{"meta":{"query_hash":"bc92b95a269a","filters":{"venue":"Journal of Computational Design and Engineering"},"cohort_total":26,"direct_labels_cover":0,"predictions_cover":26,"exported":26,"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/bc92b95a269a","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Computational+Design+and+Engineering"},"results":[{"id":"W1539570097","doi":"10.7315/jcde.2014.021","title":"Survey on the virtual commissioning of manufacturing systems","year":2014,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Flexible and Reconfigurable Manufacturing Systems","field":"Engineering","cited_by":197,"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":"Agency for Defense Development; Ministry of Education, Science and Technology","keywords":"Project commissioning; Debugging; Engineering; Controller (irrigation); Systems engineering; Computer science; Control engineering; Manufacturing engineering; Operating system; Publishing","score_opus":0.018014839370516973,"score_gpt":0.19791926742201385,"score_spread":0.17990442805149687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1539570097","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":[0.465177,0.00036168616,0.53337294,0.000025608617,0.0007457169,0.00008086369,0.0000020320185,0.00003314711,0.00020099214],"genre_scores_gemma":[0.99906373,0.000019775172,0.00074658426,0.00000860652,0.000116777795,0.0000011229971,0.0000011914113,0.000019186653,0.000023027418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991231,0.00008763365,0.00039571748,0.000053591848,0.00022409462,0.00011585564],"domain_scores_gemma":[0.99823636,0.0014656597,0.00010938235,0.00006720722,0.000058205962,0.00006316426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010400143,0.000120408455,0.00024049575,0.00015102608,0.000045881556,0.000047892845,0.000119276636,0.00004457308,0.000004124754],"category_scores_gemma":[0.0000630243,0.00008084055,0.00004598983,0.000055113673,0.000014475284,0.00006900753,0.000007965359,0.0001976967,0.0000018422702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010175585,0.000004654282,0.00002245521,0.00006933205,0.00006025492,0.0000014169938,0.00007090859,0.99682647,0.0004963636,0.0006204319,0.00050182844,0.0013157352],"study_design_scores_gemma":[0.00025981275,0.00013584831,0.0135926,0.00041800618,0.000009995918,0.00005377499,0.000029254143,0.9754639,0.009066026,0.00013048066,0.00071823393,0.00012203885],"about_ca_topic_score_codex":0.0000063637044,"about_ca_topic_score_gemma":9.641821e-8,"teacher_disagreement_score":0.53388673,"about_ca_system_score_codex":0.000022603766,"about_ca_system_score_gemma":0.000010955334,"threshold_uncertainty_score":0.3296582},"labels":[],"label_agreement":null},{"id":"W2224136207","doi":"10.1016/j.jcde.2015.12.001","title":"Cutter-workpiece engagement determination for general milling using triangle mesh modeling","year":2015,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Machining; Milling cutter; Process (computing); Intersection (aeronautics); Tool path; Mechanical engineering; Engineering; Polygon mesh; Engineering drawing; Path (computing); Series (stratigraphy); Cutter location; Geometry; Boundary (topology); Structural engineering; Computer science; Mathematics; Mathematical analysis","score_opus":0.06579461531457148,"score_gpt":0.2805356526886614,"score_spread":0.2147410373740899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2224136207","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06217532,0.00072145334,0.9366566,0.000012918811,0.0003002672,0.0000916325,9.3006565e-7,0.000035047487,0.0000058370874],"genre_scores_gemma":[0.43255654,0.000032318134,0.5672448,0.0000088994375,0.00013489947,0.0000015120455,0.000002600551,0.000016593003,0.0000017902038],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993662,0.00001225571,0.00030283607,0.000063475505,0.000141602,0.000113623624],"domain_scores_gemma":[0.99952346,0.000110725945,0.000067033994,0.000025611316,0.00019451774,0.00007863267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042904768,0.000100877114,0.00015425087,0.00014955744,0.000044144635,0.000047261503,0.000047865127,0.000034848188,6.2397766e-7],"category_scores_gemma":[0.00007073411,0.00010320558,0.00003891048,0.000083509105,0.0000035434834,0.00028700638,0.000007496901,0.000094053845,1.1187988e-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":[0.000019464547,0.000004841683,0.0000034190614,0.00005304069,0.000021972064,0.0000018472163,0.0002244209,0.99458396,0.0005997598,0.00014844528,0.000018455485,0.004320372],"study_design_scores_gemma":[0.00067314075,0.00006656915,0.000005101482,0.00007779961,0.000027721715,0.00004291814,0.000028129347,0.9966538,0.00028098884,0.0019347527,0.000098318254,0.00011077303],"about_ca_topic_score_codex":3.7782274e-7,"about_ca_topic_score_gemma":2.7196133e-8,"teacher_disagreement_score":0.37038124,"about_ca_system_score_codex":0.0000754718,"about_ca_system_score_gemma":0.00002769281,"threshold_uncertainty_score":0.42086014},"labels":[],"label_agreement":null},{"id":"W2409698788","doi":"10.1016/j.jcde.2016.05.002","title":"Dynamic analysis and controller design for a slider–crank mechanism with piezoelectric actuators","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Dynamics and Control of Mechanical Systems","field":"Engineering","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":"University of Alberta","funders":"","keywords":"Control theory (sociology); Slider; Connecting rod; Feedback linearization; Vibration; Engineering; Controller (irrigation); Crank; Mechanism (biology); Actuator; Computer science; Mechanical engineering; Acoustics; Physics; Control (management)","score_opus":0.005200951169182097,"score_gpt":0.18130894052904872,"score_spread":0.17610798935986663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2409698788","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":[0.01731639,0.0004928604,0.9817783,0.00009616029,0.00007385152,0.00020386983,0.0000033173606,0.000033433942,0.0000018095664],"genre_scores_gemma":[0.874974,0.0000715769,0.12486607,0.000013383184,0.000030398378,0.000012195604,4.6963564e-7,0.00002304264,0.000008849051],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917674,0.00002305389,0.00033036177,0.00010241624,0.0001872977,0.00018010031],"domain_scores_gemma":[0.9987969,0.0008266279,0.00009252912,0.000038662278,0.000118646276,0.00012667869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046095837,0.00015754967,0.00038644558,0.00034039372,0.000035480836,0.00005248594,0.000067348,0.000056157223,0.0000021435253],"category_scores_gemma":[0.000038467988,0.000102249236,0.00008886634,0.00020948275,0.000008010903,0.0001369267,0.0000051017973,0.00007305118,3.4134175e-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":[0.00013180263,0.000008357509,0.0000070028586,0.000028478467,0.0011104655,0.000008925368,0.000027325,0.96042335,0.0046990174,0.0022463498,0.000011593659,0.031297337],"study_design_scores_gemma":[0.0018529181,0.00030248458,0.00023670374,0.000059708498,0.00030503163,0.000092225506,0.0000044552476,0.99045503,0.00006174065,0.006451273,0.000026860747,0.00015154529],"about_ca_topic_score_codex":5.2483557e-7,"about_ca_topic_score_gemma":3.8571136e-7,"teacher_disagreement_score":0.8576576,"about_ca_system_score_codex":0.00006347356,"about_ca_system_score_gemma":0.000023792116,"threshold_uncertainty_score":0.4169603},"labels":[],"label_agreement":null},{"id":"W2564475350","doi":"10.1016/j.jcde.2016.12.001","title":"Designing a generic human-machine framework for real-time supply chain planning","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Scheme (mathematics); Convex hull; Mathematical optimization; Decision support system; Software; Regular polygon; Artificial intelligence; Mathematics","score_opus":0.025899957398394548,"score_gpt":0.23902345211502973,"score_spread":0.2131234947166352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564475350","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":[0.023952013,0.000196023,0.9748472,0.00045151418,0.00022579916,0.00016251762,0.0000010023438,0.00004639468,0.000117532145],"genre_scores_gemma":[0.7423548,0.000018378663,0.25590125,0.0002930236,0.0012896731,0.000012410131,0.000005405488,0.00004007266,0.000084974236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990799,0.000010722572,0.00036180558,0.00012389327,0.00022045441,0.00020326488],"domain_scores_gemma":[0.9991061,0.00043253938,0.00024591462,0.000054352236,0.00013245133,0.000028632818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068430183,0.00015238335,0.00021795281,0.00037373294,0.00011634632,0.00012882258,0.00013118157,0.00004476047,0.00004885404],"category_scores_gemma":[0.00012485454,0.000115830975,0.00008127191,0.00014395146,0.00001688716,0.00051816215,0.0000383275,0.000076863114,0.000007044736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015059406,0.000059758535,0.0009592828,0.00024668977,0.00020288762,0.00004451942,0.00013645581,0.882543,0.029647244,0.07280719,0.005696842,0.007505524],"study_design_scores_gemma":[0.0028760114,0.00021958965,0.008313467,0.0013740157,0.00015160887,0.000047476697,0.000092279246,0.8804783,0.0004284168,0.09383634,0.01152215,0.00066030206],"about_ca_topic_score_codex":0.0000026301777,"about_ca_topic_score_gemma":3.6116905e-8,"teacher_disagreement_score":0.718946,"about_ca_system_score_codex":0.00003986688,"about_ca_system_score_gemma":0.000013881138,"threshold_uncertainty_score":0.47234502},"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":"W2770610194","doi":"10.1016/j.jcde.2017.11.004","title":"Interdisciplinary semantic model for managing the design of a steam-assisted gravity drainage tooling system","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta; Natural Sciences and Engineering Research Council of Canada; Canadian Association of Petroleum Producers","keywords":"Unified Modeling Language; Systems engineering; Computer science; Process (computing); Steam-assisted gravity drainage; Activity diagram; Engineering; Software engineering; Software; Programming language","score_opus":0.043852843244877586,"score_gpt":0.29631815850780685,"score_spread":0.25246531526292926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770610194","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":[0.037088927,0.00034582667,0.9619758,0.00004512212,0.0002743723,0.00019366406,0.0000024540889,0.000046531495,0.000027264614],"genre_scores_gemma":[0.69343317,0.000010674083,0.30645975,0.000001587319,0.000059426657,0.000005329481,5.964778e-7,0.000021760585,0.000007693103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990728,0.000039575487,0.000455732,0.00008165855,0.00019415072,0.00015607887],"domain_scores_gemma":[0.99874854,0.00070064235,0.0001851729,0.00015370101,0.00014675986,0.00006519987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012724,0.00014891615,0.0002975584,0.00016554153,0.0001874578,0.00010599208,0.00025875846,0.000043433243,3.0853025e-7],"category_scores_gemma":[0.000102217804,0.00011927617,0.000103918166,0.000055966244,0.000024948815,0.00024537023,0.000039381048,0.00016633318,1.4417408e-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":[0.000039802915,0.000006386844,0.000004689442,0.00045594724,0.0001026277,0.0000071833665,0.00028818764,0.9944214,0.0021095558,0.0011542335,0.000028991995,0.0013809636],"study_design_scores_gemma":[0.0005380423,0.00004823691,0.00086904987,0.0003554829,0.000048658603,0.00008911556,0.00006520162,0.9940545,0.00021433052,0.0035944975,0.000004016295,0.00011885411],"about_ca_topic_score_codex":4.6868524e-7,"about_ca_topic_score_gemma":5.1360235e-8,"teacher_disagreement_score":0.65634423,"about_ca_system_score_codex":0.000053688345,"about_ca_system_score_gemma":0.000023546378,"threshold_uncertainty_score":0.4863941},"labels":[],"label_agreement":null},{"id":"W2889263021","doi":"10.1016/j.jcde.2018.08.004","title":"Finite element method for the static and dynamic analysis of FRP guyed tower","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Structural Analysis and Optimization","field":"Engineering","cited_by":30,"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; Manitoba Hydro; University of Windsor","keywords":"Structural engineering; Finite element method; Fibre-reinforced plastic; Equilateral triangle; Serviceability (structure); Engineering; Tower; Vibration; Static analysis; Mathematics; Geometry","score_opus":0.010014018193696307,"score_gpt":0.2484417148035347,"score_spread":0.2384276966098384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889263021","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":[0.017231746,0.00045123114,0.98211807,0.000059986545,0.00007021849,0.00005584865,0.0000042767365,0.000006224762,0.0000024082938],"genre_scores_gemma":[0.6272126,0.00007217488,0.3726655,0.000012846049,0.000024568842,0.0000012279777,0.0000029162293,0.000005035514,0.0000031379611],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994903,0.000011356746,0.000276647,0.00004491131,0.00011071408,0.0000660551],"domain_scores_gemma":[0.99907297,0.00063551054,0.00007755044,0.000031421445,0.00015238253,0.000030163168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029264347,0.000069069836,0.00018558787,0.00021115296,0.000032256496,0.000023732704,0.00004253149,0.000017841314,0.000015540438],"category_scores_gemma":[0.000037544094,0.000047552694,0.0000659217,0.00024942236,0.000012857401,0.00006584072,0.0000060134553,0.00004142437,4.7013902e-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":[0.000011511107,0.0000019256565,0.00001979895,0.000016849413,0.0008629944,2.3397597e-7,0.0001806302,0.9933049,0.00059564627,0.0001118927,0.0000323593,0.004861251],"study_design_scores_gemma":[0.00019362051,0.000060272694,0.0060023246,0.000012930217,0.0005661277,0.0000045635948,0.000026818592,0.9923358,0.00015002275,0.0005223828,0.00007248437,0.000052664916],"about_ca_topic_score_codex":0.0000010426623,"about_ca_topic_score_gemma":5.3370104e-7,"teacher_disagreement_score":0.6099808,"about_ca_system_score_codex":0.000014947504,"about_ca_system_score_gemma":0.0000068036607,"threshold_uncertainty_score":0.19391426},"labels":[],"label_agreement":null},{"id":"W2898430231","doi":"10.1016/j.jcde.2018.10.006","title":"A hybridization of differential evolution and monarch butterfly optimization for solving systems of nonlinear equations","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Metaheuristic Optimization Algorithms Research","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":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metaheuristic; Nonlinear system; Mathematical optimization; Differential evolution; Maxima and minima; Heuristic; Optimization problem; Computer science; Mathematics; Algorithm","score_opus":0.02470235642766716,"score_gpt":0.2602231180731059,"score_spread":0.23552076164543873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898430231","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073718103,0.00018673508,0.9920171,0.000051121155,0.00017891303,0.00017945126,0.0000042341794,0.000008478257,0.0000021605865],"genre_scores_gemma":[0.48509538,0.000014768842,0.51481605,0.0000013264928,0.00006141402,0.0000019051018,0.0000022609408,0.0000043360533,0.0000025803972],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989612,0.000059511694,0.00047517996,0.000096119795,0.00031572217,0.00009227876],"domain_scores_gemma":[0.99766403,0.0007677467,0.00028709118,0.000059420607,0.0011635425,0.000058153797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068736967,0.00007217472,0.00018152822,0.0003613327,0.000054900873,0.000059863414,0.00012547115,0.0000363684,0.0000021350172],"category_scores_gemma":[0.00044221716,0.000069711794,0.0000291418,0.00025698447,0.000037696158,0.0003161472,0.000036850906,0.000061784194,8.003013e-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":[0.000019172589,0.000023678213,0.000026589276,0.0000874273,0.00002979061,3.3179543e-7,0.00014086653,0.9899129,0.0017474564,0.0067891716,0.000010899538,0.0012116837],"study_design_scores_gemma":[0.0004855921,0.00024363541,0.00048223726,0.00009077573,0.0000144399655,0.0000267734,0.000010523951,0.99752265,0.00042141508,0.00064178614,0.000003964407,0.000056205943],"about_ca_topic_score_codex":0.0000022198096,"about_ca_topic_score_gemma":3.184555e-8,"teacher_disagreement_score":0.47772357,"about_ca_system_score_codex":0.000030295276,"about_ca_system_score_gemma":0.00011028749,"threshold_uncertainty_score":0.28427646},"labels":[],"label_agreement":null},{"id":"W2922260712","doi":"10.1016/j.jcde.2019.03.002","title":"Variational B-rep model analysis for direct modeling using geometric perturbation","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Dynamics and Control of Mechanical Systems","field":"Engineering","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalization; Boundary representation; Representation (politics); Constraint (computer-aided design); Perturbation (astronomy); Geometric modeling; Boundary (topology)","score_opus":0.016700969542642358,"score_gpt":0.20569897468770282,"score_spread":0.18899800514506046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922260712","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":[0.10646269,0.0003275384,0.8928532,0.000012161231,0.00019014008,0.00010653142,0.000004242407,0.00002155156,0.000021915755],"genre_scores_gemma":[0.8278083,0.000010406389,0.17206709,0.000008596923,0.000074001175,0.0000022209242,0.0000043297937,0.000014998598,0.000010030788],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919146,0.000009745317,0.00037368847,0.00008010134,0.00022776323,0.00011725957],"domain_scores_gemma":[0.9993784,0.00025586408,0.000077947945,0.000039506398,0.00018350962,0.00006480835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044988169,0.00010300948,0.00027365808,0.0004990603,0.000026130305,0.00005422129,0.00006316664,0.00005098812,0.0000031071756],"category_scores_gemma":[0.00003417796,0.0000991271,0.00013880066,0.00034338087,0.0000016040898,0.00018401301,0.000006111462,0.00007941304,4.523136e-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":[0.0000142343315,0.0000063650054,0.000034951358,0.00003427421,0.00039398336,4.4440773e-7,0.000024983565,0.9962378,0.0011646916,0.0018445912,0.000003223251,0.0002404521],"study_design_scores_gemma":[0.00043013925,0.00003361493,0.00013236626,0.00002416129,0.00015839243,0.000011029101,0.0000039322954,0.99640733,0.000007549708,0.0026734907,0.000006708934,0.00011129225],"about_ca_topic_score_codex":8.1959274e-7,"about_ca_topic_score_gemma":4.3513282e-8,"teacher_disagreement_score":0.72134566,"about_ca_system_score_codex":0.00009228587,"about_ca_system_score_gemma":0.000029799416,"threshold_uncertainty_score":0.4042286},"labels":[],"label_agreement":null},{"id":"W3015497865","doi":"10.1093/jcde/qwaa039","title":"An efficient controlled elitism non-dominated sorting genetic algorithm for multi-objective supplier selection under fuzziness","year":2020,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","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":"Sorting; Mathematical optimization; TOPSIS; Genetic algorithm; Particle swarm optimization; Taguchi methods; Selection (genetic algorithm); Ideal solution; Multi-objective optimization; Supply chain; Computer science; Fuzzy logic; Maximization; Algorithm; Mathematics; Operations research; Artificial intelligence; Machine learning","score_opus":0.07019889654136877,"score_gpt":0.3544353585863633,"score_spread":0.28423646204499453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015497865","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":[0.10878352,0.0000836246,0.89031255,0.00014056414,0.00028744325,0.00036832958,0.000004774201,0.000017608283,0.0000015984705],"genre_scores_gemma":[0.5295463,0.0000013628487,0.47017795,0.000091865375,0.00015790279,0.0000066713756,9.91843e-7,0.000013928804,0.0000030312424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748296,0.00013095842,0.0010889798,0.0002814574,0.0008020544,0.00021360574],"domain_scores_gemma":[0.99584836,0.0022210837,0.00049674156,0.00006316217,0.0011405335,0.00023012066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018314248,0.00018019832,0.0005165969,0.00037915472,0.00013592995,0.00031803918,0.00022882174,0.00006972716,0.000020142137],"category_scores_gemma":[0.00093999604,0.00014540044,0.0001423297,0.00041610736,0.000021127355,0.0002496619,0.00002751367,0.00017351167,0.0000034550862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023701717,0.000040941308,0.000017851708,0.0000056807517,0.000047922702,0.000009644902,0.00043937497,0.9617433,0.008498272,0.000063049396,0.000047480014,0.02884948],"study_design_scores_gemma":[0.005619252,0.00024227625,0.011045099,0.000031886244,0.00003136877,0.00008884819,0.00017504013,0.98118275,0.00029223828,0.001107767,0.000026674945,0.00015678826],"about_ca_topic_score_codex":0.0000011665799,"about_ca_topic_score_gemma":9.588941e-8,"teacher_disagreement_score":0.42076278,"about_ca_system_score_codex":0.00005764126,"about_ca_system_score_gemma":0.0001043574,"threshold_uncertainty_score":0.5929258},"labels":[],"label_agreement":null},{"id":"W3034731855","doi":"10.1093/jcde/qwaa059","title":"A fuzzy-based framework to support multicriteria design of mechatronic systems","year":2020,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Design Education and Practice","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Mechatronics; Fuzzy logic; Conceptual design; Process (computing); Range (aeronautics); Fuzzy set; Identification (biology); Engineering design process; Measure (data warehouse)","score_opus":0.03483809038751014,"score_gpt":0.260161083025475,"score_spread":0.2253229926379649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034731855","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":[0.003975627,0.00043226744,0.99458534,0.00030094152,0.0004736746,0.00016401843,0.0000027180813,0.00004591085,0.000019521583],"genre_scores_gemma":[0.6547711,0.000013192121,0.34496775,0.00011210221,0.000110314686,0.0000035201053,9.584093e-7,0.000019518691,0.000001548854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990483,0.000057183843,0.0004445329,0.00007817997,0.00023390797,0.00013792125],"domain_scores_gemma":[0.9986856,0.0007797432,0.0001004467,0.00005127935,0.00015557096,0.00022735272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044330023,0.0001306329,0.0002516876,0.00017271534,0.000018998302,0.00005582702,0.00011256653,0.00005665699,0.000019592368],"category_scores_gemma":[0.00022248353,0.00013356707,0.000045954803,0.00022853068,0.0000071967193,0.0001769495,0.000007320632,0.00019385798,0.000008671759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006412784,0.000014347036,0.00000533177,0.00015862913,0.000060288523,0.000009007575,0.0005163499,0.9890228,0.0076307044,0.0008417387,0.0010390457,0.000637657],"study_design_scores_gemma":[0.00034533878,0.00033118718,0.00017285591,0.00012695197,0.00003607872,0.000054380995,0.000061677834,0.9964598,0.00081887987,0.00032909066,0.0011204076,0.0001433828],"about_ca_topic_score_codex":4.5972956e-7,"about_ca_topic_score_gemma":5.1289675e-9,"teacher_disagreement_score":0.65079546,"about_ca_system_score_codex":0.00003844956,"about_ca_system_score_gemma":0.00010560567,"threshold_uncertainty_score":0.5446707},"labels":[],"label_agreement":null},{"id":"W3117154257","doi":"10.1093/jcde/qwaa089","title":"A biobjective home health care logistics considering the working time and route balancing: a self-adaptive social engineering optimizer","year":2020,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Vehicle Routing Optimization Methods","field":"Engineering","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":"École de Technologie Supérieure","funders":"","keywords":"Metaheuristic; Vehicle routing problem; Scheduling (production processes); Computer science; Population; Operations research; Operations management; Routing (electronic design automation); Engineering; Artificial intelligence; Medicine; Computer network","score_opus":0.02356356349678254,"score_gpt":0.23335147664918524,"score_spread":0.2097879131524027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117154257","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":[0.009274523,0.0017880033,0.98802376,0.00042637903,0.00014721179,0.00014890655,0.0000045479114,0.00017132156,0.00001534322],"genre_scores_gemma":[0.53946435,0.00004460778,0.4601746,0.000090898575,0.00018884681,0.0000017697216,0.0000013575652,0.000033164684,4.4112937e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891037,0.00006856143,0.00043044446,0.00012943403,0.00022555163,0.00023562863],"domain_scores_gemma":[0.9989395,0.00059475575,0.00013598385,0.000038874645,0.00012990583,0.00016094245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004292831,0.00021123394,0.00036265433,0.00010306331,0.00012783424,0.000110434776,0.00009482457,0.00006607481,0.0000020734624],"category_scores_gemma":[0.000104753184,0.00019291948,0.000059136408,0.00024758864,0.00002298777,0.00013812598,0.000040501724,0.00038516053,7.620477e-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":[0.00001918135,0.0000031002696,0.000022996479,0.0000757545,0.00013516021,0.0000123896525,0.0054709967,0.9924318,0.00023214247,0.00019963561,0.00006870008,0.0013281065],"study_design_scores_gemma":[0.00053432625,0.00008705189,0.0009595507,0.00009239052,0.00003692034,0.00012255237,0.0002296523,0.9975232,0.00005096539,0.000043223106,0.00012917416,0.00019099732],"about_ca_topic_score_codex":8.3305514e-7,"about_ca_topic_score_gemma":4.6981576e-8,"teacher_disagreement_score":0.5301898,"about_ca_system_score_codex":0.00014992875,"about_ca_system_score_gemma":0.00008363644,"threshold_uncertainty_score":0.7867028},"labels":[],"label_agreement":null},{"id":"W3132127288","doi":"10.1093/jcde/qwab009","title":"A novel particle swarm optimization-based grey model for the prediction of warehouse performance","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina; École de Technologie Supérieure","funders":"","keywords":"Performance indicator; Particle swarm optimization; Warehouse; Supply chain; Data mining; Genetic algorithm; Taguchi methods; Computer science; Key (lock); Engineering; Operations research; Machine learning","score_opus":0.09769746644814277,"score_gpt":0.29296198254697847,"score_spread":0.19526451609883572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132127288","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":[0.035181247,0.00010921718,0.9641799,0.00031670046,0.00008212761,0.00010985803,0.000011292937,0.000006718161,0.0000029636253],"genre_scores_gemma":[0.74816686,0.0000040172104,0.2517492,0.000023380555,0.000027005772,0.0000075181033,9.643661e-7,0.0000051246243,0.000015934616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989486,0.000025821668,0.0004875521,0.00007840128,0.0003929066,0.00006672063],"domain_scores_gemma":[0.99642855,0.0022647984,0.00022377781,0.00009251592,0.00094638776,0.00004397914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012543133,0.00005411185,0.00012410263,0.00007408899,0.00007993388,0.000057380737,0.00013332668,0.000021316944,0.0000044016074],"category_scores_gemma":[0.00061118545,0.00003779404,0.000056863144,0.00025754288,0.000019707259,0.00018147165,0.000011069466,0.00005050117,4.6024041e-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":[0.000025459569,0.000025389943,0.00010137964,0.000010793649,0.000017670798,1.8901272e-7,0.0001790537,0.99675226,0.0012629947,0.0010890921,0.00006663259,0.00046906873],"study_design_scores_gemma":[0.00043220996,0.000035992867,0.0014933377,0.000028008662,0.000018702009,0.00003381489,0.000043357366,0.99528426,0.0012893591,0.0012777514,0.00003072264,0.00003251256],"about_ca_topic_score_codex":1.6689425e-7,"about_ca_topic_score_gemma":6.05036e-8,"teacher_disagreement_score":0.71298563,"about_ca_system_score_codex":0.000016580863,"about_ca_system_score_gemma":0.00014439096,"threshold_uncertainty_score":0.15411963},"labels":[],"label_agreement":null},{"id":"W3135803911","doi":"10.1093/jcde/qwab011","title":"Airfoil profile reconstruction from unorganized noisy point cloud data","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Airfoil; Algorithm; Noise (video); Point cloud; Computer science; Mathematics; Computer vision; Engineering; Structural engineering","score_opus":0.02453848477246032,"score_gpt":0.22300566826383547,"score_spread":0.19846718349137515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135803911","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05269584,0.0012619322,0.9451578,0.000068634356,0.00062811933,0.000038636146,0.0000073703413,0.00008496742,0.000056654168],"genre_scores_gemma":[0.45148566,0.00017073103,0.54802805,0.000018936698,0.00024788323,9.805058e-7,0.000023742516,0.000016969976,0.0000070364817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939567,0.000020560992,0.0002672808,0.000088449946,0.00013368807,0.00009433343],"domain_scores_gemma":[0.999528,0.00013190847,0.00005571955,0.00008922127,0.00013707211,0.000058104364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023564133,0.000093185314,0.0001733523,0.00008502064,0.000023491231,0.000022698667,0.00008882504,0.000049059596,0.00003132563],"category_scores_gemma":[0.00008450756,0.00009440207,0.000024877258,0.00007888419,0.000010331371,0.00028579967,0.000023904095,0.00017447463,0.0000015536497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002993228,0.000016434418,0.00013199504,0.000037960093,0.00018833035,0.000046846497,0.00006273231,0.8758217,0.10566649,0.00027596767,0.0017730027,0.015948609],"study_design_scores_gemma":[0.0011836863,0.0000709502,0.0027427576,0.00019379382,0.000086134125,0.0007531494,0.00004816019,0.9371022,0.04670895,0.008633551,0.0022016515,0.00027503667],"about_ca_topic_score_codex":4.224854e-7,"about_ca_topic_score_gemma":1.7670301e-7,"teacher_disagreement_score":0.39878985,"about_ca_system_score_codex":0.00003908584,"about_ca_system_score_gemma":0.000039185627,"threshold_uncertainty_score":0.38496047},"labels":[],"label_agreement":null},{"id":"W3201884713","doi":"10.1093/jcde/qwab051","title":"A novel lattice structure topology optimization method with extreme anisotropic lattice properties","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":58,"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":"Natural Science Foundation of Shandong Province; Natural Science Foundation of Jiangsu Province; Shandong University","keywords":"Topology optimization; Lattice (music); Topology (electrical circuits); Homogenization (climate); Mathematics; Anisotropy; Mathematical optimization; Computer science; Structural engineering; Finite element method; Physics; Engineering; Combinatorics","score_opus":0.02031769075495351,"score_gpt":0.2181945457627767,"score_spread":0.19787685500782318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201884713","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010279434,0.0008745025,0.988214,0.00015320709,0.0003036464,0.000063094536,0.0000026024582,0.00008407468,0.000025407335],"genre_scores_gemma":[0.21397603,0.00004796964,0.7857962,0.000031549047,0.00009427939,0.0000018528174,0.0000036017143,0.000033286185,0.000015222678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991902,0.000030719562,0.00032480375,0.00011088267,0.00017408183,0.00016929087],"domain_scores_gemma":[0.99930936,0.00019490742,0.00007014186,0.00006809292,0.00027418864,0.00008330983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012251876,0.00016949303,0.00024929043,0.0001746301,0.000045279765,0.000051842468,0.0000772444,0.00008897741,0.00003310172],"category_scores_gemma":[0.00006457971,0.00015406426,0.00003093622,0.00023563973,0.000024022678,0.00027574715,0.000015958272,0.00023890866,4.02303e-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":[0.000015327178,0.0000103200855,0.000021282573,0.000080143815,0.00013491615,0.00002370302,0.0001868252,0.98987323,0.008169247,0.0011674091,0.000023453282,0.00029412567],"study_design_scores_gemma":[0.00062012626,0.000056070043,0.00042761955,0.000075748765,0.00005507075,0.0016164943,0.000047566333,0.9947189,0.0018999975,0.00014773518,0.00016569407,0.0001689668],"about_ca_topic_score_codex":5.039037e-7,"about_ca_topic_score_gemma":2.724588e-7,"teacher_disagreement_score":0.2036966,"about_ca_system_score_codex":0.000053365526,"about_ca_system_score_gemma":0.000060530045,"threshold_uncertainty_score":0.62825584},"labels":[],"label_agreement":null},{"id":"W4213255064","doi":"10.1093/jcde/qwab069","title":"A heterogeneous lattice structure modeling technique supported by multiquadric radial basis function networks","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Topology Optimization in Engineering","field":"Engineering","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":"Academic Excellence Foundation of BUAA for PHD Students","keywords":"Cable gland; Lattice (music); Basis (linear algebra); Function (biology); Architectural geometry; Computer science; Mathematical optimization; Engineering; Topology (electrical circuits); Mathematics; Mechanical engineering; Engineering drawing; Geometry","score_opus":0.006829751502120535,"score_gpt":0.19207739596933734,"score_spread":0.1852476444672168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213255064","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":[0.03233774,0.0022040484,0.9647043,0.000027881173,0.0005183419,0.00007794848,0.0000049499085,0.00011980915,0.00000503213],"genre_scores_gemma":[0.8225833,0.0001185747,0.17706686,0.000024284567,0.00013870337,0.0000041856547,0.000017179158,0.000041824493,0.0000050391423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999097,0.000030185915,0.0004103748,0.00011117012,0.00016243546,0.00018884282],"domain_scores_gemma":[0.9994475,0.00014834147,0.000058173384,0.00006282195,0.00016982226,0.00011331435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014763407,0.00017820757,0.00023160195,0.00018035411,0.000044601875,0.000048578448,0.000068262496,0.0001417549,0.000030408219],"category_scores_gemma":[0.000035785186,0.00019939823,0.00005951423,0.00023022832,0.000010589962,0.0001987926,0.0000134303045,0.0003278917,3.8039565e-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":[0.000011947519,0.000007275847,0.000010689218,0.000034576893,0.000119041346,0.000032432712,0.00003819544,0.9885538,0.010355215,0.00005781112,0.00017349109,0.00060547586],"study_design_scores_gemma":[0.00036318804,0.000033666704,0.000048691843,0.000043165488,0.00004623468,0.000874,0.000007989629,0.99490047,0.0032101441,0.00018085723,0.00011402362,0.0001775552],"about_ca_topic_score_codex":3.696423e-7,"about_ca_topic_score_gemma":9.845583e-8,"teacher_disagreement_score":0.7902456,"about_ca_system_score_codex":0.00006530031,"about_ca_system_score_gemma":0.000030462434,"threshold_uncertainty_score":0.8131224},"labels":[],"label_agreement":null},{"id":"W4289885505","doi":"10.1093/jcde/qwac076","title":"A geometric modelling framework to support the design of heterogeneous lattice structures with non-linearly varying geometry","year":2022,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":29,"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; McGill University","keywords":"Geometric design; Geometric modeling; Lattice (music); Software; Computer Aided Design; Computer science; Geometric shape; Representation (politics); Geometric networks; Mathematics; Geometry","score_opus":0.02234133882158116,"score_gpt":0.21823323520863766,"score_spread":0.1958918963870565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289885505","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":[0.22843498,0.00022305196,0.7710635,0.000027304768,0.00010062639,0.00009343917,0.0000030642952,0.00005098151,0.0000030434041],"genre_scores_gemma":[0.63954014,0.00001293652,0.36037305,0.000015028638,0.000033362077,0.0000047485496,5.950105e-7,0.000018843331,0.0000012923152],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990667,0.000027399412,0.00030265134,0.00009180444,0.0003382006,0.00017320532],"domain_scores_gemma":[0.99880224,0.00087130547,0.00010297076,0.00008686742,0.00008236548,0.000054256016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004448598,0.0001434447,0.00022029711,0.00053616404,0.00010937301,0.000039995604,0.00023013722,0.00003687707,0.000012640479],"category_scores_gemma":[0.00006694913,0.00011055737,0.00004399563,0.00051562575,0.000019359295,0.000072304414,0.000067413355,0.0004416621,4.081066e-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":[0.000038723145,0.000007187828,0.000014288058,0.000038156635,0.0001158532,0.000024049328,0.00024085726,0.99612117,0.00016610841,0.00011225721,0.00004238966,0.003078957],"study_design_scores_gemma":[0.00018280647,0.00039825583,0.0003523055,0.000048148602,0.000032528675,0.00034974213,0.00005197216,0.9926932,0.0033016058,0.002375329,0.00007137677,0.0001427429],"about_ca_topic_score_codex":0.0000011883402,"about_ca_topic_score_gemma":6.5451013e-9,"teacher_disagreement_score":0.41110516,"about_ca_system_score_codex":0.00004655228,"about_ca_system_score_gemma":0.000028510754,"threshold_uncertainty_score":0.45083988},"labels":[],"label_agreement":null},{"id":"W4297688052","doi":"10.1093/jcde/qwac084","title":"TransNav: spatial sequential transformer network for visual navigation","year":2022,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":4,"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; Ministry of Natural Resources","keywords":"Computer science; Reinforcement learning; Artificial intelligence; Inference; Transformer; Machine learning; Engineering","score_opus":0.012894214952839501,"score_gpt":0.2588550867853863,"score_spread":0.24596087183254683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297688052","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":[0.03393207,0.00008362642,0.96494496,0.00059586245,0.00025113704,0.00015803454,0.0000022772883,0.000028209473,0.0000038491257],"genre_scores_gemma":[0.6491894,0.0000011892714,0.35061947,0.000036019035,0.00012291221,0.000016745777,0.000005762926,0.000006515208,0.0000019657016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991956,0.000048027196,0.00027781443,0.00009901646,0.0002545924,0.00012496985],"domain_scores_gemma":[0.99942625,0.00028172732,0.000109729626,0.000037577916,0.00008338376,0.00006135061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054579164,0.00007811671,0.000118608405,0.00009137658,0.00018939299,0.00005592181,0.00018033835,0.000017918279,0.0000059195645],"category_scores_gemma":[0.000009917725,0.000082302235,0.000068568435,0.00016035612,0.0000075249645,0.00018697786,0.00001484756,0.00019150726,3.134658e-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":[0.00002497507,0.000021088888,0.000024056844,0.000011751666,0.000024754083,0.0000022981799,0.00020900292,0.9763049,0.0015650028,0.008526666,0.00005710988,0.0132284],"study_design_scores_gemma":[0.00054656307,0.0002386568,0.0028107732,0.000009453625,0.000012022448,0.00015139401,0.000003943779,0.98947406,0.000107703345,0.005700796,0.0008582113,0.00008641226],"about_ca_topic_score_codex":0.00000692838,"about_ca_topic_score_gemma":6.524047e-8,"teacher_disagreement_score":0.6152574,"about_ca_system_score_codex":0.00004258366,"about_ca_system_score_gemma":0.000070507565,"threshold_uncertainty_score":0.33561876},"labels":[],"label_agreement":null},{"id":"W4303685875","doi":"10.1093/jcde/qwac107","title":"Twisted-fin parametric study to enhance the solidification performance of phase-change material in a shell-and-tube latent heat thermal energy storage system","year":2022,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Phase Change Materials Research","field":"Engineering","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":"Natural Resources Canada","funders":"","keywords":"Fin; Annular fin; Materials science; Latent heat; Thermal energy storage; Heat transfer; Mechanics; Phase-change material; Thermal conduction; Thermodynamics; Thermal; Heat transfer coefficient; Composite material; Physics","score_opus":0.04084439053015943,"score_gpt":0.2734285288958825,"score_spread":0.23258413836572306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303685875","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":[0.9670385,0.00040487517,0.031987175,0.00002679511,0.00026456284,0.00024888938,0.000009038957,0.000018126251,0.0000020141279],"genre_scores_gemma":[0.99903005,0.000037795075,0.00074283086,0.000006917832,0.000097472126,0.00006196806,0.0000027315432,0.000018943854,0.0000013119517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894804,0.00010851655,0.00038161222,0.000084684165,0.0003310158,0.00014614225],"domain_scores_gemma":[0.99962234,0.00014610637,0.000053850043,0.00006393711,0.00005588803,0.000057878246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079506554,0.00010665037,0.00021782228,0.00040114915,0.00005043128,0.000039366274,0.00013025702,0.000016389133,0.000008414056],"category_scores_gemma":[0.00001241624,0.000090650305,0.00002112273,0.00033972404,0.000008718114,0.00011729314,0.00005317495,0.00012475291,3.0577596e-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":[0.00014402333,0.00007656125,0.000042742548,0.000106332816,0.000032486754,0.000015734438,0.0012756691,0.9326301,0.06439602,0.000021705107,0.000008415431,0.0012501727],"study_design_scores_gemma":[0.0005649446,0.00057838723,0.005350659,0.00006158365,0.000011923917,0.00007816862,0.0002088899,0.98543453,0.007587104,0.000004728549,0.000023676937,0.00009538364],"about_ca_topic_score_codex":0.000016202612,"about_ca_topic_score_gemma":3.7746682e-7,"teacher_disagreement_score":0.056808915,"about_ca_system_score_codex":0.00013817596,"about_ca_system_score_gemma":0.000017540022,"threshold_uncertainty_score":0.3696612},"labels":[],"label_agreement":null},{"id":"W4307096839","doi":"10.1093/jcde/qwac109","title":"A novel bio-inspired approach with multi-resolution mapping for the path planning of multi-robot system in complex environments","year":2022,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Robotic Path Planning 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 Guelph","funders":"Shenzhen Graduate School, Peking University","keywords":"Motion planning; Computer science; Path (computing); Robot; Stability (learning theory); Computation; Interference (communication); Artificial intelligence; Algorithm; Machine learning","score_opus":0.06823519760274802,"score_gpt":0.23910233431687178,"score_spread":0.17086713671412376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307096839","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021492364,0.00031270916,0.99715644,0.00003227655,0.00009617816,0.00023433089,0.0000038739195,0.000014121079,8.554636e-7],"genre_scores_gemma":[0.414433,7.431597e-7,0.585524,0.0000077424775,0.000012967265,0.000012353483,0.000002064434,0.000005785061,0.0000013009919],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897456,0.000047796377,0.00036979557,0.00012836794,0.00032800977,0.00015148059],"domain_scores_gemma":[0.99922514,0.00034980843,0.00027190382,0.00007461051,0.000035789173,0.00004274492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080747553,0.00010681139,0.00020172047,0.00021592814,0.00012900365,0.00003240023,0.00028517467,0.00001944784,1.3518327e-7],"category_scores_gemma":[0.000019351453,0.000084247746,0.000038193182,0.00021627644,0.0000179207,0.00014348677,0.00006858224,0.0001627237,5.300295e-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":[0.000021559652,0.00007172609,0.0001768276,0.00004958272,0.000045632765,0.0000107171445,0.0010427345,0.9955264,0.0023228924,0.00022712752,0.0000070542605,0.00049779826],"study_design_scores_gemma":[0.0015333127,0.00012935759,0.03710134,0.00010217408,0.000009154661,0.00036633183,0.0002663539,0.9603581,0.000012852695,0.0000059004383,0.000025314948,0.000089835485],"about_ca_topic_score_codex":0.0000039277384,"about_ca_topic_score_gemma":1.2111428e-8,"teacher_disagreement_score":0.41228378,"about_ca_system_score_codex":0.00011185322,"about_ca_system_score_gemma":0.000046614572,"threshold_uncertainty_score":0.34355235},"labels":[],"label_agreement":null},{"id":"W4365457819","doi":"10.1093/jcde/qwad033","title":"Laser–tissue interaction simulation considering skin-specific data to predict photothermal damage lesions during laser irradiation","year":2023,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"National Research Foundation of Korea; Ministry of Trade, Industry and Energy; Ministry of Science and ICT, South Korea; Ministry of Food and Drug Safety; Korea Medical Device Development Fund; Ministry of Education; Ministry of Health and Welfare","keywords":"Photothermal therapy; Laser; Monte Carlo method; Optical coherence tomography; Materials science; Irradiation; Attenuation coefficient; Absorption (acoustics); Optics; Biomedical engineering; Radiation; Biological system; Mathematics; Composite material; Physics; Nanotechnology; Engineering; Statistics","score_opus":0.04653069397089487,"score_gpt":0.27719574750907555,"score_spread":0.23066505353818068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365457819","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":[0.4860259,0.00005109279,0.5133399,0.00005160873,0.00017903278,0.00016572025,0.0000136932385,0.0001505382,0.000022559117],"genre_scores_gemma":[0.9572206,0.000059038346,0.04245467,0.000009430479,0.00017676684,0.000008743571,0.000030879837,0.000034477496,0.000005369362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990038,0.000023427043,0.0004012248,0.00014630814,0.00024454662,0.00018064762],"domain_scores_gemma":[0.99902916,0.0005037821,0.00006606501,0.00015035893,0.000097369106,0.00015326242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028174106,0.00014196445,0.00016499835,0.00049201783,0.00008139465,0.00009445833,0.00015926638,0.000053375432,0.000021602898],"category_scores_gemma":[0.000052339237,0.00015658682,0.00003280729,0.00054324145,0.000012737068,0.0005742562,0.000045520177,0.00020721427,0.000023877317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013086482,0.000009937003,0.000036484023,0.00003191148,0.0000406347,0.000010687846,0.00014954303,0.98370284,0.0117510045,0.000040205185,0.0002611973,0.0039524822],"study_design_scores_gemma":[0.00029460998,0.000036439997,0.0402248,0.00008435839,0.00001692267,0.000029496843,0.000041527925,0.9564756,0.0015549804,0.000114134746,0.0009831019,0.00014401226],"about_ca_topic_score_codex":8.048445e-7,"about_ca_topic_score_gemma":3.84967e-7,"teacher_disagreement_score":0.4711947,"about_ca_system_score_codex":0.00005803082,"about_ca_system_score_gemma":0.000016982201,"threshold_uncertainty_score":0.63854253},"labels":[],"label_agreement":null},{"id":"W4367693552","doi":"10.1093/jcde/qwad039","title":"Hybrid neural network-based metaheuristics for prediction of financial markets: a case study on global gold market","year":2023,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","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":"University of Victoria","funders":"","keywords":"Metaheuristic; Firefly algorithm; Artificial neural network; Computer science; Hyperparameter; Novelty; Artificial intelligence; Machine learning; Convolutional neural network; Particle swarm optimization","score_opus":0.09818076663270471,"score_gpt":0.35188170961076126,"score_spread":0.25370094297805657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367693552","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":[0.37146848,0.00003086991,0.6275567,0.000036579466,0.0006385874,0.00019687734,0.0000400555,0.000016492191,0.000015388838],"genre_scores_gemma":[0.86281943,0.000001015028,0.13688461,0.00002398416,0.00023407172,0.000006819261,0.000001621151,0.000010526194,0.000017897375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764526,0.0003316688,0.00085597753,0.00017050428,0.0008130971,0.0001834669],"domain_scores_gemma":[0.98917484,0.009716234,0.00040309937,0.00009708541,0.00050601043,0.00010270192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007978516,0.00013683157,0.00035202855,0.0003204384,0.00007093391,0.000072166564,0.00017018477,0.000034765533,0.000006347536],"category_scores_gemma":[0.006729967,0.00011335882,0.00013263783,0.0006237518,0.000022136092,0.00011259342,0.00003082472,0.00012674269,4.5161826e-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":[0.0005202452,0.00004868225,0.0020532373,0.000014617537,0.00003835107,0.00035049292,0.000035301247,0.96529865,0.000008123339,0.00006117992,0.011667336,0.019903777],"study_design_scores_gemma":[0.0009674609,0.000699633,0.13357149,0.000040623327,0.000047381756,0.00080326374,0.00003335557,0.85808825,0.0000048751635,0.0055292253,0.00014418055,0.00007027613],"about_ca_topic_score_codex":9.5333286e-7,"about_ca_topic_score_gemma":1.5364363e-7,"teacher_disagreement_score":0.49135098,"about_ca_system_score_codex":0.00004395278,"about_ca_system_score_gemma":0.00011545557,"threshold_uncertainty_score":0.80568856},"labels":[],"label_agreement":null},{"id":"W4392180792","doi":"10.1093/jcde/qwae020","title":"Revolutionizing the latent heat storage: Boosting discharge performance with innovative undulated phase change material containers in a vertical shell-and-tube system","year":2024,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Phase Change Materials Research","field":"Engineering","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":"Natural Resources Canada","funders":"Engineering and Physical Sciences Research Council","keywords":"Latent heat; Phase-change material; Boosting (machine learning); Phase change; Tube (container); Shell (structure); Materials science; Thermal energy storage; Engineering; Structural engineering; Composite material; Computer science; Engineering physics; Physics; Artificial intelligence; Meteorology; Thermodynamics","score_opus":0.03697011949127055,"score_gpt":0.2572042458758056,"score_spread":0.22023412638453504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392180792","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":[0.8948225,0.0010381729,0.10348924,0.00013552982,0.00026926954,0.00017915132,0.0000049656546,0.000056910456,0.0000042704837],"genre_scores_gemma":[0.99821156,0.000066400295,0.0014720443,0.0000072616835,0.00019564884,0.00001529342,0.000004954198,0.00002584865,9.695527e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919695,0.000038922677,0.0002750204,0.00008646348,0.000210528,0.00019213642],"domain_scores_gemma":[0.99963933,0.00015768596,0.000016121172,0.000033947385,0.00008960767,0.000063319385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005301895,0.00012984234,0.0001914042,0.00024272951,0.000047212077,0.00014261255,0.000057404646,0.000036812537,0.000003110156],"category_scores_gemma":[0.000019206609,0.00008997391,0.000014296877,0.00033022923,0.000025613894,0.00032619794,0.000020911233,0.00022694671,8.333977e-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":[0.00017338333,0.00001267082,0.00006268311,0.00081512745,0.00010964267,0.00016551778,0.0010942308,0.93550617,0.06067126,0.0006257415,0.000024295505,0.00073928764],"study_design_scores_gemma":[0.0006715168,0.00014887756,0.0017464722,0.0011012292,0.00001469544,0.00043843707,0.00007145106,0.99409896,0.0015620767,0.000010875475,0.000028515582,0.00010689522],"about_ca_topic_score_codex":0.0000042714,"about_ca_topic_score_gemma":2.2731501e-7,"teacher_disagreement_score":0.10338909,"about_ca_system_score_codex":0.00018180093,"about_ca_system_score_gemma":0.000032602646,"threshold_uncertainty_score":0.36690295},"labels":[],"label_agreement":null},{"id":"W4411020509","doi":"10.1093/jcde/qwaf053","title":"Optimizing image format piping and instrumentation diagram recognition: Integrating symbol and text recognition with a single backbone architecture","year":2025,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Handwritten Text Recognition Techniques","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":"ASTER","funders":"Ministry of Science and ICT, South Korea; Ministry of Education, India; National Research Foundation; National Research Foundation of Korea; Ministry of Education","keywords":"Symbol (formal); Instrumentation (computer programming); Architecture; Piping; Diagram; Computer science; Engineering drawing; Artificial intelligence; Pattern recognition (psychology); Computer vision; Speech recognition; Natural language processing; Engineering; Mechanical engineering; Programming language; Database","score_opus":0.011713344175164665,"score_gpt":0.2171274700654885,"score_spread":0.20541412589032382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411020509","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08463982,0.00027687257,0.91442615,0.0003161984,0.000054023967,0.00015424303,0.0000014601045,0.000063693486,0.0000675656],"genre_scores_gemma":[0.3937157,0.00007019542,0.60609657,0.00007639756,0.000022974347,0.0000066223743,0.0000039868905,0.0000060205384,0.0000015031741],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991594,0.00005177348,0.00034360008,0.00015078258,0.00016415199,0.0001302873],"domain_scores_gemma":[0.9991323,0.00034266865,0.00017039017,0.000042627664,0.00023605986,0.00007600249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035575955,0.00014427942,0.00019168148,0.00041690524,0.000111208254,0.00035732446,0.00007544331,0.000047792684,0.0000018199657],"category_scores_gemma":[0.000058094713,0.00012620525,0.000025930018,0.00025503227,0.00003544231,0.0010000651,0.0000447761,0.00022169862,3.9182817e-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":[0.0000499816,0.000037451446,0.0000273132,0.00018735732,0.00008490986,0.000020186455,0.001389123,0.0109551605,0.007951493,0.00034044616,0.00003473376,0.97892183],"study_design_scores_gemma":[0.0018839142,0.0007737777,0.0010730182,0.0029374808,0.000081930426,0.002043938,0.00041678545,0.9394852,0.015727393,0.035094697,0.000048986793,0.00043288703],"about_ca_topic_score_codex":0.0000017564566,"about_ca_topic_score_gemma":3.84868e-7,"teacher_disagreement_score":0.978489,"about_ca_system_score_codex":0.000040914605,"about_ca_system_score_gemma":0.000040699782,"threshold_uncertainty_score":0.5146501},"labels":[],"label_agreement":null},{"id":"W4414861064","doi":"10.1093/jcde/qwaf100","title":"Optimizing Markov decision process state design for deep reinforcement learning manufacturing scheduling using Bayesian optimization","year":2025,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"Incheon National University","keywords":"Reinforcement learning; Markov decision process; Scheduling (production processes); Job shop scheduling; Dynamic priority scheduling; Bayesian optimization; Feature selection; Partially observable Markov decision process","score_opus":0.011097002070480085,"score_gpt":0.23587606496968183,"score_spread":0.22477906289920174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414861064","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00970579,0.00059287914,0.98900175,0.00001337704,0.0002923376,0.00029199306,2.9169283e-7,0.000087124725,0.000014427111],"genre_scores_gemma":[0.4713503,0.00009317391,0.5284729,0.000008621293,0.000035840614,0.000006030664,0.0000028260722,0.000025112546,0.0000051917923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987321,0.000023444838,0.0006153631,0.00014745494,0.00023379203,0.00024783818],"domain_scores_gemma":[0.99904156,0.00042928508,0.00017061549,0.000052222415,0.00021644315,0.00008985686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005517954,0.00022627832,0.00028252043,0.00051454455,0.0001711063,0.00017344425,0.0001163923,0.000077867946,0.0000062063123],"category_scores_gemma":[0.00009438262,0.00023356536,0.00006682209,0.00017406982,0.000010694731,0.00047407023,0.000019291925,0.00022763289,1.2690568e-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":[0.00010084712,0.000006959346,0.000004603923,0.00035771166,0.00009082073,0.0000040702334,0.0002478017,0.991365,0.00013444961,0.000014534039,0.0000049932873,0.007668205],"study_design_scores_gemma":[0.0008005261,0.00006381303,0.000029697268,0.00053155766,0.00005371706,0.000025039346,0.00004306616,0.99214894,0.0052920966,0.0007778994,0.000019027264,0.0002146142],"about_ca_topic_score_codex":4.2127223e-7,"about_ca_topic_score_gemma":3.8339195e-8,"teacher_disagreement_score":0.46164453,"about_ca_system_score_codex":0.00013263182,"about_ca_system_score_gemma":0.00006337168,"threshold_uncertainty_score":0.9524519},"labels":[],"label_agreement":null},{"id":"W4415502605","doi":"10.1093/jcde/qwaf112","title":"AR-RBMO: An enhanced red-billed blue magpie optimizer with attraction-repulsion and dynamic balancing strategies for global optimization","year":2025,"lang":"en","type":"article","venue":"Journal of Computational Design and Engineering","topic":"Metaheuristic Optimization Algorithms Research","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":"Lakehead University","funders":"National Natural Science Foundation of China","keywords":"Benchmark (surveying); Global optimization; Metaheuristic; Flexibility (engineering); Convergence (economics); Population; Robustness (evolution); Swarm intelligence; Optimization problem; Wilcoxon signed-rank test","score_opus":0.010517179620667231,"score_gpt":0.2738454120282298,"score_spread":0.2633282324075626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415502605","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053545814,0.00017312929,0.9937232,0.00029782284,0.00017406032,0.00021650735,0.0000014752338,0.00003480415,0.000024378298],"genre_scores_gemma":[0.27856892,0.00005398161,0.7213016,0.000024449206,0.000020516827,0.0000062624213,0.0000038384637,0.0000062038257,0.000014204351],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989577,0.000053862983,0.00035441841,0.00019620023,0.00028280987,0.00015499089],"domain_scores_gemma":[0.9987854,0.00034804095,0.0001558982,0.00008800354,0.0005170208,0.00010566595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004888595,0.0001326392,0.00021292371,0.00022061451,0.000114145594,0.0003622787,0.00016126114,0.00004993131,0.00000295342],"category_scores_gemma":[0.00009755179,0.00011477067,0.000028675511,0.00033253361,0.0000236758,0.0009217534,0.0000326875,0.00011578897,8.738183e-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":[0.00007532337,0.000024617848,0.000008742735,0.000043465498,0.00004747898,0.000004835906,0.00005794509,0.99127156,0.00031145776,0.002764681,0.000022896209,0.005366977],"study_design_scores_gemma":[0.0010556962,0.00021492294,0.0012027298,0.00010051887,0.000019677334,0.000095139345,0.00004730004,0.9947332,0.000052214175,0.0023498142,0.0000160865,0.000112664566],"about_ca_topic_score_codex":9.899902e-7,"about_ca_topic_score_gemma":2.2890937e-7,"teacher_disagreement_score":0.27321434,"about_ca_system_score_codex":0.0000815904,"about_ca_system_score_gemma":0.0002358113,"threshold_uncertainty_score":0.4680212},"labels":[],"label_agreement":null}]}