0.00017746032,"threshold_uncertainty_score":0.9999953},"labels":[],"label_agreement":null},{"id":"W4400689395","doi":"10.1007/s10589-024-00588-2","title":"Handling of constraints in multiobjective blackbox optimization","year":2024,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; HEC Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Universidade Nova de Lisboa; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Mathematical optimization; Multi-objective optimization","score_opus":0.00938289075054676,"score_gpt":0.27438086545607177,"score_spread":0.264997974705525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400689395","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":[0.000043112545,0.00035364905,0.99732715,0.0003071495,0.00008479239,0.0006148139,0.000024275849,0.00020575205,0.0010392967],"genre_scores_gemma":[0.18541734,0.00012086731,0.81403327,0.00007942149,0.000031891672,0.00015486829,0.00010280512,0.000016984406,0.000042551583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861115,0.00005674944,0.0004533499,0.0005022061,0.0002193909,0.00015717161],"domain_scores_gemma":[0.99891955,0.0003393517,0.00012597138,0.00018122765,0.0003576821,0.000076191056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001815109,0.00016187363,0.00018550243,0.00048158976,0.00011845809,0.00014340301,0.0002059219,0.000075832046,0.00003989352],"category_scores_gemma":[0.00005465812,0.00017760269,0.00004347371,0.0013653289,0.00018995024,0.00073048146,0.0000867847,0.00012820834,0.0000074670265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021606113,0.00006314383,0.000052410007,0.000026098465,0.000013250139,0.0000014094013,0.0003483505,0.88042784,0.0000138889745,0.10559481,0.000007356585,0.013449304],"study_design_scores_gemma":[0.00044779826,0.000018345376,0.00021074298,0.00005908353,0.0000071661893,0.000017307091,0.0000813668,0.99442834,0.00006615397,0.0043771625,0.000117666015,0.00016888541],"about_ca_topic_score_codex":0.0000038810945,"about_ca_topic_score_gemma":0.0000011633388,"teacher_disagreement_score":0.18537422,"about_ca_system_score_codex":0.00008631386,"about_ca_system_score_gemma":0.00016311418,"threshold_uncertainty_score":0.72424275},"labels":[],"label_agreement":null},{"id":"W4400887229","doi":"10.1063/5.0214337","title":"Optimization framework for multi-fidelity surrogate model based on adaptive addition strategy—A case study of self-excited oscillation cavity","year":2024,"lang":"en","type":"article","venue":"Physics of Fluids","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Beijing Municipal Natural Science Foundation; National Natural Science Foundation of China","keywords":"Physics; Oscillation (cell signaling); Excited state; Fidelity; Self-oscillation; Statistical physics; Quantum electrodynamics; Applied mathematics; Quantum mechanics","score_opus":0.05778049419117064,"score_gpt":0.3347453272227133,"score_spread":0.2769648330315426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400887229","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":[0.009212444,0.000015865526,0.9889542,0.000017433333,0.00016796158,0.0011392405,0.00022068861,0.00024850774,0.000023691658],"genre_scores_gemma":[0.5155069,0.000002210623,0.48434374,0.0000116430865,0.00002697179,0.000055086308,0.000034258435,0.000016624328,0.0000025067186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822927,0.00013631536,0.00046511335,0.00058720255,0.0003879796,0.00019410769],"domain_scores_gemma":[0.99777734,0.00044498625,0.00022446428,0.00051181135,0.0009742324,0.00006714657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031285424,0.0002346181,0.00029283843,0.00016707084,0.00013178433,0.000059443537,0.00023321684,0.000098601115,0.0000030230078],"category_scores_gemma":[0.000112931906,0.0002444494,0.00011249833,0.0009181933,0.000045038454,0.0008950298,0.00005914989,0.00017539487,0.0000011441399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035645866,0.0010782627,0.000020362391,0.0000573098,0.000046172394,0.000011172333,0.0014801873,0.978746,0.00014119914,0.013441142,0.000006732893,0.004935823],"study_design_scores_gemma":[0.00096762524,0.0006240954,0.000050906445,0.0000745145,0.000044099972,0.0000027466742,0.00022799647,0.99094164,0.0009520576,0.0059045036,5.869294e-7,0.00020924368],"about_ca_topic_score_codex":0.000071016235,"about_ca_topic_score_gemma":0.0000096638305,"teacher_disagreement_score":0.5062945,"about_ca_system_score_codex":0.00016178361,"about_ca_system_score_gemma":0.00017680682,"threshold_uncertainty_score":0.9968356},"labels":[],"label_agreement":null},{"id":"W4401397640","doi":"10.1007/978-981-97-3820-5_40","title":"Overcoming Constraints: The Critical Role of Penalty Functions as Constraint-Handling Methods in Structural Optimization","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Constraint (computer-aided design); Penalty method; Mathematical optimization; Computer science; Mathematics","score_opus":0.01986767899879309,"score_gpt":0.3384124223212969,"score_spread":0.3185447433225038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401397640","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":[6.194401e-7,0.0003829522,0.6981002,0.00026163747,0.00055239175,0.0003070975,0.000029825074,0.00011677537,0.30024847],"genre_scores_gemma":[0.002263521,0.00005323826,0.9662593,0.00016617036,0.00009192429,0.000019171452,0.00002191873,0.000050318213,0.031074462],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977069,0.0001036262,0.0007625478,0.00073752855,0.00037591046,0.00031352043],"domain_scores_gemma":[0.99734366,0.0013326905,0.0002233297,0.0005697244,0.00042724333,0.00010333197],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00054284686,0.0003974937,0.00048418928,0.00039047323,0.00016615547,0.0001896162,0.00060397683,0.000280837,0.001273312],"category_scores_gemma":[0.00050366536,0.0003118007,0.0001957703,0.00023017883,0.00083551224,0.0004918243,0.0003960792,0.00078218535,0.00003242947],"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":[0.000004720946,0.0000075231833,0.0000017922457,0.000024490055,0.00005000862,0.000011718552,0.0002939758,0.1457641,0.00006477902,0.8259754,0.0000074211653,0.027794076],"study_design_scores_gemma":[0.0002496136,0.00004353102,0.0000029667237,0.00021102146,0.000046738995,0.00015221533,0.0003407531,0.86708623,0.00022723731,0.13023283,0.0010683212,0.0003385157],"about_ca_topic_score_codex":0.000024173256,"about_ca_topic_score_gemma":0.000013713668,"teacher_disagreement_score":0.7213222,"about_ca_system_score_codex":0.00019544218,"about_ca_system_score_gemma":0.00031472582,"threshold_uncertainty_score":0.9999334},"labels":[],"label_agreement":null},{"id":"W4401970555","doi":"10.2139/ssrn.4939115","title":"Population-Level Center-Based Sampling for Meta-Heuristic Algorithms","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Ontario Tech University; Brock University","funders":"","keywords":"Meta heuristic; Center (category theory); Heuristic; Sampling (signal processing); Computer science; Population; Algorithm; Mathematics; Artificial intelligence; Medicine; Computer vision","score_opus":0.07654958494798271,"score_gpt":0.3386249083362406,"score_spread":0.2620753233882579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401970555","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":[0.0000608713,0.005225248,0.9891282,0.0013503844,0.0028313794,0.00081170775,0.00018984856,0.0003286194,0.00007378024],"genre_scores_gemma":[0.047301557,0.00076358055,0.9486522,0.00027984637,0.00096167787,0.00044484768,0.00021251285,0.0001668799,0.0012169124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9944298,0.00015982556,0.0009011619,0.0011875158,0.00066534616,0.0026563376],"domain_scores_gemma":[0.9973924,0.00029344036,0.0007012549,0.0007944047,0.00062062824,0.00019785251],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0019843671,0.0006690768,0.0008516343,0.00062021264,0.00042387086,0.00071629114,0.0016818363,0.00041508404,0.000020713705],"category_scores_gemma":[0.00032059173,0.0006077453,0.0009476912,0.0004437254,0.00004385556,0.00030353785,0.00081996457,0.006165619,0.000026387153],"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":[0.000043128257,0.00028840703,0.00004786698,0.00021236404,0.0039181733,0.00001883197,0.00016400365,0.59049094,0.000015896745,0.26390755,0.00010069687,0.14079218],"study_design_scores_gemma":[0.00060912943,0.00010448535,0.000028865112,0.00006764684,0.0004283729,0.0001476851,0.000024009152,0.47784916,0.000024515008,0.5197056,0.0005645387,0.00044598896],"about_ca_topic_score_codex":0.000055931585,"about_ca_topic_score_gemma":0.00009326058,"teacher_disagreement_score":0.25579807,"about_ca_system_score_codex":0.0028039934,"about_ca_system_score_gemma":0.0040669776,"threshold_uncertainty_score":0.99963737},"labels":[],"label_agreement":null},{"id":"W4402028590","doi":"10.1609/aaai.v39i25.34894","title":"Scenario-Based Robust Optimization of Tree Structures","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for International Peace and Security","funders":"Agence Nationale de la Recherche","keywords":"Regret; Mathematics; Huffman coding; Binary search tree; Tree (set theory); Combinatorics; Time complexity; Upper and lower bounds; Context (archaeology); Discrete mathematics; Algorithm; Computer science; Binary tree; Data compression; Statistics","score_opus":0.048309295751383735,"score_gpt":0.29297862948640047,"score_spread":0.24466933373501673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402028590","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":[0.0014380488,0.000017603508,0.98875034,0.0015580708,0.00036898037,0.00040460838,0.0000045036263,0.00008226251,0.007375584],"genre_scores_gemma":[0.72416705,0.00001367095,0.27543217,0.00017284794,0.000015703437,0.00001676412,8.205675e-7,0.000008888513,0.00017210256],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827504,0.000021846141,0.0005845747,0.0004703485,0.0004067338,0.00024142746],"domain_scores_gemma":[0.997552,0.000113461545,0.00051449495,0.00035272728,0.001417322,0.00005004456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025147892,0.0002167127,0.00028454282,0.00028898643,0.00016298704,0.00012074646,0.0017172295,0.000098975506,0.00004544278],"category_scores_gemma":[0.000652007,0.00017191876,0.000110059664,0.0014029868,0.00032399158,0.0003944061,0.00027380185,0.00021187092,0.000004302014],"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":[0.00004307538,0.000099657984,0.00009799941,0.000036346883,0.000012007014,1.14919345e-7,0.00020169832,0.45590496,0.004519061,0.50846916,0.000031131884,0.0305848],"study_design_scores_gemma":[0.0000428699,0.000058564565,0.00011074437,0.0001251571,0.000008202372,4.0504793e-7,0.000109780274,0.6306039,0.33297244,0.035855882,0.000009023931,0.00010304938],"about_ca_topic_score_codex":0.000021958876,"about_ca_topic_score_gemma":0.0000071049258,"teacher_disagreement_score":0.722729,"about_ca_system_score_codex":0.00006983005,"about_ca_system_score_gemma":0.00022114087,"threshold_uncertainty_score":0.70106435},"labels":[],"label_agreement":null},{"id":"W4402147782","doi":"","title":"A study of quadratic search step formulations for multiobjective derivative free optimization","year":2022,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Derivative (finance); Mathematical optimization; Quadratic equation; Mathematics; Quadratic model; Computer science; Multi-objective optimization; Statistics; Response surface methodology","score_opus":0.020547317540403242,"score_gpt":0.26551070040638397,"score_spread":0.24496338286598074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402147782","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":[0.009047623,0.000041852752,0.9860916,0.0009549078,0.0001033377,0.0017732236,0.0000612641,0.00017342388,0.0017527564],"genre_scores_gemma":[0.414489,0.000005409097,0.5843159,0.000026477794,0.0000042986976,0.00040822508,0.000059870537,0.000021196478,0.0006695948],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946383,0.0033614035,0.000504925,0.0006323115,0.0005751579,0.00028788525],"domain_scores_gemma":[0.9926696,0.0021015443,0.0004259982,0.0015423966,0.0031635382,0.00009693535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025930493,0.0001972036,0.00026630136,0.00033587354,0.0010263069,0.00012824628,0.001485868,0.00004453931,0.000049352802],"category_scores_gemma":[0.0016585673,0.00022713917,0.00009920805,0.0014321728,0.000095878095,0.00062058726,0.0011962905,0.00022744869,0.0000017274817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035179786,0.0031097797,0.0008804968,0.000027458866,0.0001073333,0.0000022743613,0.072380796,0.795376,0.00048100913,0.110067286,0.000099105186,0.017433258],"study_design_scores_gemma":[0.0024083385,0.000010628676,0.0012689432,0.000034977456,0.000016249482,0.0000052870346,0.0032758357,0.98638535,0.004904934,0.0013292612,0.00013274052,0.00022744169],"about_ca_topic_score_codex":0.0002932032,"about_ca_topic_score_gemma":0.0003941646,"teacher_disagreement_score":0.40544137,"about_ca_system_score_codex":0.00024318788,"about_ca_system_score_gemma":0.00022184906,"threshold_uncertainty_score":0.92624664},"labels":[],"label_agreement":null},{"id":"W4402474870","doi":"10.1109/ccece59415.2024.10667108","title":"Surrogate-Assisted Multi-Objective Design Optimization of a Lorentz Force Actuator","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"National Institute for Nanotechnology; National Research Council Canada; University of Manitoba","funders":"National Research Council","keywords":"Surrogate model; Lorentz force; Actuator; Computer science; Multi-objective optimization; Mathematical optimization; Control theory (sociology); Physics; Mathematics; Artificial intelligence; Magnetic field; Control (management)","score_opus":0.03271863339698354,"score_gpt":0.2882338698910352,"score_spread":0.25551523649405167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402474870","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":[0.000057887173,0.00015739622,0.9971398,0.00011156357,0.00052439654,0.0005626185,0.000007737155,0.00070804363,0.00073054904],"genre_scores_gemma":[0.065981045,0.00003994226,0.93240577,0.00006473213,0.000024154091,0.000055029872,0.000010030148,0.000033809367,0.0013854983],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817586,0.00014207014,0.00039255642,0.0006558038,0.00033233134,0.00030140346],"domain_scores_gemma":[0.99853414,0.00036995127,0.00009073086,0.00046195183,0.00042557745,0.00011763803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028931198,0.00024571663,0.00025950966,0.00034964926,0.00009396777,0.00016412183,0.00052557414,0.0001014362,0.0000869633],"category_scores_gemma":[0.00019940718,0.00021754419,0.00011396448,0.0014042212,0.000084886764,0.0012956541,0.00018999244,0.00014509316,0.00004455029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020079518,0.00017121558,0.000015128408,0.000037478167,0.000088049004,0.000022328553,0.0011736959,0.9645958,0.002070262,0.0072094835,0.00013127518,0.024465194],"study_design_scores_gemma":[0.0005922835,0.00010121764,0.00015240944,0.000052916366,0.000014151191,0.00001865353,0.000085201485,0.9759952,0.022441417,0.00025498556,0.000054560827,0.0002370058],"about_ca_topic_score_codex":0.00002019419,"about_ca_topic_score_gemma":0.0000055870464,"teacher_disagreement_score":0.06592316,"about_ca_system_score_codex":0.00018622624,"about_ca_system_score_gemma":0.0002145344,"threshold_uncertainty_score":0.8871194},"labels":[],"label_agreement":null},{"id":"W4402475374","doi":"10.1109/ccece59415.2024.10667327","title":"Exploring Long-term Memory in Evolutionary Multi-objective Algorithms: A Case Study with NSGA-III","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Wilfrid Laurier University; Brock University; Ontario Tech University","funders":"","keywords":"Term (time); Computer science; Algorithm; Evolutionary algorithm; Theoretical computer science; Artificial intelligence","score_opus":0.07021689969061078,"score_gpt":0.31114488042521227,"score_spread":0.24092798073460148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402475374","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":[0.11514778,0.00033866745,0.8813876,0.00007265236,0.0006075025,0.0012369845,0.000003896693,0.0008694185,0.0003355333],"genre_scores_gemma":[0.6395809,0.000028900527,0.35889575,0.00003572243,0.000106037995,0.000574021,0.000002912412,0.000046379733,0.0007293512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99701756,0.0001975901,0.00044280686,0.0013159156,0.0004884929,0.0005376295],"domain_scores_gemma":[0.99851775,0.0002719211,0.00007269328,0.00070866273,0.00025045592,0.00017851552],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036773825,0.00040417234,0.00034215592,0.0007667655,0.00021906286,0.00025154883,0.0005257422,0.000059927996,0.000031909905],"category_scores_gemma":[0.00004461833,0.0003430266,0.00007260975,0.0021550446,0.00010060897,0.0037163035,0.0004555429,0.00044594426,0.00006191553],"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":[0.00017289107,0.004413958,0.01840302,0.00017023442,0.00067032245,0.23940693,0.09404335,0.17924772,0.0001732792,0.0016521137,0.000048627528,0.46159756],"study_design_scores_gemma":[0.003088629,0.00060602906,0.014957426,0.00015948094,0.000026017124,0.0059475973,0.01253206,0.9613918,0.000495937,0.00004216161,0.000009831559,0.0007430468],"about_ca_topic_score_codex":0.00068716024,"about_ca_topic_score_gemma":0.001097122,"teacher_disagreement_score":0.78214407,"about_ca_system_score_codex":0.00058542716,"about_ca_system_score_gemma":0.00024958077,"threshold_uncertainty_score":0.9999022},"labels":[],"label_agreement":null},{"id":"W4402687508","doi":"10.2514/6.2024-4405","title":"Gradient-Enhanced Bayesian Optimization With Application to Aerodynamic Shape Optimization","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Institute for Christian Studies; University of Toronto","funders":"","keywords":"Bayesian optimization; Aerodynamics; Computer science; Bayesian probability; Shape optimization; Mathematical optimization; Artificial intelligence; Mathematics; Aerospace engineering; Engineering; Finite element method; Structural engineering","score_opus":0.004667329597921192,"score_gpt":0.2387497747661841,"score_spread":0.2340824451682629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402687508","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":[0.00006985809,0.00003964613,0.9933602,0.000991541,0.00029498464,0.0009788774,0.000004126115,0.0014951184,0.0027656076],"genre_scores_gemma":[0.14117901,0.00002481857,0.8574636,0.000346364,0.000056128047,0.00024998796,0.0000598687,0.000048572714,0.00057165796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978686,0.00005069319,0.00032020622,0.0010167388,0.00038318022,0.00036055644],"domain_scores_gemma":[0.9987001,0.000066691646,0.0000806114,0.00062777166,0.00032359603,0.0002012294],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001524302,0.0002816115,0.00018961987,0.00042617074,0.00018021351,0.0004295054,0.0005553693,0.000082734274,0.00013593255],"category_scores_gemma":[0.0000365438,0.00024742252,0.000047253405,0.0024404107,0.000038781618,0.001573233,0.00013952726,0.00013538366,0.00010584291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000093255585,0.000038798218,0.0000049095065,0.000013505534,0.000016350108,0.0000038213034,0.00029315724,0.94495374,0.0002740883,0.010061389,0.000040842104,0.044290073],"study_design_scores_gemma":[0.0002732026,0.00012450737,0.000036939822,0.00004339996,0.000010533951,0.000017244496,0.000035688357,0.9980284,0.00073988776,0.00015328306,0.00018393999,0.0003529517],"about_ca_topic_score_codex":0.000011905866,"about_ca_topic_score_gemma":0.000022801461,"teacher_disagreement_score":0.14110915,"about_ca_system_score_codex":0.00027045124,"about_ca_system_score_gemma":0.000099309946,"threshold_uncertainty_score":0.9999978},"labels":[],"label_agreement":null},{"id":"W4402835221","doi":"10.1109/icmmt61774.2024.10672001","title":"Recent Advances in Surrogate-Assisted Optimization Techniques Using Feature and Feature Sensitivity for Microwave Filter Design","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Sensitivity (control systems); Feature (linguistics); Computer science; Filter (signal processing); Pattern recognition (psychology); Microwave; Artificial intelligence; Surrogate model; Electronic engineering; Machine learning; Engineering; Computer vision; Telecommunications","score_opus":0.027975088047102373,"score_gpt":0.29946892601127467,"score_spread":0.2714938379641723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402835221","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":[0.000028086228,0.0031446554,0.99389374,0.0013238679,0.00023696966,0.00082924217,0.000010782786,0.00041399925,0.0001186782],"genre_scores_gemma":[0.0019853225,0.0017726063,0.99562263,0.00020105064,0.000039030856,0.000042727283,0.0000169327,0.000025668749,0.0002940289],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986731,0.0001459018,0.00015750033,0.00062774186,0.00013360548,0.00026215962],"domain_scores_gemma":[0.99911344,0.00031221093,0.00006199548,0.00021538236,0.00023653371,0.00006043151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038635446,0.00021643697,0.00020433318,0.00024703005,0.00010511279,0.00026475402,0.000120115765,0.00015196044,0.0000035065038],"category_scores_gemma":[0.00010719731,0.0001917379,0.000039787017,0.0007609023,0.000047542726,0.0015038629,0.00010806853,0.00018618375,6.013681e-7],"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":[0.00005938614,0.000060120845,0.000025929534,0.000081131846,0.000018375731,0.000061482024,0.00037066275,0.37380338,0.011041699,0.0011201209,0.0004404581,0.61291724],"study_design_scores_gemma":[0.00024243361,0.000044441178,0.000045700734,0.00011570922,0.000007112737,0.00009092904,0.000023950843,0.9620562,0.03114982,0.00039486747,0.005588737,0.00024010356],"about_ca_topic_score_codex":0.0000023081527,"about_ca_topic_score_gemma":0.000028901375,"teacher_disagreement_score":0.61267716,"about_ca_system_score_codex":0.00018552315,"about_ca_system_score_gemma":0.00007817191,"threshold_uncertainty_score":0.78188443},"labels":[],"label_agreement":null},{"id":"W4403059377","doi":"10.1080/08982112.2024.2410012","title":"A review of leveraged sample selection in variation reduction projects","year":2024,"lang":"en","type":"review","venue":"Quality Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Selection (genetic algorithm); Sample (material); Variation (astronomy); Reduction (mathematics); Business; Engineering; Statistics; Operations management; Manufacturing engineering; Computer science; Mathematics; Artificial intelligence; Chemistry","score_opus":0.07500702337839112,"score_gpt":0.3798421957638481,"score_spread":0.304835172385457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403059377","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":[4.423587e-8,0.50880736,0.49024698,0.0000099518775,0.0002810092,0.00050150807,0.0000068355916,0.0001359568,0.000010330004],"genre_scores_gemma":[0.000001454349,0.79973894,0.1999388,0.000009494871,0.00007127705,0.00015794949,0.000030961197,0.000029250526,0.000021889591],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978079,0.00024554422,0.00096280733,0.00054092833,0.00024272295,0.00020006036],"domain_scores_gemma":[0.99895316,0.00019141671,0.00037344347,0.00031264662,0.0001313134,0.00003801929],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011118287,0.00029687295,0.0009994365,0.00051422574,0.000021659613,0.000040305356,0.00030314145,0.00015264092,0.00000468882],"category_scores_gemma":[0.0008140784,0.00028905517,0.00020681531,0.0024422451,0.000007465279,0.00041651452,0.00010313108,0.00044367873,0.000009399283],"study_design_candidate":"design_other","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":[6.702939e-7,0.000050799532,1.0761005e-7,0.3010021,0.000073803894,0.0000017498351,0.0003952316,0.010677734,0.000013005486,0.0048046787,0.000062268155,0.6829179],"study_design_scores_gemma":[0.00025403514,0.00006328932,0.000011357193,0.30893794,0.00030829842,0.000099257624,0.000008587502,0.24932809,0.000023024015,0.00024381759,0.43961304,0.001109252],"about_ca_topic_score_codex":0.000084079,"about_ca_topic_score_gemma":0.0000027247063,"teacher_disagreement_score":0.6818086,"about_ca_system_score_codex":0.000564447,"about_ca_system_score_gemma":0.00024524622,"threshold_uncertainty_score":0.99995613},"labels":[],"label_agreement":null},{"id":"W4403088095","doi":"10.1016/j.swevo.2024.101742","title":"A comparative study of evolutionary algorithms and particle swarm optimization approaches for constrained multi-objective optimization problems","year":2024,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Metaheuristic; Multi-swarm optimization; Particle swarm optimization; Mathematical optimization; Evolutionary algorithm; Imperialist competitive algorithm; Algorithm; Multi-objective optimization; Meta-optimization; Derivative-free optimization; Optimization algorithm; Parallel metaheuristic; Artificial intelligence; Machine learning; Mathematics","score_opus":0.05117202170374641,"score_gpt":0.2966133150463342,"score_spread":0.2454412933425878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403088095","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":[0.0083803665,0.0015575844,0.98679364,0.00018921406,0.00034608963,0.0023111042,0.00005255334,0.0003199108,0.000049529954],"genre_scores_gemma":[0.5007802,0.000047191057,0.49876785,0.000010902036,0.000040351046,0.00020933419,0.000096456184,0.000016860551,0.000030799165],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977314,0.00020447918,0.00057163514,0.00087672874,0.00032297382,0.0002927842],"domain_scores_gemma":[0.99851066,0.0004097029,0.00022399398,0.0001828435,0.0005489967,0.000123825],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002815307,0.00030920183,0.00037908956,0.000311507,0.00040774874,0.00014371994,0.00016184809,0.00010566956,0.0000028625805],"category_scores_gemma":[0.000063967236,0.00031484553,0.00006200735,0.0008848754,0.00021421553,0.0014825601,0.00015667404,0.00014361936,0.0000014732553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044237073,0.00061592687,0.00016854581,0.000100923695,0.00011892877,0.0000024906897,0.0075258245,0.98399687,0.000021731248,0.0023561148,0.00003835105,0.0050100633],"study_design_scores_gemma":[0.0022514504,0.00073375046,0.001837784,0.000069708425,0.000059013866,0.00003902049,0.0028268842,0.98998135,0.00006828863,0.0017919265,0.000014138008,0.0003266867],"about_ca_topic_score_codex":0.00002717781,"about_ca_topic_score_gemma":0.000005630241,"teacher_disagreement_score":0.49239987,"about_ca_system_score_codex":0.00016100035,"about_ca_system_score_gemma":0.0001652144,"threshold_uncertainty_score":0.9999304},"labels":[],"label_agreement":null},{"id":"W4403430192","doi":"10.1145/3638530.3664091","title":"Parallel Co-Evolutionary Algorithm and Implementation on CPU-GPU Multicore","year":2024,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Parallel computing; Multi-core processor; CUDA; General-purpose computing on graphics processing units; Operating system; Graphics","score_opus":0.019496839192251866,"score_gpt":0.2871141515943981,"score_spread":0.26761731240214626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403430192","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":[0.033617206,0.0010048978,0.96271807,0.0011309756,0.0004002596,0.0006030007,0.000028921759,0.00018632058,0.0003103511],"genre_scores_gemma":[0.64988077,0.00018986514,0.34967384,0.00007333412,0.00005701011,0.000032711938,0.000016432628,0.000012318968,0.000063717605],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837923,0.000032643784,0.0003800675,0.00057216384,0.0004070627,0.00022886088],"domain_scores_gemma":[0.9990475,0.00012031329,0.00018417135,0.00010787585,0.00044339214,0.000096708034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013892174,0.00022991646,0.00019654368,0.00020889878,0.00033443136,0.00017588702,0.000312979,0.00007093279,0.000012033211],"category_scores_gemma":[0.0000202112,0.00019559039,0.00005449507,0.00041147432,0.00021569448,0.00060981937,0.00024771082,0.0001734667,0.000008670132],"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":[0.00006168203,0.00030567645,0.01119806,0.00044579344,0.00019568246,0.0000050235553,0.004718744,0.06159612,0.002494638,0.16765796,0.0056340946,0.74568653],"study_design_scores_gemma":[0.00044654118,0.00012434731,0.13507225,0.0001087742,0.000017733846,0.00007607687,0.000289081,0.84212977,0.00015566082,0.021034997,0.00035178007,0.00019301323],"about_ca_topic_score_codex":0.000017063208,"about_ca_topic_score_gemma":6.021188e-7,"teacher_disagreement_score":0.7805336,"about_ca_system_score_codex":0.00010692376,"about_ca_system_score_gemma":0.00011202729,"threshold_uncertainty_score":0.7975944},"labels":[],"label_agreement":null},{"id":"W4403527956","doi":"10.1088/1402-4896/ad88b1","title":"Deep optimal experimental design for parameter estimation problems","year":2024,"lang":"en","type":"article","venue":"Physica Scripta","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Estimation; Optimal design; Mathematical optimization; Estimation theory; Applied mathematics; Statistical physics; Algorithm; Mathematics; Physics; Machine learning; Systems engineering; Engineering","score_opus":0.03744974030778888,"score_gpt":0.3021189406322928,"score_spread":0.2646692003245039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403527956","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":[0.00006662261,0.00037451382,0.99705803,0.00027062744,0.0009129827,0.00073906407,0.0000031942811,0.00046003293,0.00011492928],"genre_scores_gemma":[0.1316469,0.0000026629887,0.8673007,0.000105294705,0.00008997164,0.00044609216,0.0000099889285,0.00002685878,0.00037155685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988017,0.00003969601,0.00017211685,0.0005274146,0.00019175484,0.0002672913],"domain_scores_gemma":[0.99937713,0.00010539337,0.000046225563,0.0003215078,0.00007986706,0.00006985875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010424998,0.00017397769,0.00014161343,0.000085706604,0.00012025329,0.00034972336,0.0003836041,0.00003400691,0.000008570206],"category_scores_gemma":[0.00004470255,0.00016453765,0.0000885385,0.00033031785,0.0000454318,0.0013835874,0.000105182786,0.00008415495,0.00007932707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015804146,0.00026266128,5.5245977e-7,0.000047408535,0.000059061134,0.000008460528,0.0030690327,0.8690066,0.013423862,0.038808964,0.0039217193,0.07137589],"study_design_scores_gemma":[0.0002476768,0.00015398485,0.0000042294823,0.000025145382,0.0000075277153,0.0000075890725,0.000026688831,0.96250266,0.029385403,0.005991398,0.001449783,0.00019792854],"about_ca_topic_score_codex":0.0000024691672,"about_ca_topic_score_gemma":1.1200329e-7,"teacher_disagreement_score":0.13158028,"about_ca_system_score_codex":0.00010451835,"about_ca_system_score_gemma":0.000044539447,"threshold_uncertainty_score":0.670965},"labels":[],"label_agreement":null},{"id":"W4403636947","doi":"10.1007/s11227-024-06525-8","title":"Cooperative, collaborative, coevolutionary multi-objective optimization on CPU-GPU multi-core","year":2024,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"","keywords":"Computer science; Multi-core processor; Parallel computing; Core (optical fiber); Many core; CUDA; Computational science","score_opus":0.028781018083377546,"score_gpt":0.2976588001028732,"score_spread":0.26887778201949564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403636947","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":[0.003299866,0.0016534153,0.9921879,0.0006064171,0.001434622,0.00040414432,0.000014477098,0.00018717708,0.00021196171],"genre_scores_gemma":[0.2973056,0.00023121531,0.70158726,0.00031426386,0.00029764933,0.00000494453,0.000005491498,0.000044499902,0.00020903072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997411,0.00048041533,0.00074219366,0.00040244416,0.0005848362,0.00037909485],"domain_scores_gemma":[0.99670714,0.0009837669,0.0002950639,0.0003651727,0.0015056517,0.00014317826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011742092,0.00034288736,0.00037786682,0.0004342617,0.0006103757,0.00026645427,0.00088001083,0.00010490914,0.000023965531],"category_scores_gemma":[0.00036388755,0.00023957678,0.00013800907,0.0018688632,0.00018871689,0.0016192655,0.00026895574,0.0007772664,0.000038545997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003698656,0.00018224693,0.000067597524,0.000013188192,0.00010441851,0.000068926034,0.005552691,0.9860325,0.0007846233,0.0016338325,0.00030792493,0.0052150744],"study_design_scores_gemma":[0.001075771,0.00031957807,0.0005781512,0.00028811756,0.000028972854,0.00034983983,0.0012485221,0.9946756,0.0007714749,0.0000835484,0.00030795456,0.00027247905],"about_ca_topic_score_codex":0.000009951956,"about_ca_topic_score_gemma":0.000004936693,"teacher_disagreement_score":0.29400575,"about_ca_system_score_codex":0.00042424732,"about_ca_system_score_gemma":0.00045159532,"threshold_uncertainty_score":0.9769658},"labels":[],"label_agreement":null},{"id":"W4403855116","doi":"10.1016/j.rser.2024.115058","title":"Systematic review of the life cycle optimization literature, and recommendations for performance of life cycle optimization studies","year":2024,"lang":"en","type":"article","venue":"Renewable and Sustainable Energy Reviews","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Management science; Engineering","score_opus":0.014188714476609974,"score_gpt":0.2780181345139497,"score_spread":0.2638294200373397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403855116","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000058593478,0.43731117,0.5610568,0.0005713203,0.00009604688,0.0008325409,0.0000044936746,0.000027811884,0.00009395134],"genre_scores_gemma":[0.0010966465,0.8206194,0.17499246,0.0006306494,0.000033804703,0.0005116048,0.000024817296,0.00002266256,0.0020679499],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802226,0.00026483036,0.0009270803,0.0003805549,0.00016241256,0.00024289188],"domain_scores_gemma":[0.997906,0.00027159986,0.0004965173,0.00048417822,0.0007592368,0.000082441424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001112007,0.00021519932,0.00079542055,0.00015671157,0.00026406473,0.00011020284,0.00028675084,0.000060754486,0.0000048888005],"category_scores_gemma":[0.0016927164,0.00014446802,0.000118032345,0.0013151838,0.0000720977,0.0010058682,0.00024843757,0.00006416029,1.5217789e-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":[0.000003387367,0.000023355085,0.0000018036869,0.25973865,0.00007705582,4.0502388e-7,0.00031056322,0.7301582,0.0000017324265,0.007658428,0.0005416969,0.0014846653],"study_design_scores_gemma":[0.00018655205,0.00006406443,5.357102e-7,0.063567154,0.00014406876,0.000007122901,0.0001882245,0.93184924,0.00005838635,0.00049645436,0.003281534,0.0001566317],"about_ca_topic_score_codex":0.000019623474,"about_ca_topic_score_gemma":0.0000019863676,"teacher_disagreement_score":0.38606435,"about_ca_system_score_codex":0.00006152059,"about_ca_system_score_gemma":0.00016911759,"threshold_uncertainty_score":0.58912355},"labels":[],"label_agreement":null},{"id":"W4403897142","doi":"10.1016/j.eswa.2024.125613","title":"Artificial bee colony algorithm based on multiple indicators for many-objective optimization with irregular Pareto fronts","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Multi-Objective Optimization Algorithms","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":"Brock University","funders":"Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Computer science; Pareto principle; Artificial bee colony algorithm; Multi-objective optimization; Mathematical optimization; Optimization algorithm; Pareto optimal; Algorithm; Artificial intelligence; Machine learning; Mathematics","score_opus":0.010151162525947797,"score_gpt":0.25836093192546555,"score_spread":0.24820976939951775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403897142","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":[0.000017494098,0.0003041078,0.9930608,0.00043686864,0.0002797495,0.004656435,0.00013974018,0.0008194884,0.00028529108],"genre_scores_gemma":[0.090605795,0.000009485729,0.8902092,0.00022125452,0.00035232503,0.018017204,0.00019536499,0.000110361645,0.0002789873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973212,0.00009136951,0.00042846144,0.0011798708,0.00054509944,0.00043403293],"domain_scores_gemma":[0.9979256,0.0005032366,0.0002246568,0.000764032,0.00037476848,0.000207715],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021056192,0.0003892717,0.00034709775,0.00051695656,0.0004736691,0.00036463232,0.0005675896,0.00014679406,0.000007119508],"category_scores_gemma":[0.000042515705,0.00031621804,0.00007555628,0.0015757434,0.00013051974,0.0005568691,0.000051096944,0.00018164437,0.00003289275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007433246,0.0003208696,0.00006132741,0.000050382143,0.00010337092,0.000010659944,0.00087543525,0.9659245,0.00009767087,0.0095921345,0.0007316992,0.022157595],"study_design_scores_gemma":[0.000657463,0.00033094725,0.00005575498,0.00014794315,0.000020352267,0.000016366972,0.00025880264,0.98302406,0.0011672324,0.000069534166,0.013811148,0.0004403861],"about_ca_topic_score_codex":0.000052789146,"about_ca_topic_score_gemma":0.000015436875,"teacher_disagreement_score":0.10285161,"about_ca_system_score_codex":0.00042930094,"about_ca_system_score_gemma":0.00035109068,"threshold_uncertainty_score":0.999929},"labels":[],"label_agreement":null},{"id":"W4404141415","doi":"10.3390/jrfm17110498","title":"A Double Optimum New Solution Method Based on EVA and Knapsack","year":2024,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Knapsack problem; Mathematics; Mathematical optimization; Computer science","score_opus":0.011285561035038244,"score_gpt":0.27533079971067037,"score_spread":0.2640452386756321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404141415","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":[0.00018546195,0.00082884467,0.9972479,0.00049217604,0.0005750096,0.0001302937,0.0000013794942,0.000027470764,0.0005114738],"genre_scores_gemma":[0.020935686,0.0011647949,0.9771642,0.0001852792,0.00018154221,0.000002723614,3.704457e-7,0.00000862404,0.00035676194],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915916,0.00004294858,0.00022300662,0.00021675766,0.00022224868,0.00013585294],"domain_scores_gemma":[0.99952763,0.00007192247,0.00012230493,0.00012277339,0.000060740425,0.000094649644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047839378,0.00010848507,0.0001488985,0.00028574062,0.000099177065,0.00015739776,0.00014915809,0.000035623925,0.0000047028084],"category_scores_gemma":[0.000030518255,0.00009023343,0.0000531401,0.00032624358,0.000018574814,0.00040455748,0.00009746469,0.0001758606,0.0000039754455],"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":[0.000082212064,0.000042257372,0.000031473086,0.000029333574,0.000013743943,0.000097129945,0.00033462627,0.06116785,0.000007723527,0.018973924,0.00087834493,0.9183414],"study_design_scores_gemma":[0.0017848944,0.00032844275,0.0022905285,0.00014851644,0.000050855124,0.00003116948,0.000022579961,0.9305087,0.00008107057,0.008081834,0.05651684,0.00015459581],"about_ca_topic_score_codex":0.000011245249,"about_ca_topic_score_gemma":0.0000024007472,"teacher_disagreement_score":0.9181868,"about_ca_system_score_codex":0.00005852489,"about_ca_system_score_gemma":0.000054160853,"threshold_uncertainty_score":0.36796123},"labels":[],"label_agreement":null},{"id":"W4404469638","doi":"10.1109/tcyb.2024.3489885","title":"A Hierarchical Surrogate-Assisted Differential Evolution With Core Space Localization","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Nipissing University","funders":"Fujian University of Technology","keywords":"Core (optical fiber); Space (punctuation); Differential (mechanical device); Differential evolution; Computer science; Artificial intelligence; Engineering; Telecommunications; Aerospace engineering","score_opus":0.01622192325667977,"score_gpt":0.25467102370266737,"score_spread":0.2384491004459876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404469638","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":[0.0016291635,0.000046648016,0.9956183,0.0003078245,0.0010766254,0.00030969622,0.000022570788,0.00072624936,0.0002629593],"genre_scores_gemma":[0.8922718,0.000037602014,0.10641818,0.000049430862,0.000047940604,0.000044164077,0.000008782264,0.000040892715,0.001081187],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983155,0.00007571515,0.00024043328,0.0005992592,0.0004653835,0.00030369524],"domain_scores_gemma":[0.99904776,0.00015101906,0.000051683488,0.00042272164,0.0001778835,0.00014893762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006562669,0.00025149743,0.00017590242,0.00029680636,0.00022296401,0.00022162791,0.0002925289,0.00012711842,0.000051249197],"category_scores_gemma":[0.0000064721526,0.0002225377,0.00008513113,0.001132569,0.0001421722,0.00038885028,0.0000045340407,0.0003918003,0.000082913975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008609101,0.00047951823,0.00001943397,0.00005050378,0.00011641818,0.00007705832,0.00094250025,0.90560067,0.0012820561,0.026218418,0.00011674028,0.06501059],"study_design_scores_gemma":[0.00058246864,0.0002657369,0.00035426414,0.00010303468,0.000042065716,0.00007532277,0.00002320571,0.9927836,0.0041602035,0.0008349877,0.00049033033,0.00028480196],"about_ca_topic_score_codex":0.000029483981,"about_ca_topic_score_gemma":0.00009217435,"teacher_disagreement_score":0.89064264,"about_ca_system_score_codex":0.0002929992,"about_ca_system_score_gemma":0.00014685867,"threshold_uncertainty_score":0.9074824},"labels":[],"label_agreement":null},{"id":"W4404629207","doi":"10.2514/1.j063488","title":"High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings","year":2024,"lang":"en","type":"article","venue":"AIAA Journal","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Institut Supérieur de l'Aéronautique et de l'Espace; Office National d'études et de Recherches Aérospatiales; Polytechnique Montréal","keywords":"Bayesian optimization; Curse of dimensionality; Embedding; Computer science; Optimization problem; Mathematical optimization; Random optimization; Continuous optimization; Dimension (graph theory); Global optimization; Linear subspace; Dimensionality reduction; Algorithm; Artificial intelligence; Multi-swarm optimization; Mathematics","score_opus":0.010725472379701602,"score_gpt":0.25926868146017773,"score_spread":0.24854320908047614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404629207","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":[0.0024107879,0.0006471929,0.9949007,0.000715253,0.0010166371,0.000111495574,0.000002288631,0.00013099804,0.00006466097],"genre_scores_gemma":[0.09366605,0.000102805825,0.9055959,0.00025813963,0.00023579638,0.0000021864712,0.000002746833,0.00002448407,0.00011189705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986953,0.00009122287,0.0002936535,0.00034108068,0.0003241417,0.00025456827],"domain_scores_gemma":[0.9993099,0.00011902593,0.00008530004,0.00015218394,0.00015490902,0.00017866062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003585364,0.00016660138,0.00018259005,0.0002628321,0.00034153575,0.0006791381,0.00023124063,0.00006152938,0.00014186882],"category_scores_gemma":[0.00006329623,0.00014641335,0.00006319797,0.00043658356,0.00005198724,0.0016502183,0.00012581774,0.0002824432,0.0000064083256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001680359,0.000019642051,0.0000325301,0.000007806003,0.0000361337,0.00011468662,0.00037842922,0.9792797,0.0005675557,0.0021885482,0.00012823925,0.017229952],"study_design_scores_gemma":[0.0012527413,0.000038231963,0.00004851022,0.00008517579,0.000015695108,0.0011857715,0.00001863403,0.994917,0.0002296027,0.0019085924,0.00012386723,0.0001762066],"about_ca_topic_score_codex":0.0000069761445,"about_ca_topic_score_gemma":4.715937e-7,"teacher_disagreement_score":0.09125526,"about_ca_system_score_codex":0.000114232,"about_ca_system_score_gemma":0.00014405952,"threshold_uncertainty_score":0.6548944},"labels":[],"label_agreement":null},{"id":"W4404639573","doi":"10.1038/s41598-024-76877-x","title":"External archive guided radial-grid multi objective differential evolution","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Laurentian University","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation of Korea","keywords":"Computer science; Multi-objective optimization; Benchmark (surveying); Cluster analysis; Differential evolution; Mathematical optimization; Grid; Population; Pareto principle; Evolutionary algorithm; Metric (unit); Data mining; Mutation; Machine learning; Artificial intelligence; Mathematics","score_opus":0.01596524355204873,"score_gpt":0.276251492298607,"score_spread":0.26028624874655826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404639573","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":[0.0032035117,0.0003627821,0.95602715,0.00008990283,0.038351364,0.00048452185,0.000010300201,0.0006624076,0.0008080595],"genre_scores_gemma":[0.4895928,0.000009211252,0.5027331,0.000031487736,0.000633375,0.000098030054,0.000059397255,0.00004588802,0.0067967083],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961617,0.00013390133,0.00061886274,0.0017568062,0.000815731,0.0005130157],"domain_scores_gemma":[0.997964,0.00009404934,0.00023623877,0.0011554463,0.000326239,0.00022400834],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007201956,0.00027414932,0.00023500517,0.0005761062,0.00049824687,0.0013749691,0.0005243449,0.000075832126,0.00008038952],"category_scores_gemma":[0.0002863546,0.00025080226,0.0001956722,0.0012343698,0.00032134852,0.0013581626,0.00040034956,0.00026783507,0.00014112344],"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":[0.00006288338,0.0016867227,0.0032240842,0.00029327118,0.000524549,0.015898498,0.021101996,0.14012781,0.46711284,0.07583578,0.08153201,0.19259958],"study_design_scores_gemma":[0.00032338488,0.000040211442,0.0036006148,0.00013154364,0.000020976375,0.0012233703,0.000052095897,0.93075067,0.014708607,0.041627284,0.0070316247,0.00048961566],"about_ca_topic_score_codex":0.00004903695,"about_ca_topic_score_gemma":0.00001789961,"teacher_disagreement_score":0.7906229,"about_ca_system_score_codex":0.0003888646,"about_ca_system_score_gemma":0.00043173286,"threshold_uncertainty_score":0.9999944},"labels":[],"label_agreement":null},{"id":"W4404915738","doi":"10.1109/acsos-c63493.2024.00033","title":"Online Learning of Temporal Dependencies for the Sustainable Foraging Problem","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Foraging; Computer science; Artificial intelligence; Online learning; Machine learning; World Wide Web; Ecology; Biology","score_opus":0.014923948136683338,"score_gpt":0.28416273074462994,"score_spread":0.2692387826079466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404915738","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":[0.0001173625,0.00046240358,0.9968158,0.00092857605,0.000103817394,0.00041940407,0.0000015587417,0.00027172756,0.00087936694],"genre_scores_gemma":[0.20237274,0.000018033643,0.77984446,0.000049374317,0.000037520975,0.000047797523,0.0000039593256,0.000013551426,0.017612582],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991746,0.000021444963,0.00017429028,0.00024343732,0.00015411321,0.00023214532],"domain_scores_gemma":[0.99909925,0.0003684372,0.000051805415,0.0001850593,0.00026964108,0.00002581062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032891022,0.00008929791,0.00009667496,0.000095988704,0.00017808517,0.0001474776,0.00039111444,0.000024077865,0.000010228033],"category_scores_gemma":[0.00011724158,0.00005845338,0.000058264308,0.0004784489,0.000038724902,0.0006769498,0.00021183754,0.00011868124,0.0000021475544],"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":[0.000006096897,0.00006927989,0.0002874902,0.00024299954,0.00005503423,0.000015091209,0.0025595517,0.42082885,0.00018512587,0.4876986,0.00037764572,0.08767421],"study_design_scores_gemma":[0.00013211972,0.000054364973,0.000042975887,0.000019608016,0.000005261242,0.0000073883407,0.0020283079,0.9783842,0.0009888582,0.0061861016,0.012068743,0.00008206249],"about_ca_topic_score_codex":0.000053486445,"about_ca_topic_score_gemma":0.000009981539,"teacher_disagreement_score":0.5575554,"about_ca_system_score_codex":0.000051951716,"about_ca_system_score_gemma":0.00010707848,"threshold_uncertainty_score":0.23836598},"labels":[],"label_agreement":null},{"id":"W4405429622","doi":"10.1109/jiot.2024.3518581","title":"MOSSA: An Efficient Swarm Intelligent Algorithm to Solve Global Optimization and Carbon Fiber Drawing Process Problems","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Process (computing); Swarm behaviour; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics","score_opus":0.012839217186999853,"score_gpt":0.288572968888972,"score_spread":0.2757337517019721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405429622","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":[0.014113105,0.0003628198,0.9829964,0.00023592767,0.0015226112,0.00029694106,0.0000031379834,0.0001596486,0.0003094242],"genre_scores_gemma":[0.15836474,0.000034870303,0.8410483,0.00015932554,0.00017881008,0.000014294862,0.0000017585796,0.000031474432,0.00016644587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979199,0.00005078957,0.00056974625,0.00055417255,0.0005574558,0.00034790984],"domain_scores_gemma":[0.99870515,0.00004635801,0.00022824274,0.00022010352,0.0005100066,0.0002901244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005485374,0.00025589287,0.0002604854,0.00026173366,0.00009033798,0.0007187673,0.000690015,0.00008958321,0.000017660124],"category_scores_gemma":[0.00006556454,0.00022937772,0.00007811376,0.0006064306,0.000067748966,0.0011091322,0.00019471886,0.0003698771,0.000006548089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008445246,0.00008288686,0.000012387373,0.000041490424,0.000047971593,0.000040004463,0.008324774,0.9252384,0.00009340089,0.00017940873,0.000022466973,0.06590837],"study_design_scores_gemma":[0.00021432462,0.00027376186,0.0000084564235,0.00047098595,0.000017593165,0.0005878506,0.00013792499,0.9889312,0.0082991645,0.00071523787,0.00009947505,0.00024402526],"about_ca_topic_score_codex":0.000050072063,"about_ca_topic_score_gemma":0.0000016382318,"teacher_disagreement_score":0.14425164,"about_ca_system_score_codex":0.00032942364,"about_ca_system_score_gemma":0.00013247601,"threshold_uncertainty_score":0.9353752},"labels":[],"label_agreement":null},{"id":"W4405730979","doi":"","title":"Processus gaussien pour l'optimisation bayésienne avec variables mixtes hiérarchiques: Application à l'éco-conception avion","year":2024,"lang":"fr","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Bayesian optimization; Gaussian process; Computer science; Process (computing); Bayesian probability; Mathematical optimization; Gaussian; Artificial intelligence; Mathematics","score_opus":0.012496815171707927,"score_gpt":0.24570580978321102,"score_spread":0.2332089946115031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405730979","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":[0.0021420834,0.0068732686,0.9402062,0.032180294,0.00096882175,0.0011369846,0.00007263952,0.0010806009,0.015339054],"genre_scores_gemma":[0.21734793,0.002597737,0.7387992,0.00025011355,0.0002684752,0.00037195472,0.00049383705,0.00013739713,0.03973335],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99055463,0.004907535,0.0010131026,0.0018784846,0.00079363614,0.0008526323],"domain_scores_gemma":[0.9907299,0.0024930285,0.00060576916,0.0019250803,0.003841617,0.0004046023],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005516782,0.00064461166,0.00047375396,0.0005263523,0.00073495234,0.0016449029,0.0015647686,0.00043133844,0.0003384578],"category_scores_gemma":[0.001978098,0.0007342404,0.00024752363,0.0023643097,0.00057706464,0.0028013103,0.0006649211,0.00075338903,0.00049859576],"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":[0.000015451888,0.0007302186,0.00014640728,0.0003872708,0.00008161976,0.000017943343,0.023075784,0.005142665,0.007676698,0.40283194,0.0009447658,0.55894923],"study_design_scores_gemma":[0.0006956551,0.0000031842692,0.0008996016,0.0022038922,0.000082968545,0.00009486854,0.0002403102,0.90176773,0.028956326,0.025458591,0.038857307,0.0007395577],"about_ca_topic_score_codex":0.0006748049,"about_ca_topic_score_gemma":0.00045024362,"teacher_disagreement_score":0.89662504,"about_ca_system_score_codex":0.0007068801,"about_ca_system_score_gemma":0.00094989483,"threshold_uncertainty_score":0.9995109},"labels":[],"label_agreement":null},{"id":"W4405929204","doi":"10.1016/j.swevo.2024.101827","title":"Population-level center-based sampling for meta-heuristic algorithms","year":2024,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; Brock University; Ontario Tech University","funders":"Brock University","keywords":"Computer science; Center (category theory); Heuristic; Sampling (signal processing); Algorithm; Population; Meta heuristic; Artificial intelligence; Telecommunications","score_opus":0.0861505226043258,"score_gpt":0.33446159651186075,"score_spread":0.24831107390753493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405929204","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":[0.00028011843,0.0012323051,0.99589187,0.0006803392,0.0008942464,0.00041604188,0.00011875735,0.00043056073,0.000055758792],"genre_scores_gemma":[0.24713579,0.000013737458,0.7520525,0.00012232576,0.00011761005,0.000129999,0.00032072322,0.000019022764,0.0000883122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986136,0.000055018078,0.0003005901,0.0005639064,0.00023519454,0.0002316698],"domain_scores_gemma":[0.9990202,0.0004447884,0.00007590875,0.00014745447,0.00022462237,0.000086989494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019518638,0.00019412543,0.00021221842,0.00025981423,0.0003099906,0.00018336464,0.00015338702,0.000060178416,0.0000069675016],"category_scores_gemma":[0.000068145804,0.00018674081,0.00012485943,0.00043142078,0.00003923038,0.00070997095,0.00006211391,0.00009276388,0.000011773303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012949144,0.000092855276,0.00013479649,0.00011579518,0.00017430726,0.0000054200514,0.00015997866,0.91387993,0.000019012445,0.022427471,0.00055611535,0.062421337],"study_design_scores_gemma":[0.00043010735,0.00006280949,0.004152568,0.00002668885,0.000074469164,0.00002334738,0.000009663942,0.9563545,0.000018061815,0.037410773,0.0012323499,0.00020467788],"about_ca_topic_score_codex":0.000020616057,"about_ca_topic_score_gemma":0.0000018442913,"teacher_disagreement_score":0.24685568,"about_ca_system_score_codex":0.00013405755,"about_ca_system_score_gemma":0.00008563327,"threshold_uncertainty_score":0.7615069},"labels":[],"label_agreement":null},{"id":"W4406214684","doi":"10.1007/s10589-024-00645-w","title":"Inter-DS: a cost saving algorithm for expensive constrained multi-fidelity blackbox optimization","year":2025,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"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; Hydro-Québec; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec","keywords":"Solver; Computer science; Fidelity; Mathematical optimization; Bridging (networking); Point (geometry); Algorithm; Mathematics","score_opus":0.019205981593508156,"score_gpt":0.3145895859890645,"score_spread":0.29538360439555633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406214684","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":[0.0000020372868,0.000105926265,0.9953042,0.00096806063,0.00016957916,0.0025578977,0.0001048833,0.00037017115,0.00041721447],"genre_scores_gemma":[0.00414364,0.00006583999,0.99189115,0.0013259931,0.000058911755,0.0015402336,0.00062691496,0.000025701107,0.0003216192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979564,0.000082263155,0.00063596235,0.0008307491,0.00020270473,0.00029193514],"domain_scores_gemma":[0.99705404,0.0005215888,0.0003148945,0.00038144167,0.0015780751,0.00014997616],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022954479,0.00029624574,0.00029934145,0.00037660758,0.00065669004,0.00031755533,0.00042295104,0.00012883825,0.000022710185],"category_scores_gemma":[0.00017554006,0.000338612,0.00009282232,0.0010397051,0.0002120291,0.00073961145,0.00021048976,0.00014259727,0.0000060580537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004381675,0.00013669439,0.000019487963,0.000015536243,0.00003495126,3.7114924e-7,0.000116978146,0.86968434,0.000006487864,0.037789926,0.00012883381,0.09206204],"study_design_scores_gemma":[0.0019281186,0.00002506028,0.000095923046,0.000037745125,0.000024921586,0.000009996732,0.00015081119,0.99263,0.000036985515,0.0031143597,0.0016363234,0.00030975937],"about_ca_topic_score_codex":0.0000043328732,"about_ca_topic_score_gemma":0.0000017599234,"teacher_disagreement_score":0.12294568,"about_ca_system_score_codex":0.00017315568,"about_ca_system_score_gemma":0.00025827234,"threshold_uncertainty_score":0.9999066},"labels":[],"label_agreement":null},{"id":"W4407140437","doi":"10.1002/est2.70136","title":"Multi‐Objective Optimization of a Spherical Thermal Storage Tank Using a Student Psychology‐Based Approach","year":2025,"lang":"en","type":"article","venue":"Energy Storage","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro One (Canada); University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Thermal; Psychology; Computer science; Mathematics education; Physics; Thermodynamics","score_opus":0.020473279761038958,"score_gpt":0.31295504334394714,"score_spread":0.29248176358290817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407140437","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":[0.0015196858,0.00019449936,0.9953558,0.00005556389,0.00049837306,0.00025415738,0.000006155236,0.000191185,0.0019246037],"genre_scores_gemma":[0.23646262,0.000005878973,0.76276726,0.00040464723,0.000031339503,0.000047216232,0.000009412314,0.000023077613,0.00024857945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977899,0.00033345542,0.00041997235,0.00076850865,0.0003492496,0.0003388877],"domain_scores_gemma":[0.9984528,0.0001011402,0.00029725826,0.0007481386,0.00031200369,0.00008867269],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023102087,0.0002895692,0.0003834681,0.00029929625,0.0001737944,0.00005694722,0.00079589954,0.00015907655,0.000022084663],"category_scores_gemma":[0.00007519271,0.00029181744,0.00012277886,0.0014306644,0.00015714913,0.00042880874,0.00027190865,0.00019820455,0.0000011025843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031888892,0.0006771811,0.00014749203,0.000008981608,0.000059912916,0.000014888508,0.00037223572,0.99200803,0.0026745223,0.0016495419,0.000017956341,0.0023373591],"study_design_scores_gemma":[0.0016357986,0.00007180872,0.0013311192,0.000023321858,0.000018363025,0.0000038855655,0.00008904358,0.9949694,0.0014109537,0.000027413187,0.00016671688,0.00025216947],"about_ca_topic_score_codex":0.00005880485,"about_ca_topic_score_gemma":0.000004287305,"teacher_disagreement_score":0.23494294,"about_ca_system_score_codex":0.00030552305,"about_ca_system_score_gemma":0.00021068838,"threshold_uncertainty_score":0.9999534},"labels":[],"label_agreement":null},{"id":"W4407558133","doi":"10.29169/1927-5129.2025.21.07","title":"Optimal Design of a Biconvex Airfoil for a Supersonic Aircraft Using the Basin-Hopping and Exhaustive Search Methods","year":2025,"lang":"en","type":"article","venue":"Journal of Basic & Applied Sciences","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Airfoil; Supersonic speed; Aerospace engineering; Computer science; Angle of attack; Environmental science; Geology; Aerodynamics; Acoustics; Aeronautics; Engineering; Physics","score_opus":0.065701559064253,"score_gpt":0.377463104524327,"score_spread":0.311761545460074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407558133","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":[0.009990412,0.0003623539,0.98813796,0.00070355745,0.00023136698,0.00040651244,0.0000011138669,0.000012531002,0.00015418309],"genre_scores_gemma":[0.1465149,0.0000331421,0.85319483,0.00017991044,0.000037875285,0.000009186893,4.0440796e-8,0.0000053207536,0.000024810153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982362,0.00019660914,0.0004764493,0.00033923073,0.00043285318,0.00031868438],"domain_scores_gemma":[0.99740887,0.0014937368,0.0003694277,0.00019407025,0.00045806568,0.00007584873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004662533,0.00015381441,0.00035322987,0.0004466885,0.0005208787,0.00017185199,0.0009617221,0.000051636507,0.0000035602545],"category_scores_gemma":[0.00023234216,0.00010426936,0.0000924328,0.0014749123,0.00073231605,0.00058734574,0.00022386233,0.00022443102,1.9114192e-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":[0.00011895137,0.000097683376,0.00016408449,0.000054043532,0.00010985165,0.000004221387,0.0045032967,0.8477066,0.05682247,0.016346028,0.000049990118,0.07402276],"study_design_scores_gemma":[0.00076651905,0.00022788187,0.00021265031,0.000085964704,0.000034520122,0.000046188343,0.0031197437,0.95146054,0.041917082,0.0019337495,0.00007090613,0.00012428052],"about_ca_topic_score_codex":0.000005865912,"about_ca_topic_score_gemma":4.0845444e-7,"teacher_disagreement_score":0.13652448,"about_ca_system_score_codex":0.000100616926,"about_ca_system_score_gemma":0.000879763,"threshold_uncertainty_score":0.42519814},"labels":[],"label_agreement":null},{"id":"W4407638347","doi":"10.1109/tetci.2025.3537916","title":"Population Stream-Driven Scalable Evolutionary Many-Objective Optimization","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Scalability; Computer science; Population; Evolutionary algorithm; Artificial intelligence; Demography","score_opus":0.017092192909028806,"score_gpt":0.3029121016931501,"score_spread":0.2858199087841213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407638347","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":[0.00029138083,0.000044876604,0.9959325,0.0009837532,0.0012572462,0.0003937052,0.000011920256,0.00024374387,0.0008408305],"genre_scores_gemma":[0.47036085,0.00004582948,0.5287394,0.00021440387,0.000027713368,0.00006504868,0.000017455615,0.000012075393,0.000517224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979277,0.00013566892,0.0005584611,0.0006760689,0.000403815,0.000298247],"domain_scores_gemma":[0.99870396,0.00034061653,0.00013745931,0.00035795715,0.00039654184,0.00006346166],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015807738,0.00024711218,0.00022665452,0.000844272,0.00036685006,0.000091871145,0.00051339634,0.00012232052,0.000056337154],"category_scores_gemma":[0.000039865678,0.00029451423,0.00009402144,0.0018419753,0.00007556707,0.0008540661,0.000014437682,0.00036609796,0.000022344975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014947187,0.00020806809,0.00018448294,0.000011878601,0.000024189068,0.000004199848,0.00019873124,0.9274494,0.0000031069371,0.032657854,0.000018083234,0.039225075],"study_design_scores_gemma":[0.00022580227,0.000043684075,0.0018166087,0.00009942065,0.000007919445,0.0000049609944,0.00007048789,0.9713166,0.0005357303,0.02559413,0.000045357585,0.00023931677],"about_ca_topic_score_codex":0.000104718994,"about_ca_topic_score_gemma":0.00003375998,"teacher_disagreement_score":0.47006947,"about_ca_system_score_codex":0.000634302,"about_ca_system_score_gemma":0.00015215689,"threshold_uncertainty_score":0.9999507},"labels":[],"label_agreement":null},{"id":"W4408185721","doi":"10.1016/j.enbuild.2025.115539","title":"Examining the generalizability of inverse surrogate models for different geometries and locations","year":2025,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Council of Professional Engineers; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalizability theory; Inverse; Surrogate model; Econometrics; Inverse method; Statistics; Applied mathematics; Computer science; Mathematics; Geometry","score_opus":0.02374986492304802,"score_gpt":0.2533005811959907,"score_spread":0.22955071627294268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408185721","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":[0.07316466,0.00030408276,0.9258969,0.00036759095,0.00007772357,0.00007420812,0.000003223878,0.000024605602,0.0000870172],"genre_scores_gemma":[0.85891086,0.00022653562,0.140155,0.0002575152,0.000010070998,0.00003855412,0.0000023293926,0.0000037336772,0.0003953778],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946326,0.000025988871,0.00013164294,0.0002224952,0.000058883004,0.000097750286],"domain_scores_gemma":[0.9993376,0.00028471928,0.000060307797,0.00018247914,0.00010921313,0.000025646006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015282964,0.00007815085,0.00011035544,0.00009547454,0.00018073093,0.000040945353,0.00015894517,0.000029910916,7.2113016e-7],"category_scores_gemma":[0.00008727554,0.0000561236,0.000017797462,0.0002860445,0.0001337632,0.00025349684,0.00017389448,0.000028068962,1.4589039e-8],"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":[0.000011436441,0.00004929099,0.0017945459,0.00003555126,0.00003887211,1.6729471e-7,0.00072840846,0.117876574,0.0010920102,0.824184,0.00012349467,0.054065634],"study_design_scores_gemma":[0.00043538449,0.000034463064,0.0014013384,0.000018027627,0.000010686069,0.0000010402745,0.00012335872,0.93381256,0.009855223,0.05287279,0.0013455638,0.00008959388],"about_ca_topic_score_codex":0.00004706036,"about_ca_topic_score_gemma":0.000018369596,"teacher_disagreement_score":0.81593597,"about_ca_system_score_codex":0.000015453448,"about_ca_system_score_gemma":0.00002028958,"threshold_uncertainty_score":0.2288654},"labels":[],"label_agreement":null},{"id":"W4408421961","doi":"10.1016/j.energy.2025.135422","title":"A multi-criteria assessment method for design and dispatch of distributed energy systems considering different energy consumption attributes","year":2025,"lang":"en","type":"article","venue":"Energy","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Natural Science Foundation of Tianjin-Science and Technology Correspondent Project; Natural Science Foundation of Tianjin City","keywords":"Computer science; Energy (signal processing); Gradient method; Mathematical optimization; Algorithm; Mathematics; Statistics","score_opus":0.037817911156329286,"score_gpt":0.33529400483538696,"score_spread":0.2974760936790577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408421961","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":[0.00008470484,0.00088037096,0.99783736,0.00013914728,0.00061867747,0.00019142422,0.00009444279,0.00014020543,0.000013682168],"genre_scores_gemma":[0.14930028,0.00026769258,0.84952056,0.000106585656,0.000027163056,0.0003921541,0.0001117062,0.000019649198,0.00025423418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786675,0.00042288343,0.00053078803,0.0006411746,0.00019343726,0.00034498234],"domain_scores_gemma":[0.9977199,0.0011116682,0.00028018604,0.00044875636,0.00032888594,0.000110622146],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027222474,0.00029830262,0.00052307366,0.00022273225,0.00017360262,0.00014055794,0.0003362418,0.000119183824,0.00000437724],"category_scores_gemma":[0.00011179079,0.000280323,0.00008223246,0.00031417242,0.000080515216,0.00027888184,0.0003084952,0.00005890116,1.067243e-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":[0.000093691895,0.00056417,0.0006808033,0.00024227404,0.0004338881,0.000013367373,0.00013827947,0.46928477,0.037972126,0.42624256,0.0006790028,0.06365508],"study_design_scores_gemma":[0.0015353895,0.00007920104,0.0006911522,0.000108983026,0.000030048297,0.000009133781,0.000026260492,0.96924394,0.024475755,0.0012178014,0.0023257844,0.0002565583],"about_ca_topic_score_codex":0.00032145862,"about_ca_topic_score_gemma":0.000038097,"teacher_disagreement_score":0.49995917,"about_ca_system_score_codex":0.00017418196,"about_ca_system_score_gemma":0.000099219746,"threshold_uncertainty_score":0.9999649},"labels":[],"label_agreement":null},{"id":"W4408864303","doi":"10.1080/0305215x.2025.2453542","title":"Conservative surrogate models for optimization with the active subspace method","year":2025,"lang":"en","type":"article","venue":"Engineering Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Institut des Sciences Mathématiques, Université du Québec à Montréal","keywords":"Surrogate model; Subspace topology; Mathematical optimization; Computer science; Mathematics; Artificial intelligence","score_opus":0.009513816565957506,"score_gpt":0.2562027188241931,"score_spread":0.2466889022582356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408864303","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":[0.000005647996,0.00005501521,0.99543583,0.002395503,0.00023273943,0.0010306326,0.000011369982,0.00041640128,0.00041683493],"genre_scores_gemma":[0.0024738824,0.000025350197,0.99614316,0.0003182884,0.000026320162,0.0003393135,0.00004122575,0.000035306213,0.0005971582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873817,0.00007662981,0.00021301096,0.00048706375,0.00019161226,0.00029350776],"domain_scores_gemma":[0.99807847,0.0005932518,0.00015418025,0.00043575134,0.0006856499,0.000052675732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002858252,0.0002566633,0.00021926347,0.00022398196,0.00026296586,0.00016094613,0.00046022408,0.00008644936,0.0000038506255],"category_scores_gemma":[0.00018816702,0.00020542063,0.00005506829,0.0013575697,0.000037317397,0.0012301069,0.000097417775,0.00015293769,6.8235204e-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":[0.00003169978,0.000022596605,0.0000014756845,0.00001520173,0.000061707906,6.414717e-7,0.0005099412,0.9719224,0.000017201895,0.026123857,0.00011103113,0.0011822626],"study_design_scores_gemma":[0.00089558086,0.000046775403,0.000013275157,0.000040487543,0.000026247326,0.0000032947723,0.00009179283,0.99682784,0.0013418504,0.00018677818,0.00029486275,0.00023120787],"about_ca_topic_score_codex":0.000007929415,"about_ca_topic_score_gemma":0.0000028194668,"teacher_disagreement_score":0.025937079,"about_ca_system_score_codex":0.00017537964,"about_ca_system_score_gemma":0.0001280673,"threshold_uncertainty_score":0.837681},"labels":[],"label_agreement":null},{"id":"W4409364742","doi":"10.1609/aaai.v39i13.33576","title":"Forward KL Regularized Preference Optimization for Aligning Diffusion Policies","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Preference; Diffusion; Computer science; Mathematical optimization; Mathematics; Statistics; Physics; Thermodynamics","score_opus":0.0602084729461822,"score_gpt":0.310791092154396,"score_spread":0.2505826192082138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409364742","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":[0.0029402322,0.00001586871,0.9873288,0.002996156,0.00049096433,0.0010660982,0.000007826864,0.00016636265,0.0049876417],"genre_scores_gemma":[0.630717,0.000062860614,0.36749926,0.00037598214,0.000044079192,0.00017128926,0.000002500058,0.000018143173,0.0011089405],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978644,0.000024190485,0.0006509557,0.00065969495,0.0004008055,0.00039991736],"domain_scores_gemma":[0.9971876,0.00020862914,0.00051276153,0.0003952374,0.0016228639,0.00007288599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000413209,0.00028747314,0.00034305896,0.0002946713,0.0004321187,0.00031051008,0.001889648,0.0001329025,0.000019846939],"category_scores_gemma":[0.0011840195,0.00023148587,0.00015178267,0.0012160391,0.0002453596,0.00067320396,0.00055574585,0.00020286467,0.0000066858984],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009722644,0.00013853349,0.00006112189,0.00006125571,0.00002258438,7.3945316e-8,0.0008738517,0.02264646,0.026204307,0.9039217,0.00009586475,0.04587701],"study_design_scores_gemma":[0.00008133779,0.00008586214,0.000043009073,0.00022337539,0.000013419092,7.8453314e-7,0.0002529833,0.6294992,0.26813352,0.1014357,0.000065870765,0.00016491495],"about_ca_topic_score_codex":0.00002088391,"about_ca_topic_score_gemma":0.0000044628773,"teacher_disagreement_score":0.802486,"about_ca_system_score_codex":0.000109440116,"about_ca_system_score_gemma":0.00016106079,"threshold_uncertainty_score":0.943972},"labels":[],"label_agreement":null},{"id":"W4409991914","doi":"10.1109/alife-cis64968.2025.10979840","title":"Frequency-Based Multi-Objective Feature Selection to Enhance the Generalization of Evolutionary Algorithms","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Generalization; Computer science; Feature selection; Evolutionary algorithm; Selection (genetic algorithm); Algorithm; Artificial intelligence; Evolutionary computation; Feature (linguistics); Machine learning; Mathematics","score_opus":0.008427663799172449,"score_gpt":0.2921659980145152,"score_spread":0.2837383342153428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409991914","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":[0.00019170686,0.00014507446,0.99494654,0.00246666,0.00047273515,0.00070695544,0.000010657681,0.00024098257,0.0008187013],"genre_scores_gemma":[0.04353131,0.00001027179,0.95176643,0.0014533754,0.000042488842,0.00013347516,0.000014118179,0.000012703281,0.0030358257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983982,0.00016942111,0.00028644386,0.0005779663,0.00030717818,0.00026083042],"domain_scores_gemma":[0.998163,0.00014571712,0.0001517946,0.00050301926,0.0009751968,0.00006131321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018772479,0.00020753474,0.00019181534,0.00031569737,0.0002946175,0.00005267833,0.0006627636,0.0001089125,0.000018636503],"category_scores_gemma":[0.00023318622,0.00016295532,0.00008107494,0.0027928124,0.00007566424,0.00045912008,0.00014513922,0.00017869387,0.000013633965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027090713,0.00039881043,0.0022137775,0.00003259613,0.00009318138,0.0000023179916,0.00077733997,0.8847549,0.020218458,0.051174656,0.0049061147,0.035400774],"study_design_scores_gemma":[0.00031956908,0.000059332477,0.0081725195,0.00003141581,0.000009094756,0.0000025091933,0.000037145128,0.902527,0.08686087,0.0012340702,0.00056340493,0.00018311405],"about_ca_topic_score_codex":0.00013204046,"about_ca_topic_score_gemma":0.00011172967,"teacher_disagreement_score":0.06664242,"about_ca_system_score_codex":0.00029798603,"about_ca_system_score_gemma":0.00034426848,"threshold_uncertainty_score":0.6645125},"labels":[],"label_agreement":null},{"id":"W4410012132","doi":"10.1016/j.asoc.2025.113216","title":"Knowledge-guided global optimization for expensive and black-box constrained multi-objective engineering design problems","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced Multi-Objective Optimization Algorithms","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 Victoria","funders":"Northwestern Polytechnical University; National Natural Science Foundation of China","keywords":"Black box; Box–Behnken design; Computer science; Mathematical optimization; Multi-objective optimization; Engineering optimization; Artificial intelligence; Optimization problem; Machine learning; Response surface methodology; Mathematics; Algorithm","score_opus":0.023721737601208367,"score_gpt":0.2872424935392727,"score_spread":0.26352075593806434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410012132","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":[0.00017051246,0.0001329724,0.9960988,0.000078481076,0.00033231778,0.0019753682,0.0000050158983,0.00060707406,0.0005994709],"genre_scores_gemma":[0.16755685,0.000005508476,0.83209324,0.00014329884,0.00004014651,0.000097024655,0.000008982992,0.000022268125,0.000032684777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980138,0.000053581036,0.00045831213,0.000855883,0.00013470638,0.00048370505],"domain_scores_gemma":[0.9980922,0.00074585306,0.00020798537,0.00031735693,0.0005262149,0.00011040497],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037090533,0.00035071844,0.00037882317,0.00018158171,0.00034355882,0.00020115275,0.00040852604,0.00013562026,0.0000012310632],"category_scores_gemma":[0.00035736067,0.0003968566,0.000061395986,0.0008691726,0.00012697416,0.00026306583,0.00039385172,0.00015245547,0.000003455913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010042926,0.000060325478,0.000018962413,0.000058134523,0.00005773473,0.0000012497529,0.001311859,0.9579523,0.00041164178,0.020499961,0.00004974373,0.019568035],"study_design_scores_gemma":[0.0024113592,0.00003593658,0.00007867004,0.00008742404,0.000018264678,0.000009254681,0.00019687702,0.99415255,0.0016335638,0.0009588851,0.000051755153,0.00036547467],"about_ca_topic_score_codex":0.0000038870503,"about_ca_topic_score_gemma":0.0000012235354,"teacher_disagreement_score":0.16738634,"about_ca_system_score_codex":0.0002690716,"about_ca_system_score_gemma":0.00019661646,"threshold_uncertainty_score":0.9998483},"labels":[],"label_agreement":null},{"id":"W4410124548","doi":"10.1016/j.swevo.2025.101962","title":"Multimodal multi-objective optimization via multi-operator adaptation and clustering-based environmental selection","year":2025,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer 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":"University of Fredericton","funders":"National Natural Science Foundation of China","keywords":"Computer science; Adaptation (eye); Selection (genetic algorithm); Cluster analysis; Operator (biology); Artificial intelligence; Mathematical optimization","score_opus":0.010375414389769737,"score_gpt":0.2416787254102316,"score_spread":0.23130331102046187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410124548","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":[0.0039392277,0.00033422743,0.994185,0.00020470338,0.00036114865,0.0006608641,0.00001373206,0.0002744959,0.00002663134],"genre_scores_gemma":[0.4709111,0.000031500243,0.5287021,0.00013990725,0.000020694764,0.000045999095,0.00007617381,0.0000118271255,0.00006072187],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982623,0.00017691546,0.00035407537,0.00073026,0.00022017126,0.00025627998],"domain_scores_gemma":[0.9992927,0.00012479232,0.00016305962,0.00014079058,0.0001753744,0.00010330698],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015422265,0.0002785684,0.00020463795,0.0003980636,0.0006070416,0.00011872536,0.00012991476,0.00013724006,0.000005401614],"category_scores_gemma":[0.000050163493,0.00031525077,0.000043592012,0.00050666643,0.00013225258,0.0011263656,0.0001446542,0.00016747281,0.000006107867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039803377,0.00021234102,0.0014617222,0.00002082084,0.000025189476,0.0000016589955,0.00047348806,0.9762794,0.0004032181,0.000106612526,0.000012152906,0.020963628],"study_design_scores_gemma":[0.002592904,0.00011207737,0.03598661,0.000029360488,0.000018792072,0.000018363444,0.00015866438,0.96033156,0.0002567055,0.00018383934,0.000023747087,0.00028738493],"about_ca_topic_score_codex":0.00005483141,"about_ca_topic_score_gemma":0.000038154634,"teacher_disagreement_score":0.46697187,"about_ca_system_score_codex":0.0003604021,"about_ca_system_score_gemma":0.00011794486,"threshold_uncertainty_score":0.99992996},"labels":[],"label_agreement":null},{"id":"W4410460921","doi":"10.1007/s10957-025-02691-8","title":"Adaptive Generalized Conditional Gradient Method for Multiobjective Optimization","year":2025,"lang":"en","type":"article","venue":"Journal of Optimization Theory and Applications","topic":"Advanced Multi-Objective Optimization Algorithms","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":"Perimeter Institute","funders":"","keywords":"Mathematics; Theory of computation; Mathematical optimization; Multi-objective optimization; Gradient method; Applied mathematics; Algorithm","score_opus":0.010650661419480616,"score_gpt":0.312289075429338,"score_spread":0.30163841400985736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410460921","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":[0.0000031145053,0.00025989188,0.9976756,0.00047564262,0.00015380987,0.0008669112,0.00002959362,0.00005181435,0.00048364422],"genre_scores_gemma":[0.0024974968,0.00016772465,0.99603105,0.00048532343,0.00009339147,0.00030631127,0.000039114275,0.000013664221,0.00036594408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852216,0.00028058336,0.0005552796,0.00031373935,0.00016799795,0.00016026541],"domain_scores_gemma":[0.99678063,0.0008756258,0.0006107686,0.0002236643,0.0014137866,0.000095516254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000918651,0.00017227218,0.00026967816,0.00041406258,0.00041035697,0.00010454775,0.00033401165,0.00008748664,0.000019526971],"category_scores_gemma":[0.00026647898,0.00016353617,0.0001153282,0.0007157284,0.000094860836,0.00078187534,0.000070845985,0.00013404901,6.6420625e-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":[0.00006609732,0.00007831497,0.0000023709547,0.000005344495,0.00005588977,2.5416742e-7,0.00010703849,0.5833791,0.000024958683,0.4103834,0.00005467234,0.0058426023],"study_design_scores_gemma":[0.0015628575,0.00008418294,0.000023626251,0.000020179814,0.000059791415,0.000028835888,0.00017928822,0.89728457,0.00056947075,0.09909286,0.0009532334,0.00014110813],"about_ca_topic_score_codex":6.9683654e-7,"about_ca_topic_score_gemma":2.4601093e-7,"teacher_disagreement_score":0.3139055,"about_ca_system_score_codex":0.00012097338,"about_ca_system_score_gemma":0.00016807966,"threshold_uncertainty_score":0.66688114},"labels":[],"label_agreement":null},{"id":"W4410767107","doi":"10.4271/02-18-04-0023","title":"Design Space Exploration of a Continuous Rubber Track System via Surrogate Modeling","year":2025,"lang":"en","type":"article","venue":"SAE International journal of commercial vehicles","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Track (disk drive); Natural rubber; Space (punctuation); Computer science; Aerospace engineering; Engineering; Mechanical engineering; Materials science","score_opus":0.031083650341654966,"score_gpt":0.29757846423496737,"score_spread":0.2664948138933124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410767107","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":[0.010738084,0.00018542632,0.9853857,0.0017368534,0.0014195009,0.00015484389,0.000003409626,0.000041963078,0.00033422734],"genre_scores_gemma":[0.78332734,0.000045804154,0.21633993,0.000105856474,0.00012184095,0.0000040876885,0.0000014102019,0.000008976478,0.000044778004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981115,0.00021660246,0.00077093195,0.00017525959,0.0005777688,0.00014797112],"domain_scores_gemma":[0.9966598,0.0002518198,0.0006478972,0.00018148172,0.0021995874,0.00005938086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006370361,0.00014814745,0.0003218044,0.00043890203,0.00007418347,0.00011202135,0.0009838149,0.000068402864,0.00000382195],"category_scores_gemma":[0.00014596309,0.00014216967,0.00013441278,0.00032510216,0.00004864119,0.0015893929,0.00014185632,0.00020487834,0.000004648066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028776051,0.00024121233,0.00032194107,0.000024638512,0.00027539633,0.000059083264,0.0014862122,0.85321885,0.007964391,0.024365988,0.00016935635,0.111585155],"study_design_scores_gemma":[0.001537697,0.00010480408,0.0005594157,0.00035557643,0.000026089385,0.00006012481,0.00034637278,0.97752935,0.012981766,0.006170716,0.00018324683,0.00014486765],"about_ca_topic_score_codex":0.00002601451,"about_ca_topic_score_gemma":0.000009101623,"teacher_disagreement_score":0.77258927,"about_ca_system_score_codex":0.00020813191,"about_ca_system_score_gemma":0.0001870778,"threshold_uncertainty_score":0.5797511},"labels":[],"label_agreement":null},{"id":"W4410904053","doi":"10.21203/rs.3.rs-6657064/v1","title":"Benchmarking constrained, multi-objective and surrogate-assisted derivative-free optimization methods","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Polytechnique Montréal","funders":"","keywords":"Benchmarking; Derivative (finance); Computer science; Mathematical optimization; Mathematics; Economics","score_opus":0.08449971043528098,"score_gpt":0.44349993854042585,"score_spread":0.35900022810514487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410904053","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":[0.000056587043,0.00093121565,0.9919967,0.00058765506,0.0006858284,0.0023049605,0.00019343437,0.00042891942,0.002814703],"genre_scores_gemma":[0.0034673482,0.0007143981,0.9943061,0.00006460191,0.0001003417,0.0005063935,0.00014217627,0.00004612002,0.0006525218],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99170375,0.0033241506,0.00069692027,0.0021498096,0.0011339039,0.0009914702],"domain_scores_gemma":[0.990806,0.0029695926,0.0003771727,0.0020268788,0.003474904,0.00034547882],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0036029357,0.0006182643,0.0007725545,0.0015495227,0.00081801746,0.0007951579,0.002196671,0.0006042371,0.000051235118],"category_scores_gemma":[0.005298504,0.000651527,0.0001809856,0.002365921,0.0006971519,0.00072311715,0.009071965,0.0022335153,0.0000044816916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007034732,0.00044906925,0.000647335,0.0009727377,0.00035054443,0.00009794731,0.0038588583,0.7183901,0.00019931016,0.0109397415,0.00020455355,0.26381946],"study_design_scores_gemma":[0.0016752435,0.000119719734,0.0026006603,0.00083394273,0.00001689602,0.00001707841,0.0005099432,0.98941875,0.00082854135,0.0031671554,0.00022294927,0.00058914296],"about_ca_topic_score_codex":0.00020824623,"about_ca_topic_score_gemma":0.00006593296,"teacher_disagreement_score":0.27102864,"about_ca_system_score_codex":0.0008273278,"about_ca_system_score_gemma":0.0012968932,"threshold_uncertainty_score":0.9995936},"labels":[],"label_agreement":null},{"id":"W4411879513","doi":"10.3390/mi16070753","title":"Using Adaptive Surrogate Models to Accelerate Multi-Objective Design Optimization of MEMS","year":2025,"lang":"en","type":"article","venue":"Micromachines","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Institute for Nanotechnology; National Research Council Canada; University of Manitoba","funders":"National Research Council Canada","keywords":"Robustness (evolution); Actuator; Finite element method; Flexibility (engineering); Computer science; Microelectromechanical systems; Engineering optimization; Mathematical optimization; Surrogate model; Optimization problem; Multi-objective optimization; Control engineering; Engineering; Artificial intelligence; Algorithm; Machine learning; Mathematics","score_opus":0.08309343457273025,"score_gpt":0.32098100372323973,"score_spread":0.2378875691505095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411879513","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":[0.0014727366,0.00016249898,0.99668455,0.00008259475,0.000345113,0.0007808852,0.000017596962,0.00017940508,0.00027460663],"genre_scores_gemma":[0.109183684,0.000015782665,0.89030147,0.00020031306,0.00001540117,0.00003182029,0.0000042584857,0.000023243761,0.00022403391],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982517,0.00018914253,0.00041723944,0.0006426855,0.00018320396,0.00031604856],"domain_scores_gemma":[0.9983621,0.0001614931,0.00021421185,0.00046978786,0.0007056267,0.000086736996],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027483643,0.00029199707,0.00036832414,0.00049948524,0.0002063118,0.00009349867,0.0006854227,0.000088677465,0.000005817719],"category_scores_gemma":[0.00009053995,0.00028790935,0.00008300809,0.0015820626,0.00006562945,0.0010253789,0.00039894192,0.00013055914,0.0000045247775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004398902,0.00010977477,0.0000325662,0.0000086657865,0.000052106694,0.0000033694894,0.0011026114,0.9698559,0.02493402,0.0012166261,0.000019156858,0.0026211906],"study_design_scores_gemma":[0.00077573507,0.00005786292,0.000101204314,0.00006357097,0.000015526635,0.0000045208735,0.00006964678,0.9318858,0.065665424,0.0011137024,0.00000583102,0.00024117435],"about_ca_topic_score_codex":0.00019749098,"about_ca_topic_score_gemma":0.000017704871,"teacher_disagreement_score":0.10771095,"about_ca_system_score_codex":0.0001776965,"about_ca_system_score_gemma":0.00017821013,"threshold_uncertainty_score":0.9999573},"labels":[],"label_agreement":null},{"id":"W4412749127","doi":"10.1016/j.asoc.2025.113510","title":"Multi-scale niching based differential evolution for feature selection on high-dimensional data","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Hubei University of Technology; National Natural Science Foundation of China; Ontario Ministry of Natural Resources and Forestry","keywords":"Feature selection; Computer science; Scale (ratio); Differential evolution; Selection (genetic algorithm); Feature (linguistics); Differential (mechanical device); Data mining; Artificial intelligence; Pattern recognition (psychology); Machine learning; Physics","score_opus":0.01595772653215268,"score_gpt":0.28278824228581445,"score_spread":0.2668305157536618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412749127","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":[0.0013498252,0.000013540776,0.99617857,0.0003234406,0.00087129336,0.00065935194,0.00002348949,0.0005009742,0.00007954596],"genre_scores_gemma":[0.41854683,1.1177598e-7,0.5808014,0.00032815544,0.00008929414,0.000015375446,0.00015035557,0.000013955735,0.000054487256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803066,0.000056951965,0.00027297778,0.0009971646,0.00026851197,0.0003737256],"domain_scores_gemma":[0.9984351,0.0005004044,0.00018403887,0.00066946005,0.00014769242,0.00006331968],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028631795,0.00024792927,0.0002459451,0.0002294643,0.0007239283,0.00014009929,0.00083142443,0.00013240777,0.0000021458525],"category_scores_gemma":[0.0001079605,0.00025792595,0.000054501463,0.00061266345,0.000036169302,0.00024186539,0.0005500695,0.00031638643,0.0000069633124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007431188,0.00031215558,0.00018369066,0.00003468854,0.000048656937,9.3305414e-7,0.000081311904,0.92243046,0.011996432,0.028912367,0.000879609,0.035045393],"study_design_scores_gemma":[0.0018612901,0.000031136366,0.003469585,0.000046837336,0.000015661486,0.000001371785,0.00000955274,0.99090534,0.0020070965,0.0013358325,0.00007772751,0.00023855022],"about_ca_topic_score_codex":0.000013074964,"about_ca_topic_score_gemma":0.000017190658,"teacher_disagreement_score":0.41719702,"about_ca_system_score_codex":0.00032107442,"about_ca_system_score_gemma":0.00015824665,"threshold_uncertainty_score":0.9999873},"labels":[],"label_agreement":null},{"id":"W4413189063","doi":"10.3390/math13162579","title":"Beyond the Pareto Front: Utilizing the Entire Population for Decision-Making in Evolutionary Machine Learning","year":2025,"lang":"en","type":"article","venue":"Mathematics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Brock University; Wilfrid Laurier University","funders":"","keywords":"Pareto principle; Population; Artificial intelligence; Computer science; Front (military); Multi-objective optimization; Evolutionary algorithm; Machine learning; Operations research; Economics; Engineering; Sociology; Operations management; Demography; Mechanical engineering","score_opus":0.012432316410969544,"score_gpt":0.2949834346573994,"score_spread":0.28255111824642987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413189063","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":[0.0008920724,0.00038240448,0.9959583,0.000593054,0.00027056763,0.0005448796,0.000002630357,0.00007943465,0.0012766322],"genre_scores_gemma":[0.3996088,0.000011513228,0.59987164,0.00016486816,0.000020122527,0.000070540824,0.0000038074627,0.000009884049,0.00023883153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897027,0.00006800651,0.00033108,0.00023003247,0.00021030985,0.00019032328],"domain_scores_gemma":[0.9971726,0.0021650686,0.00015351102,0.0003975122,0.000097187236,0.00001414988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000521645,0.000121220844,0.00014420245,0.000113182476,0.00047653192,0.000106443695,0.00056632183,0.000044965444,0.000007979935],"category_scores_gemma":[0.0015586667,0.00007781481,0.000060360988,0.00044026138,0.000032540916,0.00025427202,0.00025616944,0.00017893869,0.000005448451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018074677,0.00017962053,0.0055977986,0.00010437586,0.00004174928,0.0000038158196,0.008788809,0.40678647,0.000020926489,0.3897809,0.00051899644,0.18815847],"study_design_scores_gemma":[0.00013247033,0.000007954295,0.002505797,0.00016045234,0.0000062634986,0.0000033158437,0.00044925124,0.84667903,0.0000071537584,0.14962979,0.00034666253,0.00007188306],"about_ca_topic_score_codex":0.00000799509,"about_ca_topic_score_gemma":0.00004964138,"teacher_disagreement_score":0.43989256,"about_ca_system_score_codex":0.00013268582,"about_ca_system_score_gemma":0.000031401214,"threshold_uncertainty_score":0.36651465},"labels":[],"label_agreement":null},{"id":"W4413215786","doi":"10.1145/3712255.3726660","title":"A Hyper-feasible Solutions Based Update Weight Vectors Evolutionary Algorithm for Constrained Multiobjective Optimization Problem","year":2025,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Mathematical optimization; Evolutionary algorithm; Computer science; Multi-objective optimization; Evolutionary computation; Algorithm; Mathematics","score_opus":0.014630136503367846,"score_gpt":0.24461295393666962,"score_spread":0.2299828174333018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413215786","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":[0.0011379648,0.00038059376,0.9941418,0.0016238231,0.0004562818,0.0015334488,0.00005845674,0.00019330451,0.00047432157],"genre_scores_gemma":[0.2983431,0.000049726583,0.70115644,0.00011581426,0.000035332963,0.00016823059,0.000036477493,0.000012886924,0.00008202476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979439,0.00005783514,0.0005817436,0.0006895647,0.00034960645,0.00037734737],"domain_scores_gemma":[0.9967402,0.00020529689,0.00045870774,0.0001746804,0.0023250366,0.0000960451],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002254001,0.00030740944,0.0003305222,0.00038513617,0.0008352139,0.00011752097,0.00057065487,0.00013211333,0.0000088156075],"category_scores_gemma":[0.00011698864,0.00028573335,0.00012875743,0.0011016834,0.00037674684,0.00066982297,0.0003270914,0.00016764298,0.0000017477101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052279687,0.0003503871,0.0020722405,0.0001502703,0.0000927256,1.879525e-7,0.00029253733,0.8912103,0.00073845906,0.052355383,0.0012455262,0.051439676],"study_design_scores_gemma":[0.0015547114,0.000094546944,0.015005267,0.00016467953,0.000048912898,0.000016059144,0.00013266563,0.9578562,0.0003820475,0.024251623,0.00022271641,0.00027057098],"about_ca_topic_score_codex":0.000015444988,"about_ca_topic_score_gemma":0.0000011113009,"teacher_disagreement_score":0.29720512,"about_ca_system_score_codex":0.00024702895,"about_ca_system_score_gemma":0.0005508537,"threshold_uncertainty_score":0.99995947},"labels":[],"label_agreement":null},{"id":"W4413217170","doi":"10.1145/3712255.3734303","title":"BEACON: Continuous Bi-objective Benchmark problems with Explicit Adjustable COrrelatioN control","year":2025,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Foreign, Commonwealth and Development Office; International Development Research Centre","keywords":"Benchmark (surveying); Computer science; Correlation; Control (management); Mathematics; Artificial intelligence; Geometry; Geology","score_opus":0.007286590246987096,"score_gpt":0.21249624364268402,"score_spread":0.2052096533956969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413217170","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":[0.041061886,0.00040855698,0.95432365,0.0006587573,0.00026819267,0.0009752001,0.0000068606746,0.000112386704,0.0021845102],"genre_scores_gemma":[0.8971485,0.00004322115,0.10230858,0.00011070743,0.00002529988,0.00007035831,0.000005849785,0.000009523091,0.00027798742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983949,0.000040629442,0.00042477754,0.0005361755,0.0003453133,0.0002581977],"domain_scores_gemma":[0.9975697,0.00013940154,0.00044892478,0.00014912883,0.0016245097,0.000068352216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016582709,0.00024295335,0.00031800152,0.00021324931,0.00038507278,0.00012293283,0.0004597688,0.00009052461,0.0000061297355],"category_scores_gemma":[0.00006563022,0.0001945041,0.00005156842,0.00086171314,0.00019353263,0.0006594739,0.0002219935,0.00019447699,0.0000021284159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022247074,0.0004470781,0.1198287,0.0003284632,0.00025867004,0.0000010549397,0.0030820973,0.6528556,0.003610293,0.16942638,0.0014579135,0.0484813],"study_design_scores_gemma":[0.0013291112,0.00013561841,0.20396028,0.00021852672,0.000037456695,0.000025443109,0.00035495908,0.7760156,0.00028905808,0.017336752,0.00010165434,0.00019557549],"about_ca_topic_score_codex":0.000030141531,"about_ca_topic_score_gemma":0.0000026253315,"teacher_disagreement_score":0.8560866,"about_ca_system_score_codex":0.00011802333,"about_ca_system_score_gemma":0.00020081754,"threshold_uncertainty_score":0.79316473},"labels":[],"label_agreement":null},{"id":"W4413217398","doi":"10.1145/3712255.3726593","title":"A Constrained Multi-objective Co-Evolutionary Algorithm Based on Operator Score and Reward","year":2025,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Operator (biology); Evolutionary algorithm; Evolutionary computation; Mathematical optimization; Artificial intelligence; Mathematics; Biology","score_opus":0.017290752717829837,"score_gpt":0.2625716933148158,"score_spread":0.24528094059698594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413217398","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":[0.022744572,0.0003048891,0.9741095,0.0010331305,0.0002722582,0.00072777295,0.000028574927,0.00011944637,0.00065991376],"genre_scores_gemma":[0.6134586,0.000035894358,0.38610125,0.00026570793,0.000018822982,0.00003366999,0.0000071771974,0.000007670045,0.000071223505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983252,0.00005682475,0.000398538,0.0006353335,0.00034201922,0.00024203399],"domain_scores_gemma":[0.9982639,0.00016585251,0.0002631195,0.00015201017,0.0010567106,0.00009838701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016662126,0.00026849165,0.00029116462,0.0002906592,0.00048409752,0.000110447,0.00042340782,0.000101415535,0.0000050344106],"category_scores_gemma":[0.00012590976,0.000234844,0.000060301434,0.00064118934,0.00047509928,0.00036921274,0.00027753043,0.00020376741,0.0000022791894],"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":[0.00043532535,0.0021195344,0.12468088,0.0008141033,0.00039219492,0.00000755119,0.0041201646,0.3383418,0.007418291,0.15041621,0.0055532437,0.36570072],"study_design_scores_gemma":[0.0011689462,0.00011239606,0.19073817,0.00018793833,0.000014950154,0.000020170763,0.00018504968,0.80182415,0.00037023303,0.0051220986,0.00006945494,0.00018641316],"about_ca_topic_score_codex":0.000012489371,"about_ca_topic_score_gemma":5.40693e-7,"teacher_disagreement_score":0.59071404,"about_ca_system_score_codex":0.00013050022,"about_ca_system_score_gemma":0.00032149654,"threshold_uncertainty_score":0.95766604},"labels":[],"label_agreement":null},{"id":"W4413217936","doi":"10.2139/ssrn.5386966","title":"A Reward-Directed Diffusion Framework for Generative Design Optimization","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Diffusion; Generative grammar; Computer science; Mathematical optimization; Mathematics; Artificial intelligence; Thermodynamics; Physics","score_opus":0.019654085544513294,"score_gpt":0.29512955125753837,"score_spread":0.27547546571302506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413217936","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":[0.000008614706,0.00208989,0.9929494,0.001084058,0.001797328,0.0014654327,0.000019675572,0.0004725919,0.000112984126],"genre_scores_gemma":[0.00051653537,0.006767021,0.98980135,0.00025882706,0.000497834,0.00025363383,0.00004544791,0.000050901934,0.0018084556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99512744,0.000512914,0.00067799137,0.0010301333,0.00047343486,0.0021780627],"domain_scores_gemma":[0.9965359,0.0005346446,0.0008552871,0.0007579063,0.0011730875,0.00014319888],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00162837,0.00055267784,0.00058010034,0.0005327397,0.00064619305,0.00043133623,0.0016364673,0.0005835986,0.000012997134],"category_scores_gemma":[0.0010495245,0.00054247986,0.00031108744,0.0007119672,0.000052879015,0.0004573085,0.00078106485,0.0041516903,0.0000035967735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006068435,0.00009548832,0.000003889856,0.000016164162,0.00020425337,0.0000021578887,0.00035814612,0.8370576,0.000013955073,0.13564427,0.00006882628,0.026474595],"study_design_scores_gemma":[0.0005293591,0.00014432642,0.000002685397,0.00013566767,0.00003796951,0.00003628382,0.000059555852,0.5869858,0.00015390026,0.4115055,0.000087727814,0.00032121423],"about_ca_topic_score_codex":0.0000101589185,"about_ca_topic_score_gemma":0.000017997212,"teacher_disagreement_score":0.2758612,"about_ca_system_score_codex":0.0030393128,"about_ca_system_score_gemma":0.0072894003,"threshold_uncertainty_score":0.9997027},"labels":[],"label_agreement":null},{"id":"W4414140393","doi":"10.1016/j.eswa.2025.129460","title":"Regularity model-driven large-scale multi-objective evolutionary algorithm based on dual-information offspring reproduction strategy","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Artificial Intelligence in Medicine (Canada)","funders":"Dalian Science and Technology Innovation Fund; Higher Education Discipline Innovation Project; China Academy of Space Technology; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Evolutionary algorithm; Benchmark (surveying); Population; Exploit; Set (abstract data type); Estimation of distribution algorithm; Pareto principle; Optimization problem","score_opus":0.011455052258397037,"score_gpt":0.266928922922486,"score_spread":0.25547387066408894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414140393","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":[0.000021535858,0.00020324961,0.99379855,0.00045332694,0.00026926497,0.0025178194,0.000069689195,0.0006986677,0.0019678758],"genre_scores_gemma":[0.07143656,0.000021913926,0.92257655,0.00025647882,0.0001279808,0.0044576037,0.00016827325,0.00002752057,0.00092713896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973762,0.00013396773,0.0005904867,0.0009782489,0.00051190204,0.00040916866],"domain_scores_gemma":[0.9968174,0.000079530495,0.00036258704,0.0016540514,0.0009547948,0.00013164977],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031918913,0.00033633472,0.00031760192,0.00046806227,0.00078926014,0.00021008615,0.00051502854,0.00016388747,0.0000029582118],"category_scores_gemma":[0.000043217533,0.00032241136,0.0000805468,0.001445265,0.00009015126,0.0016777541,0.00013164748,0.00028915875,0.00004744057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019044355,0.0003425113,0.00006119387,0.000031253083,0.000030473577,9.925411e-7,0.00062206073,0.97490704,0.000119458884,0.015342667,0.0004388445,0.0080844425],"study_design_scores_gemma":[0.0011684562,0.000058520454,0.000679159,0.00009907148,0.000009830455,0.000013156314,0.0005948612,0.9909429,0.0005318964,0.00018125155,0.0053856303,0.0003353004],"about_ca_topic_score_codex":0.0000973738,"about_ca_topic_score_gemma":0.0000120916,"teacher_disagreement_score":0.07141502,"about_ca_system_score_codex":0.00068107946,"about_ca_system_score_gemma":0.0003979161,"threshold_uncertainty_score":0.9999228},"labels":[],"label_agreement":null},{"id":"W4414222950","doi":"10.1609/icaps.v35i1.36109","title":"Parallelizing Multi-objective A* Search","year":2025,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Curse of dimensionality; Graph; Enhanced Data Rates for GSM Evolution; Shortest path problem; Optimization problem; Path (computing); Search algorithm; Key (lock)","score_opus":0.04308071131536093,"score_gpt":0.3321844671879373,"score_spread":0.28910375587257636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414222950","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":[0.34680542,0.00019038156,0.6147876,0.004890281,0.0011722855,0.00053730427,0.000010123059,0.0011840432,0.030422553],"genre_scores_gemma":[0.8218466,0.00001623093,0.17755869,0.00019126442,0.000013558111,0.000011454564,9.87993e-7,0.000005917533,0.0003552645],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998879,0.000012472294,0.00024773847,0.00036580054,0.00031373,0.00018123798],"domain_scores_gemma":[0.9988897,0.00009213632,0.00016613495,0.000101523714,0.0007098526,0.000040675055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029019325,0.00014812211,0.00015823578,0.00023943729,0.00020849642,0.00024491418,0.00092289736,0.000067888854,0.0000036054807],"category_scores_gemma":[0.00032369854,0.000118783966,0.000044722347,0.0003760096,0.00007969882,0.0003915665,0.00040863993,0.00027638522,0.000002382584],"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":[0.0001400258,0.0002554672,0.02775611,0.00012059436,0.00036104576,0.0000059645236,0.0059039975,0.13547349,0.061850313,0.7562153,0.00019431807,0.011723423],"study_design_scores_gemma":[0.000455592,0.000021645039,0.0062409905,0.0005069552,0.0000048786733,0.000007111131,0.000687081,0.9712075,0.01923667,0.0014989665,0.000019864035,0.000112774535],"about_ca_topic_score_codex":0.000012536496,"about_ca_topic_score_gemma":3.549164e-7,"teacher_disagreement_score":0.83573395,"about_ca_system_score_codex":0.000070387425,"about_ca_system_score_gemma":0.00008790151,"threshold_uncertainty_score":0.48438695},"labels":[],"label_agreement":null},{"id":"W4414261676","doi":"10.1002/nme.70118","title":"A Framework for Nonlinearly‐Constrained Gradient‐Enhanced Local Bayesian Optimization With Comparisons to Quasi‐Newton Optimizers","year":2025,"lang":"en","type":"article","venue":"International Journal for Numerical Methods in Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer 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":"Institute for Christian Studies; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayesian optimization; Leverage (statistics); Bayesian probability; Convergence (economics); Minification; Function (biology); Global optimization; Optimization problem","score_opus":0.0196110175085854,"score_gpt":0.3928689335461665,"score_spread":0.3732579160375811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414261676","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":[0.000009359052,0.00003584592,0.99388283,0.0022467587,0.0030183245,0.0006352749,0.000011119717,0.00012031717,0.000040187377],"genre_scores_gemma":[0.004673263,0.000010841619,0.9945062,0.00034257703,0.00013688256,0.0002231755,0.0000088502275,0.000034763052,0.00006348118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981542,0.00008151784,0.00059201574,0.0004618215,0.00029501726,0.00041540727],"domain_scores_gemma":[0.9973967,0.0013869174,0.00018365476,0.0002133348,0.0006218083,0.00019759293],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005697536,0.00026264618,0.00038835287,0.00069659477,0.00011606381,0.00022791949,0.0008899263,0.00011037257,0.0000056864606],"category_scores_gemma":[0.0016394048,0.00025405627,0.00014549971,0.00090961,0.000039360308,0.00046213187,0.00011462085,0.00039312406,5.3426976e-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":[0.00016741482,0.000115328905,0.000016965574,0.000011735359,0.00010349773,0.0000057235466,0.00019145703,0.92791355,0.00013509401,0.019340206,0.000031580574,0.051967442],"study_design_scores_gemma":[0.00148417,0.00021915948,0.000026620102,0.00024778798,0.000013356227,0.000034688957,0.0001016966,0.9924118,0.0014435584,0.0027512051,0.000992537,0.00027341658],"about_ca_topic_score_codex":0.000005288754,"about_ca_topic_score_gemma":0.0000011458693,"teacher_disagreement_score":0.06449825,"about_ca_system_score_codex":0.0005133632,"about_ca_system_score_gemma":0.0001325228,"threshold_uncertainty_score":0.9999912},"labels":[],"label_agreement":null},{"id":"W4415598810","doi":"10.1115/detc2025-168995","title":"RePaint-Enhanced Conditional Diffusion Model for Generating Designs Under Performance Constraints","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Flexibility (engineering); Controllability; Parametric statistics; Hull; Probabilistic logic; Design of experiments; Parametric model; Optimal design; Diffusion","score_opus":0.04076867937342012,"score_gpt":0.307810912348404,"score_spread":0.2670422329749839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415598810","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":[0.0015625338,0.00008143665,0.9908286,0.00059356866,0.0009782618,0.0017351141,0.00006712067,0.00027694184,0.0038764467],"genre_scores_gemma":[0.4335911,0.00006494333,0.5533774,0.0018058644,0.00007072143,0.00018943325,0.000045643803,0.000022461798,0.010832446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960456,0.0001379068,0.0010096378,0.0014772846,0.00048129866,0.0008482544],"domain_scores_gemma":[0.9967406,0.00055611297,0.000395671,0.00074437266,0.0013554513,0.00020781707],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0006088773,0.0005580157,0.000506347,0.00035216633,0.0014063141,0.00035699402,0.0007681691,0.00027155882,0.00023112507],"category_scores_gemma":[0.00033422542,0.0005874789,0.00022921794,0.00082141586,0.00058180233,0.0013547784,0.00045460742,0.00033386087,0.00002599631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040679857,0.00019599276,0.00001595687,0.00006909929,0.00006596482,9.344448e-7,0.00037042965,0.86389285,0.008793475,0.0702008,0.00026620363,0.056087602],"study_design_scores_gemma":[0.0026763428,0.0001286158,0.0001127607,0.00017615994,0.00003544045,0.000007742831,0.00016792615,0.9693512,0.017200883,0.009508488,0.000028259557,0.00060620485],"about_ca_topic_score_codex":0.0000027845128,"about_ca_topic_score_gemma":0.0000074200607,"teacher_disagreement_score":0.43745118,"about_ca_system_score_codex":0.0005086156,"about_ca_system_score_gemma":0.0012328676,"threshold_uncertainty_score":0.9998937},"labels":[],"label_agreement":null},{"id":"W4415960426","doi":"10.26434/chemrxiv-2025-f1wcr","title":"Honegumi RAG Assistant: An Agentic System for Accelerating Bayesian Optimization Adoption in Experimental Sciences","year":2025,"lang":"","type":"article","venue":"ChemRxiv","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Code (set theory); Domain (mathematical analysis); Documentation; Bayesian optimization; Code generation; Bayesian network; Source code; Bayesian probability","score_opus":0.035920299597437456,"score_gpt":0.31785016600266486,"score_spread":0.2819298664052274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415960426","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":[0.008707419,0.00059167575,0.98491687,0.00016525057,0.0019664806,0.0017110322,0.00000364019,0.00023905934,0.0016985778],"genre_scores_gemma":[0.579695,0.000020080135,0.41967082,0.00007895629,0.00009420495,0.00023018435,0.000017872722,0.000025435558,0.00016747256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957436,0.0002552466,0.0010548338,0.0016710656,0.0004769448,0.0007983153],"domain_scores_gemma":[0.99803436,0.00018116804,0.00052297267,0.0006707769,0.0004064415,0.00018430286],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0010336268,0.0005143518,0.0005436623,0.0007516518,0.0012205306,0.0011310542,0.0011232612,0.00025485773,0.0000359237],"category_scores_gemma":[0.00020338944,0.000593,0.00015323484,0.0029573105,0.0003352494,0.0029636282,0.00031914655,0.00024051538,0.0000055759297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060036557,0.0006126594,0.00033115828,0.0002738895,0.000029692987,0.000010485477,0.0019402622,0.9701758,0.007788468,0.01059051,0.000012888957,0.008174142],"study_design_scores_gemma":[0.0019911488,0.00019781444,0.0002339826,0.0005573398,0.000023377923,0.000006815662,0.0028547165,0.91036934,0.08318079,0.00006471513,0.000020447664,0.00049951934],"about_ca_topic_score_codex":0.000019580566,"about_ca_topic_score_gemma":0.000010147749,"teacher_disagreement_score":0.5709876,"about_ca_system_score_codex":0.0011876961,"about_ca_system_score_gemma":0.0005039542,"threshold_uncertainty_score":0.9999059},"labels":[],"label_agreement":null},{"id":"W4416008725","doi":"10.1080/00401706.2025.2584500","title":"Genetic Algorithm-Based Bayesian Optimal Design for Network Experiments","year":2025,"lang":"en","type":"article","venue":"Technometrics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayesian probability; Design of experiments; Bayesian network; Bayesian experimental design; Genetic algorithm; Optimal design","score_opus":0.0236615742409144,"score_gpt":0.2951492238251354,"score_spread":0.271487649584221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416008725","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":[0.000010748013,0.00086895394,0.9965794,0.00012611586,0.00049410097,0.0010278,0.0000065953864,0.0006834671,0.00020282567],"genre_scores_gemma":[0.0053523486,0.000021042513,0.9935649,0.0003425891,0.00005245995,0.00036421212,0.000004747339,0.000025250349,0.00027242937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827075,0.00003643446,0.00032769796,0.0006084782,0.00026348582,0.00049314613],"domain_scores_gemma":[0.99839956,0.00039319808,0.00013382548,0.0007287776,0.00025431224,0.00009033757],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030445895,0.00023032883,0.00024128608,0.0011832372,0.00025365246,0.00013452192,0.0011159948,0.00013884151,0.0000071355416],"category_scores_gemma":[0.000305538,0.00024949684,0.000090668174,0.007504706,0.00006195071,0.00022115394,0.00022829925,0.00013084592,0.000007822515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000759552,0.00012933972,0.000084765124,0.0000106645675,0.000022238115,0.0000054013526,0.00001774054,0.65633667,0.00003963703,0.0036748427,0.0011548732,0.33851627],"study_design_scores_gemma":[0.00089858996,0.00013056216,0.00016378763,0.000016606478,0.0000097138645,0.0000013229997,0.00000874393,0.98674035,0.005785898,0.0031285242,0.0028632234,0.00025268347],"about_ca_topic_score_codex":0.0000019241706,"about_ca_topic_score_gemma":8.6816755e-8,"teacher_disagreement_score":0.33826357,"about_ca_system_score_codex":0.00024526875,"about_ca_system_score_gemma":0.00016652464,"threshold_uncertainty_score":0.9999957},"labels":[],"label_agreement":null},{"id":"W4416034102","doi":"10.18653/v1/2025.findings-emnlp.838","title":"LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet?","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Bayesian probability; Key (lock); Bayesian inference; Bayesian optimization","score_opus":0.01814474607173039,"score_gpt":0.2931597976741683,"score_spread":0.2750150516024379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416034102","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":[0.00003392121,0.0012513542,0.9816096,0.0053713345,0.004085603,0.002500933,0.000036699712,0.0002228915,0.0048876945],"genre_scores_gemma":[0.030020457,0.0004208175,0.942543,0.0005677672,0.00006419358,0.00029017587,0.000027988772,0.000049519756,0.026016029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954658,0.00022844657,0.0009768744,0.0019257278,0.0004896712,0.000913481],"domain_scores_gemma":[0.99668175,0.00042428475,0.00044062574,0.0013130581,0.0009592903,0.00018098205],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008854172,0.00053077115,0.00056674733,0.0012154345,0.0008820332,0.0013509344,0.0014433506,0.00029868798,0.00026021196],"category_scores_gemma":[0.00048434825,0.00054755923,0.00020360407,0.005125032,0.00041547403,0.0018530218,0.00055122614,0.00030291665,0.000024277879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039206403,0.0003842474,0.0003134424,0.00011315085,0.000033883956,0.00000909792,0.0007751104,0.91980433,0.000034490102,0.03008813,0.0009994445,0.04740545],"study_design_scores_gemma":[0.0027058097,0.00007351342,0.00024519724,0.00054006453,0.000020941901,0.0000026483838,0.00084666145,0.9820394,0.0007815076,0.008386645,0.003815129,0.0005425132],"about_ca_topic_score_codex":0.000018735218,"about_ca_topic_score_gemma":0.00021501441,"teacher_disagreement_score":0.062235028,"about_ca_system_score_codex":0.0005944383,"about_ca_system_score_gemma":0.00067627593,"threshold_uncertainty_score":0.99969757},"labels":[],"label_agreement":null},{"id":"W4416893698","doi":"10.1016/j.engappai.2025.113378","title":"A reward-directed diffusion framework for generative design","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Compute Canada","keywords":"Diffusion; Generative grammar; Generative model; Generative Design; Surface fitting","score_opus":0.02645839830539809,"score_gpt":0.3086985925554244,"score_spread":0.28224019425002633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416893698","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":[0.000026784102,0.00012360758,0.99801,0.0002730412,0.00018886845,0.00094590016,0.000006616715,0.0003849253,0.000040285766],"genre_scores_gemma":[0.026057437,0.000025669147,0.9728607,0.0000387542,0.000045745735,0.00088394934,0.0000042347265,0.000012165555,0.00007134718],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989355,0.000021707527,0.00036642802,0.00037430914,0.00011537323,0.00018665324],"domain_scores_gemma":[0.9982527,0.0007030615,0.000104961386,0.0005198644,0.0003730221,0.00004640176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018424538,0.00014004474,0.0001695628,0.0002470644,0.00013401372,0.000048339312,0.0005862565,0.00008197513,0.0000042590723],"category_scores_gemma":[0.00050649175,0.00015384826,0.000059749276,0.0013190238,0.000044548415,0.00014921399,0.00010014781,0.00011789115,0.000008152512],"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":[0.00000523452,0.000060101156,0.0000011000312,0.0000124996905,0.000011973094,9.168989e-8,0.00016968406,0.42252746,0.004081075,0.49537966,0.000016874916,0.077734224],"study_design_scores_gemma":[0.000016471342,0.000022042714,0.000010704034,0.000031242816,0.0000050779367,3.5227535e-7,0.000025856012,0.76078075,0.1506403,0.08789374,0.00046860008,0.00010488728],"about_ca_topic_score_codex":0.0000046297087,"about_ca_topic_score_gemma":7.077553e-7,"teacher_disagreement_score":0.40748593,"about_ca_system_score_codex":0.00006681021,"about_ca_system_score_gemma":0.00006784847,"threshold_uncertainty_score":0.627375},"labels":[],"label_agreement":null},{"id":"W4416939867","doi":"10.2139/ssrn.5851456","title":"HeatGen: A Guided Diffusion Framework for Multiphysics Heat Sink Design Optimization","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Multiphysics; Surrogate model; Heat sink; Probabilistic logic; Pressure drop; Topology optimization; Optimization problem; Boundary value problem; Artificial neural network","score_opus":0.027805355877525875,"score_gpt":0.3117422621490365,"score_spread":0.28393690627151064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416939867","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":[0.0000262052,0.0042282976,0.9834585,0.0021919706,0.005003064,0.004597772,0.00005728653,0.00030522255,0.00013167119],"genre_scores_gemma":[0.0030198595,0.031640247,0.9598402,0.00061139674,0.0016021983,0.00040164884,0.00009034453,0.00018174095,0.0026123608],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9854983,0.001221979,0.0023988555,0.0026879185,0.0013513223,0.0068416335],"domain_scores_gemma":[0.99090195,0.0016553974,0.0016636935,0.001977404,0.0032709118,0.0005306748],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.004390424,0.001733012,0.0017018748,0.0009875557,0.0024228105,0.0012267317,0.0036555242,0.0014625518,0.000052712385],"category_scores_gemma":[0.0018700196,0.0018548245,0.0011847335,0.001964492,0.000254742,0.001446973,0.0017540234,0.009632829,0.000020970405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00030356945,0.0005665962,0.00001195161,0.00008177256,0.0005622814,0.000005312144,0.0008394716,0.8266829,0.00007862258,0.0970016,0.000055776876,0.07381014],"study_design_scores_gemma":[0.0024227775,0.00064346817,0.0000037938514,0.0006864372,0.0002091919,0.00020086346,0.00032107736,0.6518993,0.00038852735,0.3419312,0.0002301479,0.001063253],"about_ca_topic_score_codex":0.000052702664,"about_ca_topic_score_gemma":0.000024158227,"teacher_disagreement_score":0.24492958,"about_ca_system_score_codex":0.009522416,"about_ca_system_score_gemma":0.019564847,"threshold_uncertainty_score":0.99983376},"labels":[],"label_agreement":null},{"id":"W4417136649","doi":"10.1016/j.cor.2025.107351","title":"An exact penalty method with nonmonotone line search and rapid infeasibility detection for constrained multiobjective optimization: Application in supervised machine learning","year":2025,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Iran National Science Foundation","keywords":"Line search; Penalty method; Convergence (economics); Constraint (computer-aided design); Quadratic programming; Sequential quadratic programming; Line (geometry); Quadratic equation","score_opus":0.03756761558015091,"score_gpt":0.3951614774180427,"score_spread":0.35759386183789177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417136649","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":[0.006654037,0.00007374119,0.98925793,0.0005888211,0.00005829555,0.003129829,0.0000134931,0.00016535458,0.00005851894],"genre_scores_gemma":[0.39858982,0.000031968335,0.6006621,0.00004881967,0.000026012858,0.0005101505,0.00007721575,0.00001460591,0.000039303868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963824,0.0012390164,0.00042805195,0.0010969967,0.00039857216,0.0004549565],"domain_scores_gemma":[0.99642986,0.0009040881,0.000037927148,0.00063627335,0.0018326079,0.00015921441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025543892,0.00023261945,0.00030754425,0.0010118112,0.0010034089,0.00047887448,0.00052437757,0.00012183592,0.000006922626],"category_scores_gemma":[0.0003814249,0.0002263054,0.00003824187,0.0024543838,0.0002485358,0.0012772437,0.0002556178,0.0006418269,0.0000016563018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000123389,0.00023277769,0.00055146415,0.000027930997,0.00002167159,0.0000015772388,0.00085715274,0.854539,0.001970377,0.0010132006,5.2672397e-7,0.14066096],"study_design_scores_gemma":[0.0027875805,0.00059567334,0.00216486,0.00003509058,0.0000054269053,0.000010425371,0.00026042998,0.9885912,0.005224827,0.00008390303,0.000028105149,0.00021251154],"about_ca_topic_score_codex":0.00059888425,"about_ca_topic_score_gemma":0.0011498077,"teacher_disagreement_score":0.39193577,"about_ca_system_score_codex":0.00040304757,"about_ca_system_score_gemma":0.00044535118,"threshold_uncertainty_score":0.9228467},"labels":[],"label_agreement":null},{"id":"W4417382663","doi":"10.1145/3785134","title":"Algorithm 1060: EDOLAB, a Platform for Research and Education in Evolutionary Dynamic Optimization","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Mathematical Software","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Benchmark (surveying); Suite; Consistency (knowledge bases); MATLAB; Evolutionary algorithm; Optimization problem; Genetic algorithm","score_opus":0.030343149558077168,"score_gpt":0.3628512883966522,"score_spread":0.33250813883857505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417382663","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":[0.00009419607,0.000113791495,0.9973306,0.0009449519,0.0001820452,0.00104764,0.000020421205,0.00018595633,0.00008036982],"genre_scores_gemma":[0.00084034266,0.00006451058,0.9964931,0.00011746702,0.000010573226,0.00059280865,0.000018368128,0.000018063774,0.0018447834],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984527,0.00005781496,0.00034471333,0.0005215249,0.00028912918,0.00033410586],"domain_scores_gemma":[0.99754,0.001301931,0.000050011087,0.0005926279,0.00042426624,0.000091181304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003763629,0.00016895715,0.00020439834,0.0006614736,0.00038495238,0.000103947714,0.00049741677,0.0001426173,0.000027604248],"category_scores_gemma":[0.00061836565,0.00017142859,0.00005088605,0.0011933865,0.00012222175,0.0006708481,0.000042515574,0.00031322,0.000013693547],"study_design_candidate":"design_other","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":[0.00002101853,0.0007647101,0.000008895286,0.000104588195,0.000016833159,8.508791e-7,0.00022294943,0.070545904,0.0000058987202,0.0052327695,0.000022092592,0.9230535],"study_design_scores_gemma":[0.0004665351,0.00007900271,0.00010476284,0.00013158492,0.0000053493104,0.000006232621,0.00012782626,0.5486184,0.000049313247,0.4502072,0.00009077297,0.00011301115],"about_ca_topic_score_codex":0.0000056324607,"about_ca_topic_score_gemma":0.0000067084,"teacher_disagreement_score":0.9229405,"about_ca_system_score_codex":0.00040472808,"about_ca_system_score_gemma":0.00037520388,"threshold_uncertainty_score":0.6990655},"labels":[],"label_agreement":null},{"id":"W60686164","doi":"10.1007/978-3-642-25566-3_40","title":"Sequential Model-Based Optimization for General Algorithm Configuration","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2377,"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":"Solver; Computer science; Algorithm; Categorical variable; Mathematical optimization; Satisfiability; Integer programming; State (computer science); Optimization problem; Search algorithm; State space; Mathematics; Machine learning","score_opus":0.026164776281192334,"score_gpt":0.26749758618354724,"score_spread":0.2413328099023549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W60686164","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":[4.989434e-7,0.000077473676,0.99556005,0.00018214315,0.0016926602,0.0011367871,0.000033001696,0.00026815684,0.0010492388],"genre_scores_gemma":[0.0007306621,0.000018582854,0.9971319,0.0009858292,0.00040547678,0.00006757867,0.00008409699,0.00006446243,0.0005114481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962039,0.00003538729,0.0006244569,0.0017579637,0.0007392892,0.0006390087],"domain_scores_gemma":[0.9970486,0.00019446465,0.0004914929,0.0010678452,0.0010118559,0.00018573357],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005247568,0.00060969905,0.0005015853,0.0008785846,0.00036539658,0.00042168685,0.0019423526,0.00040922186,0.000038409547],"category_scores_gemma":[0.00008723113,0.00062769785,0.00017450111,0.0004837822,0.0004998371,0.0011227698,0.00037803524,0.00043550166,0.0000112830385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055892897,0.000020043226,2.8399987e-7,0.000010347638,0.0000054358834,0.000005591134,0.00012373262,0.68046176,0.000032935524,0.009221246,0.0000039936604,0.31010905],"study_design_scores_gemma":[0.00067022763,0.00014490543,0.0000011072719,0.00007892436,0.00001153921,0.000012058016,3.9388524e-8,0.92646396,0.0034110006,0.06842327,0.00013190201,0.00065104116],"about_ca_topic_score_codex":0.000009203582,"about_ca_topic_score_gemma":0.00000946371,"teacher_disagreement_score":0.309458,"about_ca_system_score_codex":0.0005223036,"about_ca_system_score_gemma":0.0010727731,"threshold_uncertainty_score":0.99961746},"labels":[],"label_agreement":null},{"id":"W626466576","doi":"10.1016/j.eswa.2015.05.036","title":"A combined interactive procedure using preference-based evolutionary multiobjective optimization. Application to the efficiency improvement of the auxiliary services of power plants","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Multi-Objective Optimization Algorithms","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":"Polytechnique Montréal","funders":"","keywords":"Multi-objective optimization; Computer science; Mathematical optimization; Evolutionary algorithm; Profitability index; Set (abstract data type); Pareto principle; Preference; Decision maker; Optimization problem; Operations research; Mathematics; Artificial intelligence; Machine learning; Economics","score_opus":0.01707482874664799,"score_gpt":0.2587027076591576,"score_spread":0.2416278789125096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W626466576","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":[0.0022869604,0.00014167512,0.99211913,0.00030081643,0.00013530199,0.004712703,0.000053471154,0.000078464516,0.00017145943],"genre_scores_gemma":[0.85959417,0.000002000246,0.13727336,0.00012505123,0.000029803401,0.0029095057,0.000018099287,0.000018576311,0.000029422366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981698,0.00010509495,0.00047183098,0.0005001073,0.0005542428,0.00019892494],"domain_scores_gemma":[0.9968996,0.00012323058,0.0006731128,0.0010103207,0.0011887659,0.00010495902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019772767,0.0002120111,0.00023635304,0.0001388499,0.00026454884,0.000041131792,0.0010635109,0.00006340286,0.0000013078865],"category_scores_gemma":[0.000035761142,0.00013095718,0.000047289643,0.001181134,0.00011292121,0.00035277527,0.00021454022,0.00011286135,0.0000038139426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006262075,0.00033106792,0.00033247517,0.000031880907,0.000031630647,7.7346385e-8,0.0042070346,0.99178267,0.0019199711,0.0010018072,0.000035456673,0.00026333318],"study_design_scores_gemma":[0.0007503457,0.00017606211,0.0006425513,0.00013493607,0.00000960003,0.0000059173663,0.002794921,0.98964137,0.005427123,0.00004371704,0.00020592903,0.00016754307],"about_ca_topic_score_codex":0.00032294515,"about_ca_topic_score_gemma":0.000016988122,"teacher_disagreement_score":0.85730726,"about_ca_system_score_codex":0.00031012658,"about_ca_system_score_gemma":0.00043543035,"threshold_uncertainty_score":0.5340279},"labels":[],"label_agreement":null},{"id":"W6893880472","doi":"10.5281/zenodo.6521193","title":"Deep Reinforcement Learning for Optimal Experimental Design in Biology","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"European Commission","keywords":"Python (programming language); Reinforcement learning; Reinforcement; Directory; Learning classifier system; Optimal design","score_opus":0.039867878897461885,"score_gpt":0.28847799848019645,"score_spread":0.24861011958273457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893880472","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":[0.00031098505,0.00006390528,0.9947642,0.00016812536,0.00011254535,0.0007043707,0.0000049987534,0.0004530567,0.0034177888],"genre_scores_gemma":[0.77573276,0.000021816171,0.221732,0.00023731646,0.00006715594,0.0000029690075,0.0006684281,0.00079230807,0.00074523623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981279,0.00052184367,0.0002455151,0.00049153675,0.00023026702,0.00038294846],"domain_scores_gemma":[0.9992271,0.000058111385,0.00011614908,0.00029779712,0.00021092495,0.000089903275],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007345211,0.00012524713,0.00012762843,0.00030861903,0.002236793,0.0002634952,0.0012360929,0.000029441751,0.0022596403],"category_scores_gemma":[0.00033036392,0.00015062699,0.000039587303,0.0006442178,0.000067549394,0.00036280917,0.002188375,0.00025950733,0.00024074507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067113346,0.00012309072,0.0000013111369,0.0000049533123,0.000011980411,0.0000067024284,0.002271116,0.95012003,0.0035395406,0.011288854,0.001099992,0.031465292],"study_design_scores_gemma":[0.0010513476,0.00077750726,0.00001505624,0.000002907343,0.0000015279747,0.000044696117,0.00063965266,0.79419905,0.0016810612,0.00014922293,0.20126748,0.0001705137],"about_ca_topic_score_codex":0.0000045274273,"about_ca_topic_score_gemma":3.7252036e-8,"teacher_disagreement_score":0.7754218,"about_ca_system_score_codex":0.00049445993,"about_ca_system_score_gemma":0.000006852224,"threshold_uncertainty_score":0.9990622},"labels":[],"label_agreement":null},{"id":"W6906506499","doi":"10.17863/cam.22360","title":"Rejoinder","year":2016,"lang":"en","type":"article","venue":"Apollo (University of Cambridge)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Office of Science; Advanced Scientific Computing Research; U.S. Department of Energy","keywords":"Implementation; Variation (astronomy); Statistical model; Empirical research; Statistical analysis","score_opus":0.007116720672522968,"score_gpt":0.18367624383046538,"score_spread":0.1765595231579424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6906506499","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":[0.004034553,0.000020247813,0.9859315,0.0016902273,0.00014518919,0.000081071004,0.000006823904,0.00011872916,0.007971657],"genre_scores_gemma":[0.5479152,0.00006580357,0.42896566,0.0001403711,0.00002645387,1.8609464e-7,0.0000012189431,0.000010372075,0.02287468],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999241,0.000036195474,0.0000746851,0.00027809315,0.00019299478,0.00017704257],"domain_scores_gemma":[0.9991639,0.00006980181,0.00010603169,0.00041923346,0.00015445257,0.000086554894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008478743,0.000086279426,0.00013241818,0.000121728786,0.00010564035,0.000008796133,0.00058868463,0.000045782403,0.000044031745],"category_scores_gemma":[0.00003126011,0.00008135594,0.00007096454,0.00031200083,0.00012489386,0.00083321147,0.00024237852,0.000043010696,0.00013558172],"study_design_candidate":"design_other","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":[0.000094892515,0.00033413025,0.0041976613,0.000033453136,0.00019945331,0.00022223276,0.0021957993,0.00077104016,0.027072618,0.45246568,0.02193094,0.4904821],"study_design_scores_gemma":[0.030093044,0.0012510628,0.3152655,0.0005799499,0.00016337966,0.00033146862,0.0035338686,0.20946595,0.05549663,0.008972717,0.37010902,0.004737403],"about_ca_topic_score_codex":0.000050965446,"about_ca_topic_score_gemma":0.0000080669,"teacher_disagreement_score":0.5569658,"about_ca_system_score_codex":0.00006945001,"about_ca_system_score_gemma":0.000060970586,"threshold_uncertainty_score":0.3317599},"labels":[],"label_agreement":null},{"id":"W6925642956","doi":"10.17895/ices.pub.19936694","title":"Report of the Working Group on Oceanic Hydrography (WGOH)","year":2002,"lang":"en","type":"report","venue":"International Council for the Exploration of the Sea (ICES)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Hydrography; Group (periodic table); Buoy; Indian ocean","score_opus":0.1896767795538437,"score_gpt":0.30695874407967555,"score_spread":0.11728196452583184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6925642956","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":[0.0001327283,0.000856925,0.9300013,0.0057563465,0.020959143,0.0025385332,0.00021592533,0.00014776614,0.039391298],"genre_scores_gemma":[0.88161266,0.004164545,0.058020502,0.0016642984,0.0049145017,0.0014297479,0.00034478054,0.00038412807,0.04746482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9928137,0.00016084762,0.0011093616,0.0006508907,0.005014413,0.00025075517],"domain_scores_gemma":[0.98929197,0.0007411824,0.0030938892,0.0017626674,0.0050675073,0.00004276479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026502616,0.0003803374,0.00039180747,0.00017333386,0.00039970662,0.00014548005,0.0039135097,0.00019358564,0.000025711912],"category_scores_gemma":[0.0023373354,0.00022099665,0.0007661363,0.0008595829,0.00024227469,0.00077421847,0.00059649325,0.00047487693,0.000006697819],"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":[0.00048797688,0.0021399755,0.0022999973,0.0004935786,0.004927078,0.00004471303,0.0064921025,0.67239857,0.00058443344,0.08681988,0.13553244,0.087779276],"study_design_scores_gemma":[0.0012857224,0.00020913694,0.0011907336,0.0011140197,0.00022398414,0.00017076533,0.00016496467,0.3303833,0.002185574,0.01675005,0.64558506,0.0007366758],"about_ca_topic_score_codex":0.00007924224,"about_ca_topic_score_gemma":0.000092288625,"teacher_disagreement_score":0.8814799,"about_ca_system_score_codex":0.0012842094,"about_ca_system_score_gemma":0.0009288412,"threshold_uncertainty_score":0.9011982},"labels":[],"label_agreement":null},{"id":"W6931264233","doi":"10.5281/zenodo.3596164","title":"Sound Comparisons: Romance","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Romance; Pronunciation; Romance languages; Focus (optics); Portuguese; Diversity (politics); Resource (disambiguation)","score_opus":0.030349878604876524,"score_gpt":0.25715795744363296,"score_spread":0.22680807883875642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931264233","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":[0.0021491821,0.000062960906,0.9118835,0.0003883259,0.00022505851,0.00043054382,0.000023215798,0.0013138375,0.083523385],"genre_scores_gemma":[0.8222265,0.00007610657,0.17040633,0.00064800377,0.00017162501,1.2340334e-7,0.0004853841,0.0016817602,0.004304148],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982411,0.00020443217,0.00022552501,0.0005757391,0.0003843618,0.00036883628],"domain_scores_gemma":[0.9983336,0.000034596644,0.00012682643,0.0007986009,0.0005586628,0.00014776691],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000356863,0.00014719754,0.00015812887,0.00019299975,0.0011562286,0.00089452945,0.0018910286,0.000049495215,0.0032156496],"category_scores_gemma":[0.00023373353,0.00016109477,0.000045549827,0.00079344865,0.000091260474,0.00087701983,0.0015412415,0.00023523768,0.020625787],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118382966,0.0010493854,0.00015793063,0.00017310126,0.0001903662,0.00006283521,0.005700195,0.049243893,0.0074380324,0.3451142,0.1805794,0.41017228],"study_design_scores_gemma":[0.00078644964,0.00017280129,0.0010457474,0.00001959444,0.000003398339,0.00010092492,0.00013865763,0.09940071,0.0003673922,0.0010364101,0.89664286,0.00028505272],"about_ca_topic_score_codex":0.0000035875164,"about_ca_topic_score_gemma":1.024048e-7,"teacher_disagreement_score":0.82007736,"about_ca_system_score_codex":0.00015956284,"about_ca_system_score_gemma":0.000004059969,"threshold_uncertainty_score":0.99769557},"labels":[],"label_agreement":null},{"id":"W6931389591","doi":"10.5281/zenodo.5248501","title":"Eudendrium arbuscula Wright 1859","year":2012,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Wright; Specific name; Index (typography); Species name; Correct name","score_opus":0.028199456488809227,"score_gpt":0.2549779151213515,"score_spread":0.22677845863254226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931389591","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":[0.00050243497,0.00013693578,0.9130831,0.00048465832,0.0003493893,0.0003153624,0.000025952239,0.001488733,0.08361348],"genre_scores_gemma":[0.70016366,0.00024560446,0.28559738,0.001211552,0.0011683811,3.2519307e-7,0.001028174,0.0035995431,0.0069853812],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99793977,0.00030224991,0.0002427217,0.00045607306,0.00046237567,0.0005968123],"domain_scores_gemma":[0.9981614,0.000030342577,0.000119718694,0.0007604422,0.0006023581,0.00032577288],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005964005,0.0001743271,0.0001456726,0.00025310664,0.0017496442,0.00074807525,0.0017519587,0.000064093154,0.0033665684],"category_scores_gemma":[0.0004125842,0.00017978266,0.0000620662,0.00093027984,0.00012504299,0.0014951385,0.0017174998,0.00025461378,0.0114945695],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052834162,0.0012570398,0.00004581802,0.00008807871,0.0001620575,0.000049752074,0.008932754,0.004547963,0.007270052,0.2213779,0.12942547,0.6267903],"study_design_scores_gemma":[0.00048428637,0.00007385026,0.0009241034,0.000011640981,0.0000060723387,0.00018025766,0.000083709856,0.012134414,0.001321142,0.00027093844,0.98423606,0.00027353832],"about_ca_topic_score_codex":0.0000033551762,"about_ca_topic_score_gemma":4.7994817e-8,"teacher_disagreement_score":0.8548106,"about_ca_system_score_codex":0.0001930446,"about_ca_system_score_gemma":0.0000040283567,"threshold_uncertainty_score":0.9995499},"labels":[],"label_agreement":null},{"id":"W6931432950","doi":"10.5281/zenodo.5576033","title":"Simple layers for species distribution modeling and bioclimatic data","year":2021,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Liquation; Diafiltration; Emperipolesis; Triacetin; Durvalumab","score_opus":0.08000480673845832,"score_gpt":0.2904689596343653,"score_spread":0.21046415289590698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931432950","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":[0.000002812467,0.0004869101,0.9570118,0.00018540274,0.0001183655,0.00056602515,0.002082161,0.00078218326,0.038764294],"genre_scores_gemma":[0.004762879,0.00644675,0.58423865,0.0005167489,0.0015260668,0.0000011561553,0.24206285,0.020190608,0.1402543],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981496,0.00015226356,0.00023335355,0.0008427901,0.0002942408,0.0003277978],"domain_scores_gemma":[0.99813163,0.000035128476,0.00016841566,0.0011333969,0.0003991488,0.00013228355],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003593145,0.00020949355,0.00022810267,0.0002032145,0.00095197477,0.0007533414,0.0017556625,0.00010571518,0.0025870386],"category_scores_gemma":[0.000804873,0.00023194055,0.00003559477,0.0004994918,0.00009895168,0.00032725828,0.003327662,0.0001757846,0.00021953303],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013743716,0.00013464566,8.1984e-7,0.00038574307,0.00012397082,0.000012813325,0.00030871257,0.004421936,0.00031045976,0.011517689,0.909364,0.07340546],"study_design_scores_gemma":[0.00025158434,0.000025819987,0.0000035751082,0.000048964437,0.000008872653,0.000017534996,0.00006249782,0.3981796,0.000014760228,0.00017431592,0.6010493,0.00016319571],"about_ca_topic_score_codex":0.000011804402,"about_ca_topic_score_gemma":8.9081936e-7,"teacher_disagreement_score":0.39375767,"about_ca_system_score_codex":0.00011892944,"about_ca_system_score_gemma":0.000008367372,"threshold_uncertainty_score":0.99832475},"labels":[],"label_agreement":null},{"id":"W6931664732","doi":"10.5281/zenodo.5784787","title":"Assessing and assuring interoperability of a genomics file format: results and scripts","year":2022,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Interoperability; Scripting language; File format; Test (biology); Genomics","score_opus":0.03452343956077849,"score_gpt":0.26027700939468384,"score_spread":0.22575356983390535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931664732","genre_codex":"other","genre_gemma":"methods","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":[0.00023651916,0.0004729457,0.31183967,0.00028475717,0.00023201677,0.0009588591,0.0021633855,0.0014309497,0.6823809],"genre_scores_gemma":[0.039478872,0.003032168,0.7489373,0.0007552709,0.00064175436,0.0000015908215,0.013021462,0.025063299,0.16906828],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982978,0.00031074585,0.00030064132,0.00058933423,0.00027028623,0.00023120258],"domain_scores_gemma":[0.9987506,0.000041729745,0.0003038886,0.0005693682,0.00022065736,0.0001137346],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005304018,0.00017701395,0.00022934136,0.00033884327,0.0008018855,0.00072991924,0.0008804582,0.00007191941,0.008865653],"category_scores_gemma":[0.00062210776,0.00019459406,0.000028247463,0.0003514544,0.00018342714,0.00045071723,0.003682508,0.00029843123,0.000115419876],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000516883,0.00020291992,0.0000027630615,0.0003837292,0.00011153521,0.000022939896,0.0042739254,0.0004001855,0.0005168941,0.0016391035,0.5408012,0.45159313],"study_design_scores_gemma":[0.00053726917,0.00010176123,0.00015028592,0.00006358379,0.0000062056993,0.000073793235,0.00025583513,0.009484511,0.000038899816,0.000045745688,0.9890487,0.00019339382],"about_ca_topic_score_codex":0.000023002965,"about_ca_topic_score_gemma":5.281611e-7,"teacher_disagreement_score":0.51331264,"about_ca_system_score_codex":0.00017001946,"about_ca_system_score_gemma":0.0000066824496,"threshold_uncertainty_score":0.9920404},"labels":[],"label_agreement":null},{"id":"W6931913177","doi":"10.5683/sp3/1jf2kt","title":"Aboriginal Peoples Survey, 2006 [Canada]: Adults","year":2009,"lang":"en","type":"dataset","venue":"Borealis","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"General partnership; Product (mathematics); Population; Indigenous; Ethnic group","score_opus":0.007760170416241743,"score_gpt":0.2614338810864885,"score_spread":0.2536737106702468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931913177","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.401851e-7,0.00017309186,0.10977128,0.00017136276,0.00045290412,0.00026942804,0.88881487,0.0001105849,0.00023606492],"genre_scores_gemma":[0.0000015526413,0.00087007915,0.033687443,0.00072926184,0.00023656877,0.000027006274,0.964258,0.000021643911,0.0001684457],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99696743,0.00021880456,0.00045555647,0.00094925216,0.00084433873,0.00056458387],"domain_scores_gemma":[0.996827,0.0003113823,0.00040458003,0.0016573706,0.00054125156,0.00025843104],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002683755,0.00050902687,0.00050521136,0.00020739854,0.00017358108,0.00019096678,0.0021565214,0.00023850976,0.000034814006],"category_scores_gemma":[0.00025253405,0.00051060703,0.00008128125,0.0006413401,0.00005535773,0.00038550876,0.00012591043,0.00040661838,0.00001837033],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012623817,0.00006180264,0.00001266191,0.000021191197,0.000018636076,0.000102084305,0.00002181327,0.00065945025,4.130619e-8,0.000055212644,0.99520963,0.003824821],"study_design_scores_gemma":[0.00040719355,0.000055626417,0.00860841,0.00006556448,0.000012166954,0.000025107156,0.000005171435,0.0023208272,0.0000062453487,0.000036032885,0.9878715,0.00058614725],"about_ca_topic_score_codex":0.9437959,"about_ca_topic_score_gemma":0.99236816,"teacher_disagreement_score":0.07608384,"about_ca_system_score_codex":0.00038637337,"about_ca_system_score_gemma":0.0023188903,"threshold_uncertainty_score":0.9997346},"labels":[],"label_agreement":null},{"id":"W6957698311","doi":"10.60527/75xq-p143","title":"Ma thèse en 180 secondes, finale canadienne - Édition 2016Maud Gratuze, Université Laval (Québec) - Troisième prix","year":2016,"lang":"fr","type":"other","venue":"Fondation Maison des sciences de l'homme","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Work (physics); Doors; Section (typography)","score_opus":0.027856192350369458,"score_gpt":0.27270072643363513,"score_spread":0.24484453408326567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6957698311","genre_codex":"methods","genre_gemma":"other","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":[0.0019268541,0.0010428864,0.8527394,0.0048660953,0.0018002363,0.0008784092,0.00036165232,0.00032640988,0.13605806],"genre_scores_gemma":[0.007472114,0.0008719697,0.20019501,0.0005567753,0.00066430354,0.000060459683,0.00010991862,0.00015416562,0.78991526],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99460244,0.0006723218,0.000686751,0.0017313845,0.00074851094,0.0015585896],"domain_scores_gemma":[0.9964653,0.0005041955,0.0009597176,0.0008348481,0.0005687043,0.00066718186],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0011094264,0.00079541217,0.00062601216,0.000982497,0.001695265,0.00067773584,0.0023024902,0.00050451426,0.034091987],"category_scores_gemma":[0.00043620574,0.00075736316,0.00023386053,0.001619002,0.0019801739,0.0031494496,0.0005199321,0.0005001511,0.0016459359],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019272359,0.0009690146,0.0045887977,0.00041309258,0.0003425584,0.00047970502,0.02159123,0.035929427,0.003923173,0.38252863,0.2365206,0.31252104],"study_design_scores_gemma":[0.0019751603,0.00070023746,0.007086465,0.000554583,0.00009591034,0.00034504902,0.00068659545,0.2354413,0.00082811917,0.017126739,0.7335114,0.0016484637],"about_ca_topic_score_codex":0.02587796,"about_ca_topic_score_gemma":0.12399762,"teacher_disagreement_score":0.65385723,"about_ca_system_score_codex":0.004658869,"about_ca_system_score_gemma":0.003474341,"threshold_uncertainty_score":0.9996044},"labels":[],"label_agreement":null},{"id":"W6981641240","doi":"","title":"Evaluating approaches to EAL newcomer support: protocol for a systematic review.","year":2022,"lang":"en","type":"dissertation","venue":"Oxford University Research Archive (ORA) (University of Oxford)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mainstream; Variety (cybernetics); Inclusion (mineral); Trustworthiness; Protocol (science); Qualitative research; Mainstreaming; Narrative","score_opus":0.14197026140373678,"score_gpt":0.379817873403489,"score_spread":0.2378476119997522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6981641240","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":[0.00008418421,0.00004239287,0.7378112,0.00071455206,0.00013876175,0.23777512,0.0005189395,0.00022004337,0.022694813],"genre_scores_gemma":[0.00014365774,0.00037741597,0.89652884,0.00014688639,0.000065947555,0.009041792,0.0022345486,0.00016301233,0.09129789],"study_design_codex":"systematic_review","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9931859,0.0015333613,0.0005240376,0.001552124,0.002133205,0.0010713966],"domain_scores_gemma":[0.99470246,0.0009988203,0.0009571453,0.0015081591,0.001283061,0.0005503382],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002716532,0.00054699363,0.0013361069,0.0020556722,0.0019284158,0.000091627124,0.0050112996,0.0002007213,0.00037868094],"category_scores_gemma":[0.0007691583,0.0006931433,0.0005937495,0.002686527,0.00024398358,0.0011334766,0.0021516047,0.0011547388,0.000017000353],"study_design_candidate":"systematic_review","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":[0.009211866,0.0031406956,0.00017720375,0.73209023,0.0025256127,0.0008931582,0.05894322,0.018893879,0.00048755124,0.10722053,0.032294415,0.034121603],"study_design_scores_gemma":[0.018684693,0.012849104,0.0007203006,0.08078343,0.0012898024,0.0000844474,0.1056093,0.4834836,0.00020266301,0.00870483,0.28206235,0.0055254726],"about_ca_topic_score_codex":0.00016462822,"about_ca_topic_score_gemma":0.0005223009,"teacher_disagreement_score":0.6513068,"about_ca_system_score_codex":0.0009943192,"about_ca_system_score_gemma":0.0016646434,"threshold_uncertainty_score":0.99955195},"labels":[],"label_agreement":null},{"id":"W6990440976","doi":"","title":"Designing Radiotherapy Plans with Elastic Constraints and Interior Point Methods","year":2003,"lang":"en","type":"article","venue":"Digital Commons - Trinity University (Trinity University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Point (geometry); Interior point method; Path (computing); Plan (archaeology); Linear programming; Planner; Mathematical model","score_opus":0.0187459869925336,"score_gpt":0.24051442660506625,"score_spread":0.22176843961253265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6990440976","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":[0.01100472,0.000014995098,0.95216423,0.00010556229,0.00009271184,0.00036243413,0.000082717794,0.00035275813,0.03581989],"genre_scores_gemma":[0.42746875,0.000051702067,0.57092446,0.000057005946,0.000007702312,2.503709e-7,0.000015099531,0.000023754623,0.0014512759],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977651,0.00057051994,0.00019006629,0.0007353402,0.00024576884,0.00049320405],"domain_scores_gemma":[0.99756694,0.00093106553,0.00021735085,0.00058701006,0.0002626283,0.00043500046],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028937554,0.00038630192,0.00045929078,0.00084183435,0.0006613638,0.0002691615,0.0009190491,0.0001433086,0.000030504938],"category_scores_gemma":[0.00018078045,0.00044638675,0.00012152523,0.0016141456,0.00076881726,0.002760459,0.0004177646,0.0004900504,0.000009408206],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029355576,0.0029337038,0.056278642,0.0002165334,0.0024214035,0.008826562,0.01151636,0.01223483,0.0019496693,0.7331932,0.0002791346,0.1672144],"study_design_scores_gemma":[0.116326556,0.013006423,0.035621915,0.0013950283,0.0014201206,0.0070268176,0.08855856,0.11397057,0.024944184,0.008116363,0.57109433,0.018519139],"about_ca_topic_score_codex":0.00002874216,"about_ca_topic_score_gemma":0.00006069112,"teacher_disagreement_score":0.72507685,"about_ca_system_score_codex":0.0004906239,"about_ca_system_score_gemma":0.00028436782,"threshold_uncertainty_score":0.9997988},"labels":[],"label_agreement":null},{"id":"W7020499438","doi":"","title":"L’intégration linguistique et sociale à la langue française des étudiants internationaux d’origine iranienne aux cycles supérieurs dans le contexte de Montréal","year":2022,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ethnography; Sociolinguistics; Social worlds","score_opus":0.008202413356822029,"score_gpt":0.2312472905507475,"score_spread":0.22304487719392546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7020499438","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":[0.45812935,0.013167903,0.50109816,0.0012274616,0.0028059976,0.0010215791,0.00048580125,0.00050746964,0.021556297],"genre_scores_gemma":[0.9314365,0.0019835313,0.038140025,0.0001805868,0.00031150138,0.00014822275,0.0012565841,0.00010294529,0.026440125],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959493,0.0007374158,0.0006578952,0.0010900426,0.00089125993,0.0006740641],"domain_scores_gemma":[0.9967256,0.00041016756,0.0007355934,0.0004986609,0.0012152337,0.00041473005],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005758168,0.0006813401,0.0005676739,0.000460965,0.010695764,0.0002389972,0.0010277398,0.00046252288,0.00006936552],"category_scores_gemma":[0.00058219477,0.00089179195,0.0003865181,0.0007846348,0.00071954064,0.001703213,0.00044203614,0.0009138497,0.000028897462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007634846,0.0013826872,0.017669883,0.00019340051,0.0006761375,0.005719398,0.21238537,0.3597055,0.012231355,0.37241358,0.00059281616,0.01626639],"study_design_scores_gemma":[0.0074885725,0.00048121653,0.27213863,0.00078685285,0.00049804104,0.0031077752,0.13844971,0.47888723,0.014217698,0.0047501475,0.07611291,0.0030812125],"about_ca_topic_score_codex":0.24502401,"about_ca_topic_score_gemma":0.17677446,"teacher_disagreement_score":0.47330713,"about_ca_system_score_codex":0.0077062333,"about_ca_system_score_gemma":0.0050303726,"threshold_uncertainty_score":0.9993533},"labels":[],"label_agreement":null},{"id":"W7023810788","doi":"","title":"Proceedings of the First International Workshop on Multidisciplinary Design Optimisation","year":2001,"lang":"en","type":"other","venue":"NPARC","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multidisciplinary approach; Work (physics); Key (lock); Field (mathematics)","score_opus":0.02243639356204879,"score_gpt":0.2676957113606331,"score_spread":0.2452593177985843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7023810788","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":[0.0000016536096,0.000011063739,0.64023817,0.0007783762,0.00059664756,0.0004062164,0.000004897312,0.00013699506,0.357826],"genre_scores_gemma":[0.0003096799,0.00009019758,0.73292255,0.000111515095,0.00021563034,0.00006603657,0.000004209837,0.00013445699,0.26614574],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986972,0.000015041835,0.00021264408,0.00045125952,0.0004558718,0.00016797877],"domain_scores_gemma":[0.99897075,0.000080571015,0.00042254088,0.00032389,0.00016177948,0.000040466344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011797153,0.00022236665,0.00018076244,0.00023451225,0.00008200082,0.00005600985,0.0012936133,0.00017025674,0.00051636744],"category_scores_gemma":[0.00010416166,0.00017218915,0.00008031731,0.00043503178,0.00008464477,0.00018005248,0.00036164842,0.00021144372,0.000033681423],"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":[0.00008783471,0.0010355995,0.0006672258,0.000116002186,0.00030436288,0.000015041688,0.002356784,0.15017456,0.0011909468,0.03682887,0.70644134,0.10078145],"study_design_scores_gemma":[0.001574594,0.000114596805,0.000674498,0.0014044783,0.000026641399,0.000025012076,0.00013840238,0.82233524,0.0029290104,0.004927879,0.16500382,0.00084581686],"about_ca_topic_score_codex":0.000003558955,"about_ca_topic_score_gemma":0.0000028453092,"teacher_disagreement_score":0.6721607,"about_ca_system_score_codex":0.0001327011,"about_ca_system_score_gemma":0.000043773223,"threshold_uncertainty_score":0.702167},"labels":[],"label_agreement":null},{"id":"W7033793758","doi":"","title":"Search for a new Z' gauge boson via the pp → W ð Þ → Z0μ ν → μ μ∓μν process in pp collisions at √s p =13TeV with the ATLAS detector","year":2024,"lang":"en","type":"article","venue":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; CERN; Bundesministerium für Wissenschaft, Forschung und Wirtschaft; Institut National de Physique Nucléaire et de Physique des Particules; Austrian Science Fund; Agencia Nacional de Promoción Científica y Tecnológica; Fundação de Amparo à Pesquisa do Estado de São Paulo; TRIUMF","keywords":"Atlas (anatomy); Atlas detector; Large Hadron Collider; Branching fraction; Gauge boson; Detector; ATLAS experiment; Hadron; Boson","score_opus":0.03304694388255139,"score_gpt":0.2815733238931616,"score_spread":0.2485263800106102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7033793758","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":[0.29501975,0.00077852246,0.6718716,0.028289404,0.00010625014,0.002288884,0.00032130934,0.00014143827,0.0011828543],"genre_scores_gemma":[0.926562,0.002230891,0.056059264,0.00004272308,0.00007753933,2.428928e-7,0.000060062226,0.00004411638,0.014923151],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99688554,0.00050284516,0.00016986548,0.0007072341,0.0010966654,0.0006378551],"domain_scores_gemma":[0.9959847,0.0015842171,0.0002040019,0.0009697038,0.0010270999,0.00023029473],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0012181136,0.00026492527,0.00044744695,0.00053388794,0.0016043908,0.00004701402,0.0038356083,0.0002223231,0.00017444816],"category_scores_gemma":[0.00006291923,0.00022133565,0.00027850227,0.0031157532,0.001955144,0.00078857853,0.0016713343,0.0009994674,0.000016541037],"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":[0.013248006,0.0037432255,0.009869204,0.0027006676,0.0028252336,0.0012662031,0.4428831,0.3433709,0.0106470585,0.028589608,0.05239676,0.08846003],"study_design_scores_gemma":[0.0075293113,0.0020535048,0.0066745663,0.0010621377,0.00030200573,0.00005177956,0.21018893,0.70494145,0.0018065876,0.0006329706,0.063840374,0.00091637636],"about_ca_topic_score_codex":0.004609717,"about_ca_topic_score_gemma":0.010530142,"teacher_disagreement_score":0.63154227,"about_ca_system_score_codex":0.00044848653,"about_ca_system_score_gemma":0.0009733317,"threshold_uncertainty_score":0.99969536},"labels":[],"label_agreement":null},{"id":"W7039156554","doi":"","title":"Life cycle cost of support poles in distribution lines","year":2004,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Distribution (mathematics); Class (philosophy); Selection (genetic algorithm); Production (economics); Service life; Line (geometry)","score_opus":0.012981845667449718,"score_gpt":0.26298490565358174,"score_spread":0.25000305998613204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039156554","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":[0.85115546,0.0015667087,0.024546504,0.00040374347,0.014156861,0.010807674,0.017482523,0.0033053458,0.07657516],"genre_scores_gemma":[0.95946825,0.00033007844,0.033980355,0.00022180207,0.000054314005,0.00022373699,0.004059721,0.0001501843,0.0015115752],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99552584,0.00023411098,0.0013194601,0.0012970067,0.000902199,0.0007213637],"domain_scores_gemma":[0.9964383,0.00018746275,0.0010388836,0.0009822957,0.0009090706,0.00044395938],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005954212,0.000713991,0.0008795532,0.0005617258,0.0004143293,0.00009116679,0.0014052326,0.0006786602,0.00006889401],"category_scores_gemma":[0.0020249712,0.00081138714,0.00028314308,0.0016232136,0.00008641001,0.0017796799,0.00025892004,0.001067642,0.00011061174],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037706067,0.002154696,0.0002385842,0.00092667225,0.00028769043,0.00028014672,0.000115837676,0.09652291,0.015468761,0.41247785,0.000012506054,0.4711373],"study_design_scores_gemma":[0.021105362,0.0017928951,0.043001644,0.0053804456,0.0004907167,0.00022354441,0.0018192917,0.0594634,0.6282151,0.18376213,0.042894885,0.011850621],"about_ca_topic_score_codex":0.00029342598,"about_ca_topic_score_gemma":0.00091279804,"teacher_disagreement_score":0.6127463,"about_ca_system_score_codex":0.0011967623,"about_ca_system_score_gemma":0.00039882472,"threshold_uncertainty_score":0.9994337},"labels":[],"label_agreement":null},{"id":"W7039984523","doi":"","title":"Numerical methods to solve stochastic differential equations","year":2021,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Numerical analysis; Discretization; Stochastic differential equation; Numerical stability; Differential equation; Numerical partial differential equations; Point (geometry); Stochastic partial differential equation","score_opus":0.023832901751491244,"score_gpt":0.31863410796160707,"score_spread":0.29480120621011585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039984523","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":[0.001297108,0.00016239933,0.98342013,0.000041265932,0.0049090907,0.0011458212,0.0003077089,0.00071170815,0.008004735],"genre_scores_gemma":[0.054472025,0.000022734606,0.93566257,0.00045215565,0.00013035293,0.00062117976,0.0010800726,0.00026675808,0.0072921696],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934984,0.0006857622,0.0011638128,0.0023645158,0.0012321494,0.0010553735],"domain_scores_gemma":[0.9943257,0.001099662,0.00066639896,0.0016994199,0.0014068014,0.00080204156],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005962896,0.0010450901,0.0011144927,0.00078380323,0.0014057246,0.0004018873,0.0020231022,0.00070739177,0.00035853355],"category_scores_gemma":[0.0039919033,0.0011862445,0.0005230714,0.0023171748,0.00004594887,0.0013349648,0.00059453124,0.0017058892,0.00044512283],"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":[0.00008368101,0.0006565018,4.4702583e-7,0.00011902758,0.00038485142,0.00010037839,0.00010324103,0.056232058,0.025347846,0.28146031,0.0000074426716,0.6355042],"study_design_scores_gemma":[0.0065233107,0.0012733252,0.0011240509,0.0019112331,0.0011030885,0.00022573025,0.0012692258,0.59855217,0.24887805,0.10221057,0.024273213,0.0126560535],"about_ca_topic_score_codex":0.0000807938,"about_ca_topic_score_gemma":0.00013881816,"teacher_disagreement_score":0.62284815,"about_ca_system_score_codex":0.0010443653,"about_ca_system_score_gemma":0.00021072947,"threshold_uncertainty_score":0.9998943},"labels":[],"label_agreement":null},{"id":"W7067275600","doi":"","title":"Modeling and Analysis of Dynamic Computer Experiments","year":2018,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Nucleofection; Process (computing); TSG101; Frame (networking); Tubulopathy; Articular cartilage damage","score_opus":0.0068488552941887194,"score_gpt":0.22512261067413306,"score_spread":0.21827375537994434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7067275600","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":[0.024523336,0.000031401367,0.9731108,0.00043734183,0.00028514932,0.00022257322,0.00003682259,0.00022122385,0.0011313338],"genre_scores_gemma":[0.050345957,0.0003174441,0.91209257,0.000057431025,0.0000296124,0.0000011689368,0.00082474836,0.000046058758,0.036284987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984221,0.00009295722,0.00020531718,0.00075572374,0.0002852814,0.00023862127],"domain_scores_gemma":[0.99880165,0.00004269674,0.0002765406,0.00056324946,0.0001777692,0.0001381116],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00003056462,0.00030662035,0.00051723217,0.0012589146,0.00016200509,0.00008844159,0.0007732469,0.00021905529,0.00005573041],"category_scores_gemma":[0.0000064071173,0.00036688586,0.00018004338,0.0016367307,0.000063876934,0.0019316287,0.00037820556,0.00018262363,0.000005859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011661468,0.001795844,0.0050564515,0.0009661527,0.017063862,0.0006035908,0.056303997,0.81108,0.00020791538,0.029565662,0.013434217,0.06275616],"study_design_scores_gemma":[0.0003708029,0.00007350663,0.0016471405,0.0000644309,0.00028191519,1.1664173e-7,0.0006516732,0.99547666,0.00048525815,0.000098590506,0.0004365851,0.00041331435],"about_ca_topic_score_codex":0.00038778677,"about_ca_topic_score_gemma":0.000030159212,"teacher_disagreement_score":0.18439667,"about_ca_system_score_codex":0.000089630645,"about_ca_system_score_gemma":0.000121658384,"threshold_uncertainty_score":0.9998783},"labels":[],"label_agreement":null},{"id":"W7084067593","doi":"10.6084/m9.figshare.c.8053060.v1","title":"The intersections of palliative care and homelessness in social policy: A content analysis of Canadian policy documents","year":2025,"lang":"en","type":"other","venue":"Figshare","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université du Québec à Montréal; Providence Health Care; University of British Columbia; McGill University; Simon Fraser University","funders":"","keywords":"Palliative care; Content analysis; Ethnic group; Social policy; Social care; National Policy; Social work","score_opus":0.03859248159582912,"score_gpt":0.32451256951242435,"score_spread":0.28592008791659523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084067593","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014629207,0.0024692083,0.008943523,0.0009546881,0.00021164412,0.0018597298,0.6961225,0.00012770756,0.28929633],"genre_scores_gemma":[0.33355305,0.0033923413,0.03936766,0.0020585996,0.0022144131,0.004292911,0.1706756,0.0012003048,0.4432451],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990974,0.00006257729,0.00022316542,0.00027189593,0.0001453484,0.00019961757],"domain_scores_gemma":[0.99899524,0.000076717784,0.00031008213,0.00025646566,0.00029844994,0.00006303684],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000019983012,0.00015762815,0.00037271754,0.0037238945,0.00009011857,0.000039656068,0.00048314978,0.00012658046,0.00099925],"category_scores_gemma":[0.00039283212,0.00013783874,0.000102477024,0.0044584107,0.000029669558,0.00006107751,0.00021049505,0.00012284981,0.0000035016806],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006635825,0.0004012673,0.0059172222,0.0029960736,0.012774396,0.0000715175,0.13982558,0.013600699,0.00001772438,0.05864129,0.41414654,0.35154134],"study_design_scores_gemma":[0.0058420612,0.0003090915,0.25903988,0.012577581,0.00079686183,0.0000013850225,0.025777008,0.056307144,0.0003547249,0.00095518585,0.6351054,0.0029337194],"about_ca_topic_score_codex":0.2596619,"about_ca_topic_score_gemma":0.6596646,"teacher_disagreement_score":0.52544695,"about_ca_system_score_codex":0.0003903172,"about_ca_system_score_gemma":0.0009247956,"threshold_uncertainty_score":0.999914},"labels":[],"label_agreement":null},{"id":"W7097417421","doi":"","title":"Canada under Grants RGPIN7239-06 and STPGP336760-06.","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Space (punctuation); Space mapping; Convergence (economics); Quality (philosophy); Similarity (geometry)","score_opus":0.009277922983905167,"score_gpt":0.23396603230233945,"score_spread":0.22468810931843428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097417421","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":[0.0007025696,0.000106514155,0.99094594,0.0029672019,0.00012552546,0.00013256086,0.0000030252065,0.00013121209,0.0048854677],"genre_scores_gemma":[0.4734629,0.000045396984,0.5129068,0.008067542,0.000046351128,0.0000046081996,0.000005078068,0.00001121753,0.005450099],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891895,0.000028475337,0.0001525594,0.00037922023,0.000253959,0.00026681728],"domain_scores_gemma":[0.99931014,0.000053329797,0.00005127557,0.00032853396,0.00010795121,0.0001487973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006457992,0.00014025612,0.00013154521,0.00005827298,0.00013086355,0.00008339678,0.00032304565,0.000035686076,0.000022881039],"category_scores_gemma":[0.0000289806,0.00012591253,0.000016886306,0.00030753904,0.000027780157,0.0004911349,0.00008636059,0.00009021238,0.000014702493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021114438,0.0004516798,0.0026998124,0.0000230827,0.00012869573,0.0002578775,0.0008185601,0.12698473,0.0016360669,0.49039543,0.06967245,0.30691049],"study_design_scores_gemma":[0.0020627412,0.00014524399,0.10737382,0.00002318551,0.000011470452,0.00010714186,0.00014626925,0.84569055,0.0022244116,0.018382482,0.022844288,0.0009883823],"about_ca_topic_score_codex":0.026037501,"about_ca_topic_score_gemma":0.06010246,"teacher_disagreement_score":0.71870583,"about_ca_system_score_codex":0.00010762802,"about_ca_system_score_gemma":0.00027311203,"threshold_uncertainty_score":0.9804482},"labels":[],"label_agreement":null},{"id":"W7099290542","doi":"","title":"POLISH AIR TRANSPORT AMONG STRUCTURAL CHANGES IN THE","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Competitor analysis; Air transport; European market; Market share; European union","score_opus":0.008680661576299925,"score_gpt":0.23424207375317166,"score_spread":0.22556141217687173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099290542","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":[0.08891073,0.00001515952,0.8989539,0.0039817677,0.00022304151,0.0005392037,0.0000013362315,0.0001842534,0.007190609],"genre_scores_gemma":[0.8748597,0.0000036631031,0.12320161,0.0013036049,0.00003850368,0.000051260362,0.000002049169,0.000005459238,0.00053413224],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99924326,0.00003703764,0.00010817586,0.00022374485,0.00017406508,0.00021370087],"domain_scores_gemma":[0.9995059,0.000042240954,0.000035923753,0.00030929255,0.00006691001,0.000039759354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000078773184,0.00009622547,0.000083521234,0.00007483234,0.00006166281,0.000047670448,0.00065272104,0.00003236427,0.00011034702],"category_scores_gemma":[0.000011506198,0.00006068222,0.000022751938,0.00039806607,0.00005255424,0.0008159589,0.000039458817,0.00010160837,0.000025784899],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001101861,0.0003075076,0.2793958,0.00004698156,0.0000688695,0.00013311769,0.06402382,0.11761082,0.000912812,0.19146153,0.0032859973,0.34274173],"study_design_scores_gemma":[0.00027373954,0.000023217588,0.744139,0.000003879861,9.913714e-7,0.000008580433,0.00039592967,0.25195315,0.00047990168,0.002375442,0.00019340991,0.00015271883],"about_ca_topic_score_codex":0.000524867,"about_ca_topic_score_gemma":0.00077407795,"teacher_disagreement_score":0.785949,"about_ca_system_score_codex":0.000024739476,"about_ca_system_score_gemma":0.000013241487,"threshold_uncertainty_score":0.24745491},"labels":[],"label_agreement":null},{"id":"W7099895388","doi":"","title":"Perceptions of the Canadian criminal justice system among Nigerians: Evidence from a local Church","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Criminal justice; Perception; Immigration; Diversity (politics); Nigerians; Population; Economic Justice; Sample (material)","score_opus":0.03816751808403968,"score_gpt":0.25673067831076773,"score_spread":0.21856316022672806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099895388","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":[0.010703463,0.000027590944,0.9806621,0.00007172778,0.00048304873,0.00023561968,0.000009598607,0.00009027708,0.007716543],"genre_scores_gemma":[0.7475153,0.0000034477237,0.2519935,0.000096785676,0.000028138773,0.000018357758,6.149217e-7,0.000007411869,0.00033645786],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871224,0.00012454254,0.00025738147,0.00035923716,0.00028325923,0.0002633198],"domain_scores_gemma":[0.99852383,0.00010563861,0.00011044305,0.0007299487,0.0003320941,0.00019802134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001663527,0.00013039167,0.00014218439,0.00009934441,0.00033334797,0.000052216863,0.0011205778,0.00007584425,0.00018865343],"category_scores_gemma":[0.000120216406,0.00009767723,0.000065583634,0.0004550201,0.0002529321,0.0006867771,0.00017827595,0.0001504889,0.00005555984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000113663344,0.0008349842,0.14518005,0.0011399019,0.00042600877,0.0003146744,0.35117027,0.15142025,0.0060173785,0.25262913,0.0024443974,0.08830929],"study_design_scores_gemma":[0.00018546804,0.00004261412,0.33187827,0.00022970691,0.00009680751,0.000018881094,0.009602555,0.65621585,0.0013632638,0.000109460554,0.000016124268,0.00024099826],"about_ca_topic_score_codex":0.25018445,"about_ca_topic_score_gemma":0.18920752,"teacher_disagreement_score":0.7368118,"about_ca_system_score_codex":0.00035765927,"about_ca_system_score_gemma":0.0003921072,"threshold_uncertainty_score":0.82558733},"labels":[],"label_agreement":null},{"id":"W7100705169","doi":"","title":"In","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Inclusion (mineral); Context (archaeology); Confidentiality; Work (physics); Government (linguistics)","score_opus":0.005481206191174562,"score_gpt":0.2414179862226636,"score_spread":0.23593678003148905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100705169","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":[0.00007844688,0.0000022627264,0.9620478,0.000559841,0.000114113085,0.00003124404,2.4528957e-8,0.00007176874,0.037094474],"genre_scores_gemma":[0.0815377,0.0000012064048,0.91548747,0.001032323,0.000016965772,0.0000041234725,1.0325277e-7,0.0000023813748,0.0019177232],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996512,0.000018848858,0.000058595644,0.0001315209,0.00005631677,0.00008353062],"domain_scores_gemma":[0.9997345,0.000033842363,0.0000123275795,0.00017487931,0.00002170354,0.000022735492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007674173,0.000033013854,0.000039664843,0.00005298193,0.000012991465,0.000017897562,0.00020694232,0.000012176099,0.000030357558],"category_scores_gemma":[0.000044048134,0.000029424138,0.000007769468,0.00021431201,0.000007691523,0.00037831927,0.00006211054,0.000030594165,0.00011741122],"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":[7.7650225e-7,0.000051663505,0.00053146307,0.0000014309486,0.0000011892294,0.0000041809967,0.00026061267,0.03022414,0.00021066442,0.87345433,0.00054216804,0.09471739],"study_design_scores_gemma":[0.00020721968,0.000010279817,0.001247255,9.893297e-7,7.300176e-8,0.0000022096806,0.0000029628231,0.98072034,0.0010467076,0.009576141,0.0071286876,0.00005714899],"about_ca_topic_score_codex":0.0000065705253,"about_ca_topic_score_gemma":0.000006567629,"teacher_disagreement_score":0.9504962,"about_ca_system_score_codex":0.000015489193,"about_ca_system_score_gemma":0.0000058980813,"threshold_uncertainty_score":0.15091228},"labels":[],"label_agreement":null},{"id":"W7105897877","doi":"10.23952/jano.7.2025.3.08","title":"A line decomposition algorithm for multiobjective optimization","year":2025,"lang":"","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multi-objective optimization; Line (geometry); Decomposition; Minification; Optimization algorithm; Genetic algorithm","score_opus":0.00808009215581886,"score_gpt":0.28741063485751495,"score_spread":0.2793305427016961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7105897877","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":[0.000008952535,0.0006593351,0.9949587,0.00080902263,0.0013698519,0.0013554299,0.00003094205,0.00006085891,0.00074689573],"genre_scores_gemma":[0.011995051,0.001259249,0.98542285,0.0006585077,0.0003760665,0.000061855935,0.000046501587,0.000049809067,0.00013012675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964743,0.00013727973,0.0016501626,0.0007540391,0.0004966397,0.000487532],"domain_scores_gemma":[0.99476284,0.00054009806,0.0017659861,0.00031351479,0.0023307856,0.0002867815],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006687578,0.00053323,0.000942301,0.00087174925,0.0005710498,0.0004264849,0.00047709406,0.0003675953,0.000031629774],"category_scores_gemma":[0.00024625633,0.00053355255,0.00026763434,0.0016029753,0.00018001374,0.0013149913,0.00020487118,0.00048135684,0.0000015490666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039066546,0.00042164003,0.000002346114,0.00004343122,0.00016783974,0.0000034377267,0.00037549585,0.73053235,0.0000671561,0.0018586027,0.000060089373,0.26607692],"study_design_scores_gemma":[0.005581596,0.00079768285,0.00002773939,0.00019999049,0.00027483926,0.000044184486,0.00017512988,0.98916066,0.0013426081,0.0016351417,0.00030152933,0.00045892718],"about_ca_topic_score_codex":0.0000031383768,"about_ca_topic_score_gemma":1.3982138e-7,"teacher_disagreement_score":0.265618,"about_ca_system_score_codex":0.00043169683,"about_ca_system_score_gemma":0.000423758,"threshold_uncertainty_score":0.9997116},"labels":[],"label_agreement":null},{"id":"W7107873549","doi":"10.18280/jesa.581002","title":"Multi-Objective Optimization of a Multi-Cylinder HCNG Engine Using an Integrated Taguchi–CRITIC–GRA Approach","year":2025,"lang":"","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Welding; Work (physics); Set (abstract data type); Process (computing)","score_opus":0.03606709627113977,"score_gpt":0.30341369447099287,"score_spread":0.26734659819985307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7107873549","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":[0.0033774124,0.002793466,0.98886883,0.000057865807,0.0023192992,0.00169184,0.000097243974,0.00048308034,0.00031096424],"genre_scores_gemma":[0.15454929,0.00044777157,0.8438009,0.0001282921,0.0001470321,0.000031710842,0.000032059583,0.00017644104,0.00068648957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9894047,0.0028388062,0.0033162478,0.0017256641,0.0012650941,0.0014495201],"domain_scores_gemma":[0.98973554,0.00042684225,0.0025072165,0.0013872143,0.0052350513,0.00070814695],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0019890151,0.0013715226,0.0018831318,0.0024940537,0.0015708973,0.0014308423,0.00217821,0.00058407837,0.00009322906],"category_scores_gemma":[0.002227977,0.0013592391,0.00059087604,0.0054960693,0.00092182145,0.0047782464,0.0007728999,0.0018480277,0.0000115214925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007363607,0.0024576096,0.00028858706,0.00044176108,0.0005720928,0.00010182044,0.004494303,0.84818935,0.0013338986,0.0004176105,0.000012497962,0.14161682],"study_design_scores_gemma":[0.0056317784,0.00042585755,0.0119918175,0.0021175153,0.00037485515,0.0010722087,0.0021434685,0.9736612,0.0012198543,0.00023204894,0.000019419858,0.0011100215],"about_ca_topic_score_codex":0.00018625696,"about_ca_topic_score_gemma":0.00002006363,"teacher_disagreement_score":0.15117188,"about_ca_system_score_codex":0.0019703377,"about_ca_system_score_gemma":0.0020119606,"threshold_uncertainty_score":0.99990356},"labels":[],"label_agreement":null},{"id":"W7110062133","doi":"10.2139/ssrn.5886009","title":"Dynamic Constrained Multi-Objective Antenna Design Optimization Method Guided by Reference Vectors","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Wireless; Convergence (economics); Metric (unit); Microstrip antenna; Antenna (radio); Optimization problem; Entropy (arrow of time); Control theory (sociology)","score_opus":0.026066062683516276,"score_gpt":0.325345613926261,"score_spread":0.2992795512427447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7110062133","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":[0.000017215883,0.005343267,0.9870026,0.00072357565,0.0027527295,0.0030069791,0.00021279287,0.00043719733,0.00050364016],"genre_scores_gemma":[0.009623503,0.03363421,0.94688976,0.00029185734,0.00013822658,0.00020289674,0.00018261845,0.00017666949,0.008860237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9792739,0.0046426128,0.003120531,0.0037733784,0.0016123906,0.0075772004],"domain_scores_gemma":[0.9878147,0.001345108,0.003853895,0.001955107,0.004336795,0.0006943437],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.009050027,0.002304257,0.00224564,0.0017088896,0.0018581437,0.0011705534,0.004855304,0.0015069712,0.00016122765],"category_scores_gemma":[0.0022374482,0.0024799374,0.0008097861,0.0029674994,0.0006068435,0.0019768071,0.0018440986,0.0142222,0.000043661476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00023833297,0.0006130807,0.000014555925,0.000053446376,0.0014597952,0.0000266778,0.0010391109,0.92267567,0.0008205915,0.008481171,0.000046396675,0.064531185],"study_design_scores_gemma":[0.0051839887,0.0007754369,0.000023775487,0.0005572074,0.00038374373,0.0013042673,0.0015842656,0.96370345,0.00049748534,0.023794802,0.00009496909,0.0020966344],"about_ca_topic_score_codex":0.00028395478,"about_ca_topic_score_gemma":0.00020284325,"teacher_disagreement_score":0.062434547,"about_ca_system_score_codex":0.015249749,"about_ca_system_score_gemma":0.0314285,"threshold_uncertainty_score":0.9998663},"labels":[],"label_agreement":null},{"id":"W7114892518","doi":"10.1007/s00158-025-04215-4","title":"Attention mechanisms for differentiable state approximation of dynamic systems in multidisciplinary optimization","year":2025,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Differentiable function; Context (archaeology); Multidisciplinary approach; Feature (linguistics); Multidisciplinary design optimization; State (computer science); Component (thermodynamics); Process (computing); Displacement (psychology)","score_opus":0.00722851649366306,"score_gpt":0.2677473162213929,"score_spread":0.26051879972772984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7114892518","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":[0.018801142,0.00026543078,0.9779045,0.0001657145,0.0007056163,0.0019124864,0.000052171115,0.00014404589,0.0000489184],"genre_scores_gemma":[0.32468882,0.000117942014,0.6742469,0.0000055571136,0.000010224401,0.00018383516,0.00031894702,0.000021229802,0.00040652382],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977336,0.00012705372,0.00078440265,0.0007583992,0.00023980312,0.00035672437],"domain_scores_gemma":[0.99856627,0.00012885149,0.00042541022,0.00038066218,0.00042662089,0.00007219667],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026956538,0.00033410286,0.00043070636,0.00061718235,0.00038833066,0.00012557709,0.00033720746,0.00015414684,0.0000036750116],"category_scores_gemma":[0.00006869241,0.0003175876,0.00008209825,0.00091180595,0.00009089802,0.0014182432,0.00036123928,0.0001310748,3.5283603e-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":[0.00008996325,0.00006726754,0.0003424161,0.00031511442,0.000025239277,9.483964e-7,0.00046120273,0.9859141,0.0017739958,0.0072846767,0.000002033598,0.0037230046],"study_design_scores_gemma":[0.0022154597,0.00012230648,0.007164069,0.00017850296,0.000033704113,0.000006353355,0.00033813642,0.9819882,0.00039070685,0.0072570075,7.517979e-7,0.00030481772],"about_ca_topic_score_codex":0.000026359947,"about_ca_topic_score_gemma":0.000013302569,"teacher_disagreement_score":0.3058877,"about_ca_system_score_codex":0.00019491027,"about_ca_system_score_gemma":0.000072839524,"threshold_uncertainty_score":0.99992764},"labels":[],"label_agreement":null},{"id":"W7116055527","doi":"10.1051/e3sconf/202568000025","title":"Leveraging Advanced Optimization Techniques with Deep Learning for Efficient Aerospace and Industrial Design","year":2025,"lang":"fr","type":"article","venue":"E3S Web of Conferences","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Aerospace; Flexibility (engineering); Scalability; Deep learning; Artificial neural network; Multidisciplinary design optimization; Computational model; Range (aeronautics); Aerodynamics","score_opus":0.03729500399331939,"score_gpt":0.2826396962485464,"score_spread":0.245344692255227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116055527","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":[0.00046671228,0.0017242485,0.9928404,0.0012350857,0.00055395183,0.0012414563,0.0000031829459,0.00014511983,0.0017898127],"genre_scores_gemma":[0.30687523,0.0003488405,0.69122213,0.000040135645,0.000043676115,0.00009029494,0.0000049426785,0.000013832661,0.0013608903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981366,0.00023604433,0.0003990398,0.00061197294,0.00025627625,0.0003601081],"domain_scores_gemma":[0.9976918,0.0007180582,0.00041934825,0.0002231618,0.00086644513,0.00008115886],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00051163614,0.0002936299,0.00041949438,0.0002793981,0.00030378185,0.00022866836,0.0003380409,0.00018024807,0.000021986632],"category_scores_gemma":[0.0004951141,0.0002843677,0.00004562816,0.00076771434,0.00038715033,0.00049057143,0.00015160136,0.00028516512,4.5182725e-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":[0.00010982172,0.00005813098,0.0004014984,0.000046859932,0.000038121867,0.0000013105958,0.0005344528,0.78133196,0.00013203653,0.01264134,0.000015439216,0.20468904],"study_design_scores_gemma":[0.0016912767,0.00057109765,0.00007936359,0.0007155974,0.000047941674,0.000004741344,0.000640777,0.97831225,0.014062147,0.0003537541,0.0032270409,0.00029403294],"about_ca_topic_score_codex":0.000030103449,"about_ca_topic_score_gemma":0.000008109393,"teacher_disagreement_score":0.30640852,"about_ca_system_score_codex":0.000083540916,"about_ca_system_score_gemma":0.0013774615,"threshold_uncertainty_score":0.99996084},"labels":[],"label_agreement":null},{"id":"W7117449067","doi":"10.1109/access.2025.3649195","title":"Gradient-Boosted Decision Tree Optimizer for Antenna Optimization","year":2025,"lang":"","type":"article","venue":"IEEE Access","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Semiconductor Research Corporation","keywords":"Bayesian optimization; Convergence (economics); Scalability; Gaussian process; Kriging; Optimization problem; Benchmark (surveying); Tree (set theory); Artificial neural network","score_opus":0.028325128027298942,"score_gpt":0.3447004932181314,"score_spread":0.3163753651908325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117449067","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":[0.00020703663,0.000931082,0.9819897,0.00093481474,0.010888564,0.0031135078,0.00007750529,0.00036492132,0.0014929036],"genre_scores_gemma":[0.029052144,0.00095682935,0.9636298,0.0015321557,0.00031578654,0.00046778677,0.000060170405,0.00010482263,0.0038805301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944013,0.00021178542,0.0014178527,0.002142845,0.00068312074,0.0011431467],"domain_scores_gemma":[0.9937828,0.0011254803,0.000746141,0.0017336294,0.002287832,0.0003241195],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00062098016,0.0008194924,0.000890712,0.0012378722,0.0009900995,0.0018123821,0.0036259254,0.0004397082,0.000105032595],"category_scores_gemma":[0.00096227747,0.0008665366,0.00041509123,0.0040484616,0.00027347478,0.0047606206,0.000919625,0.0003943847,0.000031082753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033221536,0.0003640408,0.00009686439,0.00006964895,0.000116764626,0.000012367839,0.0002217365,0.80580646,0.00011120693,0.0009077562,0.0016315578,0.19032936],"study_design_scores_gemma":[0.005786897,0.00018561924,0.0003206078,0.0004747793,0.00013081502,0.000010837242,0.000043352185,0.9854712,0.0029589143,0.0025665155,0.001229307,0.0008211679],"about_ca_topic_score_codex":0.000025720565,"about_ca_topic_score_gemma":0.00003126194,"teacher_disagreement_score":0.1895082,"about_ca_system_score_codex":0.00054716197,"about_ca_system_score_gemma":0.0005238053,"threshold_uncertainty_score":0.99937856},"labels":[],"label_agreement":null},{"id":"W7124314397","doi":"10.65109/xqbl5396","title":"Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","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":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Reinforcement learning; Pareto principle; Convergence (economics); Sequence (biology); Multi-objective optimization; Iterated function; Pareto optimal","score_opus":0.01111837383102077,"score_gpt":0.25791201852329326,"score_spread":0.2467936446922725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124314397","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":[0.0004191706,0.0010539775,0.9851266,0.0015022317,0.0005197001,0.002387886,0.0000020432226,0.00027111804,0.008717245],"genre_scores_gemma":[0.73017013,0.000477142,0.23817635,0.0013139559,0.00006807509,0.00026099774,0.00000663273,0.000046572302,0.029480154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99579066,0.00041837283,0.0007812248,0.0015097287,0.000608657,0.0008913825],"domain_scores_gemma":[0.9967459,0.00065601734,0.00048284687,0.00092178094,0.0009815274,0.00021196611],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00081481365,0.00069238467,0.00059358677,0.00028533823,0.0016847429,0.0007910472,0.0008618024,0.00016565602,0.000072904615],"category_scores_gemma":[0.00051454076,0.00047124692,0.00009578468,0.0010362763,0.00071990065,0.0012612717,0.001274324,0.0009488909,0.000030486037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013501754,0.00013467284,0.0043171407,0.00007334621,0.00038210303,0.000022490241,0.009211548,0.89955467,0.00007578885,0.009079723,0.000061886094,0.07695163],"study_design_scores_gemma":[0.0031663268,0.0004730584,0.0025691884,0.00034278352,0.00009296276,0.000019764475,0.0027725615,0.9850809,0.0019609502,0.00029479564,0.0025971583,0.0006295225],"about_ca_topic_score_codex":0.0003319096,"about_ca_topic_score_gemma":0.0002583345,"teacher_disagreement_score":0.74695027,"about_ca_system_score_codex":0.00044905106,"about_ca_system_score_gemma":0.0006817008,"threshold_uncertainty_score":0.9997739},"labels":[],"label_agreement":null},{"id":"W7128996971","doi":"10.1109/iemcon67450.2025.11381191","title":"HyperSolver: A Practical Unified Framework for Large-Scale Combinatorial Optimization","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Hypergraph; Benchmark (surveying); Maximization; Set (abstract data type); Artificial neural network; Minification","score_opus":0.01942178109240284,"score_gpt":0.32809883500402603,"score_spread":0.3086770539116232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7128996971","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":[0.000005152887,0.0001315893,0.97120994,0.0064226817,0.011873988,0.002367161,0.000047519963,0.00041946842,0.0075224824],"genre_scores_gemma":[0.0044657183,0.00015014714,0.98852634,0.0020016846,0.00042081307,0.00022159511,0.000041118252,0.000056323242,0.0041162386],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99544793,0.00029733925,0.00093906105,0.00163341,0.000595945,0.0010863313],"domain_scores_gemma":[0.99437225,0.0019759748,0.00043425278,0.001252997,0.00164987,0.0003146279],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007975796,0.00059735664,0.0006981593,0.000419166,0.0009852218,0.0007295459,0.00093761366,0.00075559365,0.0003415848],"category_scores_gemma":[0.003133293,0.0006542056,0.0003112554,0.002517644,0.00017408728,0.0017569659,0.0007258461,0.0007294188,0.00004597685],"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":[0.00025130174,0.001095119,0.000034250905,0.000059763304,0.00011781713,0.00000591195,0.00066619983,0.22083877,0.000007727708,0.7720658,0.0013197524,0.0035375624],"study_design_scores_gemma":[0.0047252686,0.00028699814,0.000018139173,0.00013676827,0.00011562446,0.0000069326074,0.00059287495,0.92817444,0.00057248026,0.056366142,0.008396391,0.0006079349],"about_ca_topic_score_codex":0.000008633172,"about_ca_topic_score_gemma":0.0000041223875,"teacher_disagreement_score":0.7156997,"about_ca_system_score_codex":0.0004523305,"about_ca_system_score_gemma":0.0011507006,"threshold_uncertainty_score":0.99959093},"labels":[],"label_agreement":null},{"id":"W7131173001","doi":"10.1109/robio66223.2025.11376276","title":"Evolutionary Hybrid Optimization for Multi-Robot Task Allocation with LLM Guidance","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Optech (Canada)","funders":"","keywords":"Evolutionary algorithm; Task (project management); Crossover; Convergence (economics); Evolutionary programming; Evolutionary computation","score_opus":0.015562936107833656,"score_gpt":0.28111053876882264,"score_spread":0.265547602660989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131173001","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":[0.000010432725,0.00013794891,0.996216,0.001091919,0.00029140525,0.0008428218,0.000009658705,0.00040989392,0.000989937],"genre_scores_gemma":[0.010769975,0.000022308666,0.98393846,0.0006973964,0.000029109084,0.00028143675,0.000057351586,0.000016360467,0.0041876007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861866,0.000043415774,0.00027907474,0.00060523296,0.00018983727,0.00026380052],"domain_scores_gemma":[0.99843574,0.00010183671,0.00013206922,0.0005048466,0.00076497963,0.00006051685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014144175,0.00018956927,0.00016047418,0.00019941387,0.00026427358,0.00008966953,0.0004991364,0.00004710252,0.000014063205],"category_scores_gemma":[0.00014593807,0.00017266136,0.00004566163,0.00070543616,0.00006484077,0.0010149765,0.0001223956,0.00007277908,0.000009848921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002072459,0.00016234424,0.0000925833,0.000015941547,0.000022766055,0.0000011287498,0.000044546297,0.9699231,0.00012010988,0.020215109,0.0009856096,0.00839604],"study_design_scores_gemma":[0.001310225,0.00008263003,0.0006714755,0.0000353682,0.000009093739,0.0000070738115,0.000021755377,0.99421674,0.0017980884,0.0004299177,0.0012018272,0.00021581305],"about_ca_topic_score_codex":0.000013359505,"about_ca_topic_score_gemma":0.0000093875215,"teacher_disagreement_score":0.024293635,"about_ca_system_score_codex":0.00020972015,"about_ca_system_score_gemma":0.00021198238,"threshold_uncertainty_score":0.7040926},"labels":[],"label_agreement":null},{"id":"W7132888887","doi":"","title":"Generalizable Machine Learning for Mathematical Optimizations","year":2023,"lang":"","type":"dissertation","venue":"TSpace","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Generalization; Computational learning theory; Algorithmic learning theory; Field (mathematics); Mathematical model; Exploit; Mathematical structure; Online machine learning; Black box","score_opus":0.04118100676095209,"score_gpt":0.3683460845021808,"score_spread":0.3271650777412287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132888887","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":[0.00007850218,0.00043578542,0.9909198,0.0005984858,0.0019500798,0.002129121,0.000030040046,0.0009511734,0.0029070117],"genre_scores_gemma":[0.0002622719,0.000609906,0.7052521,0.00008197807,0.00024216447,0.00066147826,0.0017031942,0.00027932276,0.29090756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99529165,0.00023250235,0.0009946151,0.0016397244,0.0007527264,0.0010887834],"domain_scores_gemma":[0.99551,0.00090756244,0.0009039019,0.0009532162,0.0013525817,0.00037277784],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005956219,0.0008978752,0.0009710404,0.00065025967,0.001411371,0.0006014631,0.0012066709,0.0005600038,0.00060449465],"category_scores_gemma":[0.0018414928,0.0010099867,0.00041703263,0.0021122163,0.00011002604,0.0006168947,0.00031125842,0.0007784497,0.00076524296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040626983,0.0001803054,0.000009797959,0.0003445371,0.00013526823,0.000012038,0.013083611,0.97043407,0.0001597826,0.011681689,0.0005553697,0.0033629206],"study_design_scores_gemma":[0.0012312105,0.00020562686,0.000018641422,0.00026161468,0.00013553828,0.000015463613,0.0023928483,0.9872904,0.00083218975,0.0026390057,0.003967089,0.0010103972],"about_ca_topic_score_codex":0.00008239457,"about_ca_topic_score_gemma":0.000049724025,"teacher_disagreement_score":0.28800055,"about_ca_system_score_codex":0.0002970386,"about_ca_system_score_gemma":0.00046070738,"threshold_uncertainty_score":0.99988866},"labels":[],"label_agreement":null},{"id":"W7132918614","doi":"","title":"The Development of a Versatile and Efficient Gradient-Enhanced Bayesian Optimizer for Nonlinearly Constrained Optimization with Application to Aerodynamic Shape Optimization","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayesian optimization; Probabilistic logic; Aerodynamics; Bayesian probability; Constrained optimization; Nonlinear system; Optimization problem; CMA-ES; Global optimization","score_opus":0.007556436759067682,"score_gpt":0.2904343266446862,"score_spread":0.2828778898856185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132918614","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":[0.0037796088,0.00026498744,0.9871443,0.00045005375,0.00083406013,0.0069876914,0.000041599633,0.00019923454,0.00029848836],"genre_scores_gemma":[0.07508999,0.000102339305,0.9209724,0.00004017589,0.000063649306,0.0017042636,0.0008511469,0.00016711451,0.0010088958],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99480563,0.00013684783,0.0014118201,0.0019355161,0.0009012896,0.0008088705],"domain_scores_gemma":[0.9946963,0.0005758533,0.0012583259,0.0008920256,0.0022160674,0.00036138028],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007526739,0.0009523083,0.00081656285,0.00061173557,0.001140076,0.00056611997,0.0008433373,0.00036358298,0.000026081472],"category_scores_gemma":[0.0002916831,0.00081055047,0.0001492043,0.0020924541,0.00030429926,0.0003559718,0.00022221697,0.0003765669,0.0000090675285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063251366,0.00016494557,0.0000013317789,0.00030486166,0.00019087356,9.297277e-7,0.03434288,0.926359,0.0007025356,0.00063053885,0.0000072207613,0.03666236],"study_design_scores_gemma":[0.0019801292,0.00048772246,0.000033067296,0.0005910583,0.0002006579,0.000007090603,0.008287302,0.9853977,0.0020403287,0.000022320213,0.000071648676,0.0008809637],"about_ca_topic_score_codex":0.000019071053,"about_ca_topic_score_gemma":0.00017150362,"teacher_disagreement_score":0.07131038,"about_ca_system_score_codex":0.0005481358,"about_ca_system_score_gemma":0.0011626247,"threshold_uncertainty_score":0.99943453},"labels":[],"label_agreement":null},{"id":"W7133451701","doi":"","title":"ONE-SHOT OPTIMIZATION FOR THE INVERSE DESIGN OF A QUASI-1D DE LAVAL NOZZLE","year":2024,"lang":"en","type":"article","venue":"ORBi UMONS","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nozzle; Inverse; Inverse problem; Work (physics); Optimal design","score_opus":0.05804233546895118,"score_gpt":0.3068157780428112,"score_spread":0.24877344257386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133451701","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":[0.000021780004,0.00014201405,0.9971302,0.0015471171,0.0002937729,0.00048900914,0.000013278535,0.00017195314,0.00019084182],"genre_scores_gemma":[0.013588553,0.00019831644,0.9854792,0.00015921236,0.00005981297,0.00011031559,0.0000048436777,0.000021700676,0.0003780886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911827,0.00006822547,0.00017448957,0.00027744946,0.00015479374,0.00020675361],"domain_scores_gemma":[0.9988663,0.00053932134,0.000061902996,0.00034132,0.00013670845,0.000054414602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003171406,0.00010399636,0.00011713015,0.000101890815,0.00012587418,0.00009747335,0.00041933113,0.000049902806,0.000021055504],"category_scores_gemma":[0.00019652178,0.00008804136,0.00006641196,0.00053313636,0.00006993862,0.0004024779,0.00008922054,0.00009132692,0.000010515288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009368794,0.00006821351,0.000007997939,0.000018670944,0.00002875419,0.0000027792091,0.00095053186,0.9805566,0.0008092371,0.0076140813,0.001189034,0.008744712],"study_design_scores_gemma":[0.00021909582,0.000090858826,0.000025088033,0.000028253631,0.000019766823,0.000006218115,0.00005347212,0.99456406,0.0028093425,0.0010430616,0.0010372623,0.000103551676],"about_ca_topic_score_codex":0.00002662699,"about_ca_topic_score_gemma":0.00001486469,"teacher_disagreement_score":0.014007409,"about_ca_system_score_codex":0.00007796298,"about_ca_system_score_gemma":0.00020912282,"threshold_uncertainty_score":0.35902223},"labels":[],"label_agreement":null},{"id":"W74931915","doi":"10.1007/978-3-642-21434-9_3","title":"Automated Algorithm Configuration and Parameter Tuning","year":2011,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":196,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Algorithm; Set (abstract data type); Selection (genetic algorithm); Parameter space; Mathematical optimization; Data mining; Machine learning; Mathematics","score_opus":0.02456515553784827,"score_gpt":0.2496453930371361,"score_spread":0.22508023749928782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W74931915","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":[1.11882066e-7,0.00008732658,0.68768936,0.000035024066,0.00021901309,0.00022724154,0.000005374543,0.0010039322,0.31073257],"genre_scores_gemma":[0.000051135354,0.000115011215,0.73125273,0.00024586573,0.000043064483,0.000011518782,0.000027224045,0.000040540373,0.26821288],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848723,0.000021584237,0.00033717722,0.00069279684,0.00023653744,0.0002246513],"domain_scores_gemma":[0.9987043,0.0001114976,0.00026301097,0.00051446864,0.0002757623,0.00013096558],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010412598,0.00036587953,0.00032649786,0.00023068512,0.00011887628,0.00015882474,0.00035974305,0.00028470025,0.00031122717],"category_scores_gemma":[0.000031099044,0.00034729467,0.000060831695,0.00005419334,0.000108904365,0.0006446335,0.00022807693,0.00025462252,0.00017545471],"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":[0.0000028398852,0.000016541671,9.899534e-7,0.000013097061,0.00008400032,0.000048697006,0.00038141478,0.00015409477,0.000029069086,0.42057967,0.0006334552,0.5780561],"study_design_scores_gemma":[0.0003365434,0.00007391385,0.000021339009,0.0000535889,0.000017362614,0.00006056791,0.000003800017,0.95551986,0.0002194843,0.025658533,0.017489042,0.0005459894],"about_ca_topic_score_codex":0.000011589572,"about_ca_topic_score_gemma":0.000002220577,"teacher_disagreement_score":0.9553657,"about_ca_system_score_codex":0.00006364093,"about_ca_system_score_gemma":0.00006132792,"threshold_uncertainty_score":0.9998979},"labels":[],"label_agreement":null}]}