{"meta":{"query_hash":"f846d111e812","filters":{"venue":"IEEE Transactions on Intelligent Vehicles"},"cohort_total":72,"direct_labels_cover":1,"predictions_cover":72,"exported":72,"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/f846d111e812","api":"https://metacan.xera.ac/api/v1/cohort?venue=IEEE+Transactions+on+Intelligent+Vehicles"},"results":[{"id":"W2617375482","doi":"10.1109/tiv.2017.2708604","title":"Novel Communication Protocol for the EV Charging/Discharging Service Based on VANETs","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Computer network; Telecommunications link; Channel (broadcasting); Reservation; Frame (networking); Smart grid; Protocol (science); Service (business); Control channel; Vehicular ad hoc network; Wireless; Wireless ad hoc network; Telecommunications; Engineering; Electrical engineering","score_opus":0.050039005331422436,"score_gpt":0.30032369189424984,"score_spread":0.25028468656282743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2617375482","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":[0.0017424447,0.000019954477,0.9536134,0.0018996233,0.00044501227,0.041240294,0.000066330766,0.00041074288,0.00056220347],"genre_scores_gemma":[0.8796503,0.00002676439,0.0022992427,0.000509318,0.00013407881,0.11710762,0.000010264348,0.00012070635,0.00014168714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985501,0.000043024487,0.0003627177,0.00030026364,0.00030657795,0.00043729614],"domain_scores_gemma":[0.9975538,0.00049403857,0.0001252986,0.0015969928,0.00012179174,0.00010804308],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00042853953,0.0003297014,0.00022146883,0.00011659547,0.001338852,0.00028392932,0.0009793944,0.00014868221,0.00011015714],"category_scores_gemma":[0.000012021428,0.0002728785,0.00019151009,0.00011417559,0.00008073719,0.00022414482,0.0000033808415,0.0005492003,0.00012926676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014046466,0.00016174115,0.00000679863,0.000121996665,0.000079755264,6.45122e-7,0.00018310527,0.9732158,0.0029286048,0.00009891258,0.00039581582,0.022666328],"study_design_scores_gemma":[0.0007937559,0.00007609879,0.00012913515,0.00035403774,0.000047640944,0.000003812984,0.000055457447,0.89575523,0.081637464,0.000052595227,0.020814514,0.00028025146],"about_ca_topic_score_codex":0.00010080999,"about_ca_topic_score_gemma":0.0004184321,"teacher_disagreement_score":0.95131415,"about_ca_system_score_codex":0.00015071477,"about_ca_system_score_gemma":0.000034158787,"threshold_uncertainty_score":0.99997234},"labels":[],"label_agreement":null},{"id":"W2781838712","doi":"10.1109/tiv.2017.2788186","title":"${{\\mathcal L}_1}$ Adaptive Control of Vehicle Lateral Dynamics","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","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":"Robustness (evolution); Control theory (sociology); Adaptive control; Controller (irrigation); Computer science; Vehicle dynamics; Transient (computer programming); Control signal; Trajectory; Control engineering; Engineering; Control system; Control (management); Physics; Aerospace engineering; Artificial intelligence; Biology","score_opus":0.010313758224884381,"score_gpt":0.21071641742033514,"score_spread":0.20040265919545075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781838712","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":[0.34467158,0.00006229206,0.65286726,0.00004684194,0.00084264594,0.00024019898,0.00016425228,0.00020374176,0.0009011777],"genre_scores_gemma":[0.99923545,0.00004916815,0.00016014428,0.000057223453,0.000145051,0.00004989891,0.0000029182438,0.00006272193,0.00023742414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984944,0.000056490484,0.00053186214,0.00025628193,0.000280299,0.0003806987],"domain_scores_gemma":[0.9991891,0.00010852216,0.000067968445,0.00033699215,0.000167475,0.00012996129],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016383658,0.00027337015,0.00037364074,0.00018587515,0.0001238155,0.000037031023,0.00025170378,0.0001753598,0.00011733769],"category_scores_gemma":[0.000002038163,0.00026943925,0.0002179499,0.00022805852,0.00017271184,0.00012728933,0.0000013083061,0.00028785388,0.00018116499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016581446,0.00093988836,0.0011205928,0.00024848283,0.0018566297,0.00003320364,0.0023124157,0.5674942,0.066481695,0.0105625605,0.0002763015,0.3470159],"study_design_scores_gemma":[0.00062072923,0.00045639992,0.00030347897,0.00007503221,0.000056027915,0.000008702407,0.00016451339,0.9555277,0.042080212,0.00023742166,0.00020231621,0.0002674306],"about_ca_topic_score_codex":0.0001212038,"about_ca_topic_score_gemma":0.00035242655,"teacher_disagreement_score":0.65456384,"about_ca_system_score_codex":0.00017179234,"about_ca_system_score_gemma":0.000022747943,"threshold_uncertainty_score":0.9999758},"labels":[],"label_agreement":null},{"id":"W2782064812","doi":"10.1109/tiv.2017.2788193","title":"Understanding Pedestrian Behavior in Complex Traffic Scenes","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":266,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Pedestrian; Schema crosswalk; Context (archaeology); Pedestrian crossing; Computer science; Transport engineering; Demographics; Meaning (existential); Human–computer interaction; Psychology; Geography; Engineering","score_opus":0.10816409996401001,"score_gpt":0.27629304003212424,"score_spread":0.16812894006811424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782064812","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":[0.6349203,0.000036106016,0.36289498,0.00009259599,0.00039130752,0.00021824466,0.000016273008,0.00075701525,0.00067319255],"genre_scores_gemma":[0.99923,0.0001610724,0.0003614305,0.000037377595,0.000046153502,0.000052693897,0.0000027745773,0.000042108935,0.00006639106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99891126,0.000030000278,0.00033777626,0.00023917561,0.00012312736,0.000358662],"domain_scores_gemma":[0.99956936,0.00006796974,0.000023950215,0.0002469777,0.000020696794,0.00007103334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012268787,0.0002119921,0.00020421343,0.0003795582,0.00020559871,0.000025742285,0.00020477024,0.00021826167,0.00037808137],"category_scores_gemma":[0.0000016323262,0.00022937985,0.00009261024,0.0003523914,0.00024923368,0.00010397351,0.0000011137818,0.00039338123,0.00025298574],"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":[0.000532479,0.0015868265,0.0026086494,0.00017861053,0.00031151724,0.00012509669,0.0038879944,0.52310556,0.030655213,0.003056026,0.00086758164,0.43308446],"study_design_scores_gemma":[0.0018571345,0.0012672036,0.009392626,0.00021077685,0.00018562905,0.00010600858,0.0037482036,0.4508335,0.5245865,0.0019745037,0.0040662503,0.0017716731],"about_ca_topic_score_codex":0.000021150478,"about_ca_topic_score_gemma":0.00084653683,"teacher_disagreement_score":0.4939313,"about_ca_system_score_codex":0.0003336174,"about_ca_system_score_gemma":0.000020122168,"threshold_uncertainty_score":0.9353839},"labels":[],"label_agreement":null},{"id":"W2805404896","doi":"10.1109/tiv.2018.2843126","title":"Electric Vehicle Charging Scheme for a Park-and-Charge System Considering Battery Degradation Costs","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":129,"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; China Scholarship Council","keywords":"Battery (electricity); Minification; Mathematical optimization; Benchmark (surveying); Computer science; Job shop scheduling; Degradation (telecommunications); Scheduling (production processes); State of charge; Electric vehicle; Automotive engineering; Power (physics); Operating cost; Reliability engineering; Engineering; Schedule; Mathematics","score_opus":0.01458701445206027,"score_gpt":0.22400848764264306,"score_spread":0.2094214731905828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805404896","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":[0.5034178,0.00028292698,0.49458936,0.000066421875,0.00051500986,0.00039588282,0.000026369846,0.0004679799,0.00023825694],"genre_scores_gemma":[0.99764365,0.00020683481,0.0015192432,0.00011792608,0.00021578108,0.00012948953,0.000004724758,0.000074779025,0.00008758468],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99857134,0.000029564913,0.00039183587,0.0003356097,0.00018368984,0.00048797528],"domain_scores_gemma":[0.99929917,0.00017163083,0.0000558976,0.00022499844,0.00011385716,0.00013443542],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017363927,0.00028837833,0.0002634059,0.0002821217,0.00036292768,0.00009425746,0.00013017123,0.00016015375,0.000058638736],"category_scores_gemma":[0.0000060941975,0.0002929575,0.00011071897,0.00033722506,0.00005143058,0.0002107772,0.0000020578468,0.00029639443,0.00007645715],"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":[0.00025390944,0.00010136804,0.00037862512,0.00065005536,0.0004417777,0.000007077874,0.00071584765,0.006376411,0.62929827,0.0020793334,0.001644072,0.35805327],"study_design_scores_gemma":[0.00029297793,0.00017605851,0.000079267884,0.00012190442,0.00003970128,0.00003187108,0.00011627741,0.37256065,0.62536716,0.00006463264,0.00088520645,0.00026429247],"about_ca_topic_score_codex":0.000021994958,"about_ca_topic_score_gemma":0.000018356479,"teacher_disagreement_score":0.49422583,"about_ca_system_score_codex":0.00028662174,"about_ca_system_score_gemma":0.000024290479,"threshold_uncertainty_score":0.99995226},"labels":[],"label_agreement":null},{"id":"W2807126442","doi":"10.1109/tiv.2018.2843159","title":"Balancing Computation Speed and Quality: A Decentralized Motion Planning Method for Cooperative Lane Changes of Connected and Automated Vehicles","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Traffic control and management","field":"Engineering","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Platoon; Collision avoidance; Computer science; Quality (philosophy); Collision; Distributed computing; Mathematical optimization; Real-time computing; Control (management); Artificial intelligence; Mathematics; Physics","score_opus":0.03684129033571141,"score_gpt":0.3136284965105849,"score_spread":0.27678720617487346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807126442","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":[0.35679996,0.00014694144,0.64210343,0.00007631899,0.00015098468,0.00032793582,0.00003763213,0.00034024534,0.000016530075],"genre_scores_gemma":[0.9919722,0.00013701362,0.007745517,0.000043624306,0.000027154474,0.000027539745,0.000010628715,0.00002078229,0.000015554588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992061,0.000071264076,0.0002627114,0.00018875474,0.00009949841,0.00017161857],"domain_scores_gemma":[0.99940777,0.00031033857,0.000052196323,0.00007021092,0.00010369493,0.000055813285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002336589,0.00015196981,0.00025072283,0.00015100611,0.00010983817,0.00003452806,0.000038874095,0.000065078944,0.000008544949],"category_scores_gemma":[0.000009642752,0.00014771026,0.000032849635,0.00012302981,0.000058308335,0.00006401369,0.0000012959556,0.00007145129,0.0000014763543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005798346,0.00012738192,0.00006683473,0.0005085094,0.0005380283,0.000001525563,0.0055520157,0.43107596,0.1651963,0.000520336,0.0001284009,0.39570487],"study_design_scores_gemma":[0.0009844676,0.00025822246,0.0016771682,0.00011932576,0.00008545638,0.000002718033,0.0007438997,0.83738995,0.15832879,0.00006068362,0.00018715832,0.00016217821],"about_ca_topic_score_codex":0.00007336334,"about_ca_topic_score_gemma":0.00021645217,"teacher_disagreement_score":0.63517225,"about_ca_system_score_codex":0.000032699314,"about_ca_system_score_gemma":0.000005778254,"threshold_uncertainty_score":0.602345},"labels":[],"label_agreement":null},{"id":"W2897053701","doi":"10.1109/tiv.2018.2874555","title":"Estimation of Steering Angle and Collision Avoidance for Automated Driving Using Deep Mixture of Experts","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Artificial intelligence; Robustness (evolution); Computer science; Particle filter; Obstacle avoidance; Parametric statistics; Computer vision; Convolutional neural network; Monocular; Pattern recognition (psychology); Kalman filter; Mathematics; Mobile robot; Robot; Statistics","score_opus":0.013681455766689124,"score_gpt":0.2553113560878842,"score_spread":0.24162990032119508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897053701","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":[0.4946472,0.00008895802,0.50476617,0.0000060197353,0.00011728824,0.00012772612,0.000009287181,0.00022698056,0.000010403581],"genre_scores_gemma":[0.982981,0.00007211422,0.016874397,0.0000048770967,0.000013491953,0.00001795503,0.0000012037835,0.000025414924,0.000009500332],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992938,0.000013965641,0.00031536713,0.0001443672,0.00008130118,0.00015118466],"domain_scores_gemma":[0.9995669,0.000110763154,0.00006450158,0.0001572895,0.0000694259,0.000031128184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010300064,0.00013020281,0.00019821417,0.00016140057,0.00012124871,0.000007010983,0.00008528012,0.00016254974,0.000011688047],"category_scores_gemma":[0.0000073455158,0.00013560217,0.00005544176,0.00016506294,0.00013952376,0.000112437796,0.0000015716129,0.00008998698,0.0000012595004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005338272,0.00005715036,0.000053063373,0.00014382179,0.000062069244,3.868113e-7,0.0008762164,0.7539932,0.157418,0.000077967845,0.000011463168,0.08725329],"study_design_scores_gemma":[0.000084344196,0.00007965795,0.00007440884,0.00009152905,0.000016094333,0.0000030067781,0.00009257684,0.51695365,0.48246592,0.0000592449,0.000016349642,0.00006323468],"about_ca_topic_score_codex":0.000010975138,"about_ca_topic_score_gemma":0.00003457958,"teacher_disagreement_score":0.48833385,"about_ca_system_score_codex":0.000053836608,"about_ca_system_score_gemma":0.0000112792495,"threshold_uncertainty_score":0.5529696},"labels":[],"label_agreement":null},{"id":"W2903665206","doi":"10.1109/tiv.2018.2886679","title":"Cooperative Estimation of Road Condition Based on Dynamic Consensus and Vehicular Communication","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Distributed Control Multi-Agent Systems","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Reliability (semiconductor); Computer science; Scheme (mathematics); Fuse (electrical); Identification (biology); Collision avoidance; Estimation; Control theory (sociology); Real-time computing; Collision; Control (management); Engineering; Artificial intelligence; Mathematics; Statistics; Computer security","score_opus":0.016244338440878293,"score_gpt":0.2743959656882275,"score_spread":0.2581516272473492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903665206","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":[0.15146124,0.000041551182,0.84704554,0.0004905124,0.00022904396,0.00042002194,0.00006882379,0.000113928705,0.00012933099],"genre_scores_gemma":[0.99530315,0.000021876134,0.0043797772,0.00015163222,0.0000075198195,0.0000612841,0.000019278797,0.000012462087,0.000043004882],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844027,0.0003002783,0.000427458,0.00034232502,0.00031149204,0.00017815447],"domain_scores_gemma":[0.9984137,0.00034710206,0.00018922878,0.0006616877,0.00030801023,0.00008029025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000322837,0.00019095586,0.00022567259,0.00022867166,0.0002797084,0.000091436414,0.0003443474,0.00010399353,0.000023839433],"category_scores_gemma":[0.000023984525,0.00018590433,0.00007580094,0.00029962117,0.00027789877,0.00017427836,0.000003363422,0.00017464199,0.000083339844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004404486,0.0012595218,0.000049869508,0.00010131825,0.00023846127,0.000007907859,0.0017921522,0.6625536,0.028809728,0.0070163547,0.0001854228,0.2975452],"study_design_scores_gemma":[0.00051780336,0.0004737387,0.0004458816,0.00015960702,0.000025222784,0.000005350509,0.000079735524,0.86557573,0.13230513,0.00021008716,0.000052718562,0.00014896001],"about_ca_topic_score_codex":0.000110778754,"about_ca_topic_score_gemma":0.000066092194,"teacher_disagreement_score":0.8438419,"about_ca_system_score_codex":0.00011629987,"about_ca_system_score_gemma":0.00006102473,"threshold_uncertainty_score":0.75809586},"labels":[],"label_agreement":null},{"id":"W2981402159","doi":"10.1109/tiv.2020.3012947","title":"Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Control","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Traffic control and management","field":"Engineering","cited_by":224,"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":"Reinforcement learning; Model predictive control; Cruise control; Generalization; Benchmark (surveying); Computer science; Range (aeronautics); Control theory (sociology); Control (management); Artificial intelligence; Engineering; Mathematics","score_opus":0.02467582414128084,"score_gpt":0.25109501273078977,"score_spread":0.22641918858950894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981402159","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":[0.0042903456,0.00031365285,0.9936877,0.00016111687,0.00012010477,0.0009304587,0.000040103696,0.00023253962,0.00022400524],"genre_scores_gemma":[0.9988576,0.00012336667,0.000577785,0.00010349803,0.00003079652,0.00023827392,0.0000021166836,0.000029773968,0.000036767953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989929,0.000023748935,0.00039312607,0.00020702543,0.00016482703,0.00021837707],"domain_scores_gemma":[0.99946463,0.00019407892,0.00006206306,0.000082231076,0.0000729902,0.00012399461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000781889,0.00019669849,0.00036014107,0.00008145125,0.000093948925,0.0000177964,0.00008690255,0.00006630579,0.00002130267],"category_scores_gemma":[0.0000064182336,0.00019681502,0.00013165364,0.000070695525,0.00005070061,0.00007684783,0.0000010389388,0.00021156942,0.0000054432157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009833772,0.00006349329,0.000015630616,0.00008859214,0.00038010828,3.9511494e-7,0.0014565862,0.9545872,0.002035472,0.00025678295,0.000050256887,0.040082112],"study_design_scores_gemma":[0.0018301469,0.00088102807,0.000020295529,0.00002944623,0.0002385853,2.2415904e-7,0.0009124122,0.98349786,0.012057308,0.00004523788,0.00032201147,0.0001654567],"about_ca_topic_score_codex":0.0000047743524,"about_ca_topic_score_gemma":0.000013063439,"teacher_disagreement_score":0.9945673,"about_ca_system_score_codex":0.000047834616,"about_ca_system_score_gemma":0.000009740995,"threshold_uncertainty_score":0.80258834},"labels":[],"label_agreement":null},{"id":"W2990691480","doi":"10.1109/tiv.2019.2955370","title":"A Novel Electric Vehicles Charging/Discharging Management Protocol Based on Queuing Model","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":155,"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":"Electric vehicle; Queueing theory; Vehicle-to-grid; Grid; Charging station; Smart grid; Computer science; Automotive engineering; Schedule; Simulation; Electrical engineering; Engineering; Power (physics); Computer network; Operating system","score_opus":0.011809219364435245,"score_gpt":0.23107866229857627,"score_spread":0.21926944293414102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990691480","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":[0.055590063,0.000019711591,0.9225145,0.00010854335,0.0003010352,0.01663435,0.000028778697,0.0008340175,0.0039689797],"genre_scores_gemma":[0.9837386,0.0000330441,0.0040974594,0.00039489323,0.00006479219,0.010888081,0.000004071223,0.00014628534,0.00063277705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976336,0.000030312733,0.0005112895,0.00055620144,0.0005397155,0.0007289219],"domain_scores_gemma":[0.9990654,0.000088223445,0.00007510715,0.0005486919,0.000059588165,0.00016300828],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001955172,0.0005107633,0.00035021,0.00067127304,0.00021634951,0.00011927928,0.00038312815,0.0001977722,0.0002391814],"category_scores_gemma":[0.0000015621089,0.0004898713,0.00025336936,0.0006965384,0.00002428199,0.00021344675,0.0000027130188,0.0007893913,0.00032737802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014925556,0.00020194598,0.000023892879,0.0002630013,0.000095415715,0.0000038145379,0.00010997943,0.8887797,0.05088009,0.00052958535,0.00017372394,0.058789626],"study_design_scores_gemma":[0.00069669104,0.00019724088,0.000053107302,0.00021502814,0.000031628537,0.0000050816175,0.000043192857,0.7433109,0.25388056,0.00018230079,0.00096675806,0.0004174691],"about_ca_topic_score_codex":0.000012694633,"about_ca_topic_score_gemma":0.0000044726544,"teacher_disagreement_score":0.9281485,"about_ca_system_score_codex":0.00035733217,"about_ca_system_score_gemma":0.000038925013,"threshold_uncertainty_score":0.9997553},"labels":[],"label_agreement":null},{"id":"W2996542533","doi":"10.1109/tiv.2019.2960930","title":"Enhancing Driver Distraction Recognition Using Generative Adversarial Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":47,"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":"Convolutional neural network; Distraction; Computer science; Discriminative model; Generative grammar; Artificial intelligence; Machine learning; Distracted driving; Set (abstract data type); Generative model; Pattern recognition (psychology)","score_opus":0.016570158871331255,"score_gpt":0.22493342094319124,"score_spread":0.20836326207185998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996542533","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":[0.44563487,0.00002861293,0.5523808,0.0000122606925,0.0012188348,0.00017221118,0.000013353881,0.00036419687,0.00017486908],"genre_scores_gemma":[0.99763006,0.00019717011,0.001841968,0.000042145355,0.000119044234,0.000021486156,0.000011212971,0.000043481243,0.00009344651],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894,0.00004137403,0.0003250792,0.00027057427,0.0001285848,0.00029439965],"domain_scores_gemma":[0.99954003,0.000086315755,0.00005146607,0.00021509282,0.00004672766,0.000060347928],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011832662,0.00022506613,0.00020085077,0.00016116787,0.00018653518,0.00002760552,0.00010775815,0.00030425226,0.0004678093],"category_scores_gemma":[0.0000016309205,0.00024556724,0.00013072841,0.00019477785,0.000056346435,0.00026640148,0.0000012132838,0.0005769691,0.0004396873],"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":[0.00008251448,0.00006414125,0.00004337178,0.000017784228,0.000115776624,0.000003356788,0.00018599894,0.8822251,0.053975955,0.000027463002,0.000015047033,0.0632435],"study_design_scores_gemma":[0.0002142328,0.00007802382,0.000098272976,0.000053575935,0.00004945539,0.000012588555,0.00019175524,0.49020544,0.5085044,0.00014919552,0.00018642125,0.00025662544],"about_ca_topic_score_codex":0.000029939427,"about_ca_topic_score_gemma":0.000071944225,"teacher_disagreement_score":0.55199516,"about_ca_system_score_codex":0.00026588267,"about_ca_system_score_gemma":0.000020470823,"threshold_uncertainty_score":0.99999964},"labels":[],"label_agreement":null},{"id":"W3034269714","doi":"10.1109/tiv.2020.3002505","title":"Learning to Drive by Imitation: An Overview of Deep Behavior Cloning Methods","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":119,"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":"Computer science; Modular design; Artificial intelligence; Affordance; Pipeline (software); Deep learning; Orientation (vector space); Task (project management); Human–computer interaction; Machine learning; Systems engineering; Engineering","score_opus":0.05395145152887176,"score_gpt":0.33015214742734866,"score_spread":0.2762006958984769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034269714","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":[0.22387104,0.000380863,0.7745942,0.0001719194,0.00013713846,0.0001947508,0.0000147083965,0.00054668577,0.000088692235],"genre_scores_gemma":[0.97687376,0.00039722992,0.022383075,0.00016727712,0.000018582088,0.00008172124,0.00000523982,0.00004576111,0.000027358816],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989091,0.00010058975,0.0003821805,0.00025621903,0.0001268623,0.00022502425],"domain_scores_gemma":[0.99943215,0.00012528992,0.00004561966,0.00018569206,0.00005283453,0.00015844508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001614146,0.00019455454,0.0002644173,0.000116769486,0.0001314894,0.000014715084,0.00021758294,0.0001703006,0.00018304148],"category_scores_gemma":[0.000009770971,0.00021692952,0.000112805,0.00034393158,0.00005597415,0.00013739153,0.000002210161,0.00049414363,0.00007038296],"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":[0.000036273137,0.000083959065,0.00006706535,0.000047504785,0.00006697882,0.0000027052834,0.0034470197,0.3369323,0.077262044,0.000113056885,0.000025082774,0.58191603],"study_design_scores_gemma":[0.0001324444,0.0005572767,0.00018037901,0.00003269966,0.00008401988,0.000003927298,0.0013829562,0.11586447,0.87951756,0.00005464232,0.001921464,0.0002681393],"about_ca_topic_score_codex":0.000011963472,"about_ca_topic_score_gemma":0.000014327124,"teacher_disagreement_score":0.8022556,"about_ca_system_score_codex":0.0000652221,"about_ca_system_score_gemma":0.000011017047,"threshold_uncertainty_score":0.88461286},"labels":[],"label_agreement":null},{"id":"W3036171677","doi":"10.1109/tiv.2020.3003889","title":"Real-Time Driver Maneuver Prediction Using LSTM","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Advanced driver assistance systems; Position (finance); Gaze; Simulation; Artificial intelligence; Driving simulator; Driving simulation; Computer vision","score_opus":0.020992358533083857,"score_gpt":0.22068432703199561,"score_spread":0.19969196849891174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036171677","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":[0.5217244,0.000028329807,0.47478244,0.00017636322,0.0003371598,0.00019142663,0.00005899497,0.0018182764,0.00088262523],"genre_scores_gemma":[0.99798095,0.00036030047,0.0012344004,0.00010977325,0.00007636902,0.000017311902,0.0000040396294,0.000057301968,0.00015955648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989335,0.000028896495,0.0003191393,0.00027709224,0.00015626746,0.0002851087],"domain_scores_gemma":[0.9995397,0.000047145488,0.000030314446,0.00022456275,0.00003075265,0.00012752789],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000633384,0.00022722007,0.0002080405,0.00012515612,0.00017683426,0.000019999721,0.00018248778,0.00025167718,0.00055675383],"category_scores_gemma":[0.0000018374792,0.0002457639,0.00013027304,0.00025381194,0.000092114875,0.00016473189,0.0000017916076,0.00043974273,0.0007960107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010255414,0.00009656658,0.0001070317,0.000055415145,0.00020917393,0.000021780721,0.00083217793,0.77760404,0.18923937,0.00012034125,0.00064821006,0.03096332],"study_design_scores_gemma":[0.00017757184,0.00011703641,0.00017337604,0.000026882375,0.00006666474,0.000013243338,0.00009532318,0.5970087,0.4005619,0.000093281065,0.0014266643,0.00023936799],"about_ca_topic_score_codex":0.000018858054,"about_ca_topic_score_gemma":0.0000042138336,"teacher_disagreement_score":0.47625655,"about_ca_system_score_codex":0.00014184199,"about_ca_system_score_gemma":0.000019189732,"threshold_uncertainty_score":0.99999946},"labels":[],"label_agreement":null},{"id":"W3093935464","doi":"10.1109/tiv.2020.3029369","title":"A Novel Algorithm of Multi-AUVs Task Assignment and Path Planning Based on Biologically Inspired Neural Network Map","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Motion planning; Underwater; Grid reference; Grid; Computer science; Artificial neural network; Task (project management); Path (computing); Algorithm; Discretization; Artificial intelligence; Real-time computing; Engineering; Geography; Mathematics; Mobile robot","score_opus":0.052661051107601664,"score_gpt":0.24550234417612335,"score_spread":0.19284129306852169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093935464","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":[0.03064657,0.00025170407,0.9681312,0.00026574786,0.00013297956,0.00025993402,0.000070612135,0.00021566756,0.000025620482],"genre_scores_gemma":[0.98690903,0.00006724488,0.012488129,0.00038512188,0.000046327026,0.000054054,0.0000075527723,0.000031345502,0.000011180958],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886113,0.00006838202,0.00042175798,0.00023714422,0.0001800777,0.00023148544],"domain_scores_gemma":[0.99939454,0.00013963321,0.00006453473,0.00022824241,0.000034722263,0.00013832709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000116087045,0.00022018891,0.00026210854,0.000064836604,0.000114380935,0.000038540755,0.0002093111,0.00011217304,0.000021432223],"category_scores_gemma":[9.394807e-7,0.00019309031,0.00010038158,0.00015854578,0.000058708163,0.00005168919,0.000003556394,0.00028473572,0.000015201721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000630745,0.00015870953,0.00008984113,0.0000522853,0.00006437775,0.0000024558065,0.00049675483,0.8933818,0.03779832,0.0000044981443,0.000033301134,0.0678546],"study_design_scores_gemma":[0.00046529825,0.00041796625,0.00019435254,0.00014851053,0.000021693108,0.000002077927,0.00021407321,0.9260738,0.07031966,0.000007505354,0.0019264034,0.00020869114],"about_ca_topic_score_codex":0.000023648083,"about_ca_topic_score_gemma":0.0000026056257,"teacher_disagreement_score":0.95626247,"about_ca_system_score_codex":0.000041601255,"about_ca_system_score_gemma":0.000010108229,"threshold_uncertainty_score":0.7873994},"labels":[],"label_agreement":null},{"id":"W3164053719","doi":"10.1109/tiv.2021.3082151","title":"Bio-Inspired Neural Network-Based Optimal Path Planning for UUVs Under the Effect of Ocean Currents","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Underwater; Path (computing); Artificial neural network; Motion planning; Shortest path problem; Computer science; Algorithm; Ocean current; Artificial intelligence; Geology; Robot","score_opus":0.028199535288945768,"score_gpt":0.26437272825248015,"score_spread":0.2361731929635344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164053719","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":[0.42693678,0.0004792038,0.5717345,0.000083704355,0.00034059506,0.000255149,0.000031744694,0.00012123034,0.00001708074],"genre_scores_gemma":[0.9990648,0.00005314668,0.0005728047,0.000059153666,0.000066067514,0.0000779879,0.000017171129,0.00004645582,0.000042432872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99877954,0.00015339449,0.00040268447,0.00018969129,0.00018976149,0.00028495418],"domain_scores_gemma":[0.9988979,0.0004784834,0.00006063452,0.000425346,0.00007265226,0.00006498947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023202607,0.00021568478,0.0002563312,0.00006455018,0.00022603672,0.00006007029,0.00028303478,0.000090770125,0.000022779128],"category_scores_gemma":[0.0000010725678,0.00016610393,0.0002555262,0.00023344948,0.000058080164,0.00006629813,0.0000026486712,0.00024348157,0.000008866031],"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":[0.00009269885,0.000048482198,0.00027377214,0.00011196199,0.00012509988,0.000001112593,0.00019925686,0.9806258,0.0039010218,0.0000108728955,0.000177712,0.014432173],"study_design_scores_gemma":[0.0004925186,0.00024677833,0.000091393435,0.00018165515,0.00007760307,0.0000049885703,0.0002801583,0.44575244,0.5504195,0.000021608288,0.002250556,0.00018080826],"about_ca_topic_score_codex":0.0000108161385,"about_ca_topic_score_gemma":0.000004803305,"teacher_disagreement_score":0.572128,"about_ca_system_score_codex":0.000053948777,"about_ca_system_score_gemma":0.000020977302,"threshold_uncertainty_score":0.67735213},"labels":[],"label_agreement":null},{"id":"W3185790437","doi":"10.1109/tiv.2021.3099022","title":"Lightweight Semantic-Aided Localization With Spinning LiDAR Sensor","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Robotics and Sensor-Based Localization","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":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Robustness (evolution); Point cloud; Lidar; Semantic computing; Semantic grid; Semantic heterogeneity; Artificial intelligence; Computer vision; Matching (statistics); Semantic compression; Process (computing); Pipeline (software); Data mining; Semantic technology; Semantic Web; Remote sensing","score_opus":0.014195170680511673,"score_gpt":0.21581285581159207,"score_spread":0.2016176851310804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185790437","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":[0.06379256,0.00012848116,0.93439806,0.00013603299,0.00047237184,0.00015031127,0.00001178925,0.00039990418,0.00051051576],"genre_scores_gemma":[0.9956034,0.00042433437,0.003175274,0.000169306,0.00007083363,0.000016260126,0.000021087939,0.00008195746,0.00043754542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987423,0.00004889708,0.00034038178,0.00029411193,0.00028852263,0.00028578317],"domain_scores_gemma":[0.99929297,0.00006757923,0.000035363355,0.00030356972,0.00018525077,0.000115249226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006463475,0.0002499765,0.00022037842,0.0001742513,0.00020051522,0.0001054568,0.000084438725,0.00012926241,0.00019665061],"category_scores_gemma":[0.0000040479435,0.00023600349,0.00009423654,0.00050784403,0.000045773257,0.00012935806,7.990183e-7,0.00023186013,0.00014247313],"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":[0.000030482974,0.00009996848,0.00007960064,0.00008099248,0.00010244045,0.00005015822,0.00027888888,0.9831896,0.00938175,0.00021200764,0.000110796864,0.0063833334],"study_design_scores_gemma":[0.00018348382,0.000069653215,0.000030677093,0.00014208256,0.000056661538,0.00002545082,0.00019027993,0.39973658,0.59744763,0.00004589273,0.001831372,0.00024020925],"about_ca_topic_score_codex":0.00001550745,"about_ca_topic_score_gemma":0.00006880235,"teacher_disagreement_score":0.93181086,"about_ca_system_score_codex":0.00009842897,"about_ca_system_score_gemma":0.000037464575,"threshold_uncertainty_score":0.9623943},"labels":[],"label_agreement":null},{"id":"W3201745857","doi":"10.1109/tiv.2021.3117840","title":"An Enabling Trajectory Planning Scheme for Lane Change Collision Avoidance on Highways","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","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 Waterloo","funders":"Beijing Municipal Science and Technology Commission; Beijing Nova Program; Ministry of Science and Technology of the People's Republic of China","keywords":"Trajectory; Waypoint; Computer science; Control theory (sociology); Collision avoidance; Controller (irrigation); Quadratic programming; Acceleration; Kinematics; Collision; Motion planning; Real-time computing; Simulation; Mathematical optimization; Robot; Mathematics; Artificial intelligence; Control (management)","score_opus":0.04845953061388596,"score_gpt":0.277147869064543,"score_spread":0.228688338450657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201745857","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":[0.45576164,0.00046948687,0.54116195,0.000114811584,0.0006658076,0.00027878192,0.00008613736,0.001328815,0.00013253937],"genre_scores_gemma":[0.9955954,0.00038959744,0.0032897359,0.00018711052,0.00013194463,0.00022006682,0.00002011251,0.00006594595,0.00010008358],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987735,0.000036750476,0.0002975635,0.00037068906,0.0001504135,0.00037109223],"domain_scores_gemma":[0.9992174,0.00020240418,0.000035516823,0.00038214374,0.00006507109,0.000097448305],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016313989,0.0002502071,0.00024692915,0.00020149276,0.0003161448,0.00003450954,0.00019336249,0.00028394122,0.000047950653],"category_scores_gemma":[0.00000567624,0.000270601,0.00012995426,0.00023849214,0.000051421674,0.00017325762,0.0000011123375,0.0004970996,0.00006643567],"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":[0.00041600413,0.0007133652,0.000113448565,0.00024805707,0.00024187092,0.00007427199,0.0021365816,0.69706655,0.14845271,0.0014082676,0.00035016605,0.14877869],"study_design_scores_gemma":[0.00032353165,0.00028996557,0.00017645537,0.00016293407,0.000026521262,0.000011667264,0.00040161744,0.12440512,0.8693715,0.00015729028,0.0043511125,0.00032232003],"about_ca_topic_score_codex":0.000007940069,"about_ca_topic_score_gemma":0.000037752005,"teacher_disagreement_score":0.7209188,"about_ca_system_score_codex":0.00014336158,"about_ca_system_score_gemma":0.000028026849,"threshold_uncertainty_score":0.9999746},"labels":[],"label_agreement":null},{"id":"W4200632525","doi":"10.1109/tiv.2022.3148212","title":"Structured Learning of Safety Guarantees for the Control of Uncertain Dynamical Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Fault Detection and Control Systems","field":"Engineering","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":"McGill University","funders":"","keywords":"Dynamical systems theory; Computer science; Controller (irrigation); Cruise control; Control (management); Limit (mathematics); Dynamical system (definition); Control theory (sociology); Control engineering; System dynamics; Artificial intelligence; Engineering; Mathematics","score_opus":0.01104579492795688,"score_gpt":0.22762789650812232,"score_spread":0.21658210158016544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200632525","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":[0.04534322,0.00064589595,0.95143616,0.000047786594,0.0014484462,0.00067021133,0.00025065988,0.00011587753,0.000041768635],"genre_scores_gemma":[0.9993999,0.000059463007,0.000020922476,0.000010277413,0.000030333633,0.0002846486,0.0000023667894,0.000027828028,0.00016425765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988447,0.0001260102,0.00048458893,0.00012805712,0.0002525671,0.000164068],"domain_scores_gemma":[0.9990922,0.00055185554,0.00008757592,0.0001800794,0.000058983926,0.000029261957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029753774,0.00013485692,0.0002820297,0.0001158741,0.00024921837,0.000012781516,0.0001894557,0.000052415435,0.00007248808],"category_scores_gemma":[0.0000068813106,0.00010832909,0.00021974734,0.00017706367,0.000051865925,0.0000282198,8.5112816e-7,0.0003007709,0.0000017653942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003964018,0.000030871175,0.000011456092,0.00009831985,0.00024895082,3.0697413e-7,0.00032257295,0.9608936,0.023131764,0.00033042673,0.000016792432,0.014518562],"study_design_scores_gemma":[0.0006119049,0.00025010027,0.000025224093,0.000027035807,0.000071515424,0.000009420303,0.0027301004,0.96903443,0.022814997,0.000023113063,0.004296743,0.00010539715],"about_ca_topic_score_codex":0.00015059498,"about_ca_topic_score_gemma":0.00003664939,"teacher_disagreement_score":0.9540567,"about_ca_system_score_codex":0.0001061377,"about_ca_system_score_gemma":0.000016860471,"threshold_uncertainty_score":0.4417532},"labels":[],"label_agreement":null},{"id":"W4200635810","doi":"10.1109/tiv.2022.3174029","title":"Learning-Based Synthesis of Robust Linear Time-Invariant Controllers","year":2022,"lang":"en","type":"preprint","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","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":"Control theory (sociology); Computer science; Robust control; LTI system theory; Controller (irrigation); Robustness (evolution); Stability (learning theory); Control engineering; Linear system; Gradient descent; Invariant (physics); Control system; Control (management); Artificial intelligence; Engineering; Mathematics; Artificial neural network; Machine learning","score_opus":0.026387922351162135,"score_gpt":0.2538287668915304,"score_spread":0.22744084454036825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200635810","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":[0.03977098,0.000043035107,0.9558386,0.00038349698,0.0011916353,0.00064524746,0.00039299036,0.00014835803,0.001585624],"genre_scores_gemma":[0.9960821,0.00006157306,0.00056786253,0.00005067567,0.00019458547,0.00035011742,0.000045709297,0.00006872907,0.002578623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778825,0.00033543853,0.0006261766,0.00055758766,0.00038957698,0.00030299483],"domain_scores_gemma":[0.9984627,0.00046238626,0.00036375487,0.000470085,0.00010735855,0.0001337204],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00026546323,0.0004009843,0.0006079665,0.00027693773,0.00030120715,0.000040554238,0.00035791993,0.00016674494,0.011557064],"category_scores_gemma":[0.000004174079,0.00040437828,0.0007408537,0.00018228883,0.00011294157,0.000038458293,0.000009642848,0.0015593185,0.00009582346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032549805,0.0005257906,0.00004528,0.000046084253,0.00035542453,0.0000017273812,0.00008249842,0.9744385,0.00029740934,0.000059196846,0.00024516473,0.023577422],"study_design_scores_gemma":[0.00036156975,0.00020801935,0.000008287134,0.00018656038,0.00030519473,6.4569605e-7,0.00029565356,0.857221,0.13894454,0.00019391952,0.0018114124,0.00046321625],"about_ca_topic_score_codex":0.0002676841,"about_ca_topic_score_gemma":0.0000031633479,"teacher_disagreement_score":0.95631117,"about_ca_system_score_codex":0.00008568427,"about_ca_system_score_gemma":0.00018134849,"threshold_uncertainty_score":0.9998408},"labels":[],"label_agreement":null},{"id":"W4205167012","doi":"10.1109/tiv.2021.3134494","title":"Narrowband Jamming Mitigation Based on Multi-Resolution Analysis for Land Vehicles","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GNSS applications; Jamming; Computer science; Global Positioning System; Real-time computing; Robustness (evolution); Satellite navigation; GNSS augmentation; Telecommunications","score_opus":0.02582107219187604,"score_gpt":0.25035894604456777,"score_spread":0.22453787385269172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205167012","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":[0.19050948,0.0002090885,0.80844027,0.000075337135,0.00032846569,0.00015795731,0.000054067736,0.00016292898,0.00006243475],"genre_scores_gemma":[0.99496084,0.00003334213,0.0045762677,0.00006696545,0.00006853722,0.000057156198,0.000026373471,0.000038914222,0.0001715707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883795,0.00005152254,0.00035123885,0.0002877836,0.00022679506,0.00024472203],"domain_scores_gemma":[0.999352,0.00019921939,0.000046079353,0.00019322356,0.00012305031,0.00008645919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018147795,0.0002002827,0.0002499337,0.00030828343,0.00025629395,0.00010639961,0.00008120891,0.00012720296,0.00004518728],"category_scores_gemma":[0.000009063974,0.00020443452,0.00028059073,0.0005068322,0.000027133172,0.000119483375,4.042754e-7,0.0001785684,0.000023061244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004583671,0.00010156734,0.00026192612,0.000117894575,0.00023680033,0.0000040128925,0.0002239064,0.96686065,0.015576438,0.000008040805,0.000042133892,0.016520767],"study_design_scores_gemma":[0.00026436296,0.00005058058,0.00023161023,0.000098015415,0.00015048945,0.0000014176521,0.00014300657,0.66270894,0.33582953,0.000018269926,0.00034304,0.00016075406],"about_ca_topic_score_codex":0.000044067812,"about_ca_topic_score_gemma":0.0003524631,"teacher_disagreement_score":0.8044514,"about_ca_system_score_codex":0.00012464881,"about_ca_system_score_gemma":0.00003319198,"threshold_uncertainty_score":0.83365977},"labels":[],"label_agreement":null},{"id":"W4205699800","doi":"10.1109/tiv.2022.3141881","title":"Tunable Trajectory Planner Using G<sup>3</sup> Curves","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"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":"Discretization; Trajectory; Motion planning; Path (computing); Jerk; Curvature; Mathematical optimization; Computer science; Mathematics; Control theory (sociology); Set (abstract data type); Path length; Mathematical analysis; Robot; Geometry; Acceleration; Physics; Artificial intelligence; Classical mechanics; Control (management)","score_opus":0.045028324561115995,"score_gpt":0.2671886598073157,"score_spread":0.2221603352461997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205699800","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":[0.023249125,0.0005253789,0.97375673,0.0003699483,0.001089346,0.0002849143,0.000053225023,0.00043482395,0.00023653291],"genre_scores_gemma":[0.9348466,0.0001729048,0.06103161,0.0015984921,0.00010875893,0.00018763884,0.000007885222,0.00006560933,0.0019805154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974211,0.00028132735,0.0004139704,0.000620649,0.00072456145,0.00053841755],"domain_scores_gemma":[0.9987345,0.00024247044,0.00010083005,0.0006911112,0.000062457715,0.00016868218],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005008726,0.00027628415,0.00027034347,0.00036597368,0.000885925,0.00010114319,0.00107692,0.00006382622,0.00030224834],"category_scores_gemma":[0.000006574969,0.0002941028,0.00019181683,0.0007060309,0.00007207501,0.0003655448,0.000015698794,0.00066383544,0.00011953052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020652666,0.0002895626,0.000025278705,0.000032387463,0.000057817266,0.000055443066,0.0011891505,0.9834792,0.00053243566,0.000090008514,0.0011762411,0.013051797],"study_design_scores_gemma":[0.0002143713,0.0002813949,0.000028756176,0.00011387235,0.000036451216,0.00017913758,0.00046479187,0.97440445,0.020657009,0.00019922471,0.0030000769,0.00042047285],"about_ca_topic_score_codex":0.00016209055,"about_ca_topic_score_gemma":0.0000017561835,"teacher_disagreement_score":0.9127251,"about_ca_system_score_codex":0.0002848909,"about_ca_system_score_gemma":0.00016115046,"threshold_uncertainty_score":0.9999511},"labels":[],"label_agreement":null},{"id":"W4205911079","doi":"10.1109/tiv.2021.3133849","title":"Traffic Object Detection and Recognition Based on the Attentional Visual Field of Drivers","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Object detection; Advanced driver assistance systems; Gaze; Focus (optics); Cognitive neuroscience of visual object recognition; Support vector machine; Field (mathematics); Object (grammar); Deep learning; Pattern recognition (psychology)","score_opus":0.02559201957530174,"score_gpt":0.2689052677131892,"score_spread":0.24331324813788743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205911079","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":[0.21275608,0.000012339493,0.7857493,0.001042508,0.00017821482,0.00013896293,0.000006587972,0.000060776518,0.000055198252],"genre_scores_gemma":[0.99659264,0.00008307142,0.0026551068,0.00052652735,0.00002200567,0.000058578225,0.0000019048399,0.0000075846965,0.000052554293],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990649,0.00009544435,0.00019785982,0.00029772508,0.0002178856,0.00012615303],"domain_scores_gemma":[0.99876565,0.0007743118,0.00006748771,0.0002435996,0.00010434338,0.00004463717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009677653,0.00010924394,0.00009260133,0.00009901784,0.000240904,0.00003868897,0.00015109328,0.000056270677,0.000060853916],"category_scores_gemma":[0.000010674249,0.00009360836,0.00009885682,0.00041856908,0.000059765825,0.00012460981,0.0000024357369,0.00020977997,0.000026335158],"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":[0.00007033969,0.00039047966,0.000008803023,0.000014814451,0.000034085217,0.0000036030083,0.00014945256,0.1281316,0.026395082,0.00048169115,0.000046659763,0.8442734],"study_design_scores_gemma":[0.0001247644,0.00023656402,0.00013054894,0.000041618307,0.000015231525,0.00000880523,0.000073405274,0.29689386,0.70136106,0.0008834553,0.00013011937,0.00010058785],"about_ca_topic_score_codex":0.000004384634,"about_ca_topic_score_gemma":0.00004145771,"teacher_disagreement_score":0.8441728,"about_ca_system_score_codex":0.000033887027,"about_ca_system_score_gemma":0.000032491113,"threshold_uncertainty_score":0.38172382},"labels":[],"label_agreement":null},{"id":"W4225836256","doi":"10.1109/tiv.2022.3167616","title":"Deep Reinforcement Learning With NMPC Assistance Nash Switching for Urban Autonomous Driving","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Reinforcement Learning in Robotics","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 Waterloo","funders":"Toyota Motor Corporation","keywords":"Reinforcement learning; Reinforcement; Computer science; Artificial intelligence; Psychology; Social psychology","score_opus":0.016984844672470276,"score_gpt":0.2404053362757641,"score_spread":0.22342049160329383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225836256","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":[0.004438317,0.000042059055,0.99282885,0.00030920206,0.00077006896,0.0006359608,9.64374e-7,0.00049498235,0.00047962338],"genre_scores_gemma":[0.97194356,0.000023811264,0.023934454,0.00026962374,0.000046977795,0.00043988123,0.000004498104,0.00005226393,0.0032849514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973224,0.00013560808,0.0005401929,0.00064057583,0.00075211236,0.00060910266],"domain_scores_gemma":[0.9984466,0.00039684138,0.00028711703,0.00061128987,0.0001136756,0.00014450685],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005436868,0.0003238748,0.00027468379,0.00031828776,0.0018508672,0.00030272722,0.0009999396,0.00005787628,0.00012307816],"category_scores_gemma":[0.0000137003935,0.00032835043,0.00019146097,0.0005030116,0.000047125475,0.00044348912,0.000023405599,0.0008512852,0.00002651322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007841087,0.00007715464,0.00016228274,0.000026177497,0.00009716175,0.000007515108,0.0020188522,0.9757032,0.00053751137,0.0021015343,0.00005851214,0.019131681],"study_design_scores_gemma":[0.00042099616,0.0012148444,0.000045400353,0.000054692948,0.00003603141,0.000021251473,0.00058662676,0.9750465,0.0126304785,0.00007019414,0.009448082,0.00042489383],"about_ca_topic_score_codex":0.000024368925,"about_ca_topic_score_gemma":0.000021000804,"teacher_disagreement_score":0.96889436,"about_ca_system_score_codex":0.00057094655,"about_ca_system_score_gemma":0.00013295878,"threshold_uncertainty_score":0.99991685},"labels":[],"label_agreement":null},{"id":"W4243137496","doi":"10.1109/tiv.2020.2973014","title":"IEEE Transactions on Intelligent Vehicles","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","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":"OZ Optics (Canada); Canadian Standards Association","funders":"","keywords":"Computer science; Business","score_opus":0.025753284377696438,"score_gpt":0.23260114936241288,"score_spread":0.20684786498471644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243137496","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":[0.1192423,0.00020004687,0.8740423,0.0012602445,0.0012247107,0.0005181454,0.0001430468,0.002577374,0.00079183804],"genre_scores_gemma":[0.9960106,0.0014356457,0.0007118312,0.0009908779,0.00011669342,0.00016199703,0.0000048587553,0.0001580408,0.00040944692],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99716467,0.00009304515,0.0008350246,0.00072261627,0.00042734275,0.00075727643],"domain_scores_gemma":[0.99854463,0.00026880752,0.00008045052,0.00061819144,0.0000803623,0.00040757738],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018451121,0.00066543836,0.0005620928,0.00037713823,0.00047713445,0.00007083015,0.00061832246,0.0005641716,0.00089233747],"category_scores_gemma":[0.0000050299473,0.0006949777,0.0004810358,0.00064858387,0.00026617368,0.00023673021,0.0000012920282,0.0016660905,0.0024770559],"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":[0.00039453953,0.00045871185,0.0000137775805,0.000110793575,0.0004356216,0.000037351714,0.0015348989,0.6600399,0.01140738,0.00026319185,0.00070451957,0.3245993],"study_design_scores_gemma":[0.00040189485,0.00065325655,0.00003503105,0.000095456184,0.00013285059,0.0000211357,0.0005934419,0.12689763,0.86284435,0.00030693755,0.0072748177,0.0007432198],"about_ca_topic_score_codex":0.000030502417,"about_ca_topic_score_gemma":0.0000698582,"teacher_disagreement_score":0.8767683,"about_ca_system_score_codex":0.00028746622,"about_ca_system_score_gemma":0.00005851695,"threshold_uncertainty_score":0.99955016},"labels":[],"label_agreement":null},{"id":"W4254233783","doi":"10.1109/tiv.2020.3010138","title":"IEEE Transactions on Intelligent Vehicles","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","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 Standards Association","funders":"","keywords":"Computer science; Automotive engineering; Engineering","score_opus":0.025753284377696438,"score_gpt":0.23260114936241288,"score_spread":0.20684786498471644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254233783","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":[0.1192423,0.00020004687,0.8740423,0.0012602445,0.0012247107,0.0005181454,0.0001430468,0.002577374,0.00079183804],"genre_scores_gemma":[0.9960106,0.0014356457,0.0007118312,0.0009908779,0.00011669342,0.00016199703,0.0000048587553,0.0001580408,0.00040944692],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99716467,0.00009304515,0.0008350246,0.00072261627,0.00042734275,0.00075727643],"domain_scores_gemma":[0.99854463,0.00026880752,0.00008045052,0.00061819144,0.0000803623,0.00040757738],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018451121,0.00066543836,0.0005620928,0.00037713823,0.00047713445,0.00007083015,0.00061832246,0.0005641716,0.00089233747],"category_scores_gemma":[0.0000050299473,0.0006949777,0.0004810358,0.00064858387,0.00026617368,0.00023673021,0.0000012920282,0.0016660905,0.0024770559],"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":[0.00039453953,0.00045871185,0.0000137775805,0.000110793575,0.0004356216,0.000037351714,0.0015348989,0.6600399,0.01140738,0.00026319185,0.00070451957,0.3245993],"study_design_scores_gemma":[0.00040189485,0.00065325655,0.00003503105,0.000095456184,0.00013285059,0.0000211357,0.0005934419,0.12689763,0.86284435,0.00030693755,0.0072748177,0.0007432198],"about_ca_topic_score_codex":0.000030502417,"about_ca_topic_score_gemma":0.0000698582,"teacher_disagreement_score":0.8767683,"about_ca_system_score_codex":0.00028746622,"about_ca_system_score_gemma":0.00005851695,"threshold_uncertainty_score":0.99955016},"labels":[],"label_agreement":null},{"id":"W4283212685","doi":"10.1109/tiv.2022.3175647","title":"Consensus Formation Tracking for Multiple AUV Systems Using Distributed Bioinspired Sliding Mode Control","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":92,"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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Control theory (sociology); Robustness (evolution); Sliding mode control; Nonlinear system; Lyapunov function; Computer science; Bounded function; Control engineering; Lyapunov stability; Controller (irrigation); Consensus; Engineering; Multi-agent system; Artificial intelligence; Mathematics; Control (management)","score_opus":0.058074053649909835,"score_gpt":0.27925863082409313,"score_spread":0.2211845771741833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283212685","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":[0.06029922,0.00014236492,0.93170154,0.00026261248,0.0025876777,0.0020237826,0.0024572336,0.00051702803,0.000008540757],"genre_scores_gemma":[0.9968532,0.000005934284,0.0019844067,0.00009235057,0.00007222865,0.00084859284,0.0000618568,0.000042853582,0.00003860189],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99664664,0.0004089033,0.0009865032,0.0006416229,0.00064903963,0.00066726457],"domain_scores_gemma":[0.9976229,0.0008343762,0.0004345469,0.00064033136,0.00028766113,0.00018016263],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00071202964,0.0003694076,0.0004796334,0.00038638682,0.0014959166,0.00038107892,0.00082859513,0.000110910485,0.00000869854],"category_scores_gemma":[0.000038962124,0.00040462593,0.00035724312,0.00059453706,0.000041831856,0.00049239764,0.000009725483,0.0003487909,0.00001631698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023483927,0.0003043132,0.000045961464,0.00007788274,0.00014952666,0.000010989733,0.0005022073,0.94387007,0.046097107,0.0011422555,0.0001082657,0.0074566],"study_design_scores_gemma":[0.0018818339,0.00020929141,0.000012854469,0.00007139935,0.000080381076,0.00007585592,0.00080429413,0.9406274,0.05419577,0.0000673134,0.0015750886,0.00039853028],"about_ca_topic_score_codex":0.00032485215,"about_ca_topic_score_gemma":0.000037021902,"teacher_disagreement_score":0.93655396,"about_ca_system_score_codex":0.0009128661,"about_ca_system_score_gemma":0.000120487,"threshold_uncertainty_score":0.99984056},"labels":[],"label_agreement":null},{"id":"W4285136253","doi":"10.1109/tiv.2022.3174040","title":"Gaze Control for Active Visual SLAM via Panoramic Cost Map","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Robotics and Sensor-Based Localization","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":"McMaster University","funders":"","keywords":"Computer vision; Gaze; Artificial intelligence; Computer science; Orientation (vector space); Simultaneous localization and mapping; Robot; Mobile robot; Mathematics","score_opus":0.013906705474403062,"score_gpt":0.2396469498460546,"score_spread":0.22574024437165155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285136253","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":[0.019902054,0.00005141098,0.97725534,0.0001186657,0.0013192297,0.0007656855,0.0002617555,0.0002698296,0.00005600158],"genre_scores_gemma":[0.9986486,0.000046580328,0.0002821019,0.00016886313,0.000062025465,0.00043398674,0.000036145444,0.0000692443,0.00025248757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892116,0.000054432934,0.00028357992,0.00022635431,0.00022305138,0.0002914046],"domain_scores_gemma":[0.9994651,0.00018952468,0.000036286758,0.0001623348,0.0000659714,0.000080774305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009796368,0.0001988102,0.00019563266,0.00018022988,0.00034473123,0.00003789354,0.00012939847,0.00006865115,0.0002821022],"category_scores_gemma":[0.0000021621795,0.00022453954,0.0001655289,0.00014397159,0.00003374865,0.00007266984,9.04561e-7,0.00028231074,0.000056249402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019818533,0.00016959083,0.0000046063296,0.000032404267,0.000113919894,0.0000022457684,0.00020560659,0.93541586,0.010497264,0.00011519086,0.00023806647,0.05300703],"study_design_scores_gemma":[0.0005528245,0.00029133598,0.000014122755,0.000011447268,0.00006442188,0.0000042599295,0.00023928544,0.8336418,0.1577987,0.00019602233,0.006947524,0.00023823867],"about_ca_topic_score_codex":0.000029116949,"about_ca_topic_score_gemma":0.000035099067,"teacher_disagreement_score":0.97874653,"about_ca_system_score_codex":0.00027693523,"about_ca_system_score_gemma":0.00002193063,"threshold_uncertainty_score":0.9156457},"labels":[],"label_agreement":null},{"id":"W4285184932","doi":"10.1109/tiv.2022.3178061","title":"Adaptive Lane Change Trajectory Planning Scheme for Autonomous Vehicles Under Various Road Frictions and Vehicle Speeds","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scheme (mathematics); Trajectory; Computer science; Control theory (sociology); Automotive engineering; Engineering; Artificial intelligence; Mathematics; Control (management); Physics","score_opus":0.07861061887554463,"score_gpt":0.28489119627613196,"score_spread":0.20628057740058733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285184932","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":[0.04907569,0.00052796025,0.94694245,0.00075298676,0.0012515811,0.0006972442,0.00012678739,0.00052781217,0.000097504846],"genre_scores_gemma":[0.9357929,0.000040491264,0.062118385,0.00059319386,0.00013257719,0.0007092383,0.000008501798,0.00005195009,0.00055274455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976216,0.00016881336,0.00041849827,0.00075558084,0.00045875696,0.0005767443],"domain_scores_gemma":[0.99865663,0.00040899703,0.00014524192,0.0004991183,0.000084705876,0.00020528701],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004167325,0.00034165228,0.00032788058,0.0004609014,0.0013682812,0.00014787812,0.0006428339,0.000114274524,0.000035306493],"category_scores_gemma":[0.0000057795673,0.00037785296,0.00016927907,0.0005365739,0.00010211961,0.0003973789,0.000024325816,0.0006431499,0.000029402905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036019436,0.0015639303,0.00039315975,0.0000734497,0.00058771984,0.00012006933,0.01808129,0.67141986,0.005284586,0.0055559766,0.00070621766,0.29585356],"study_design_scores_gemma":[0.0008158092,0.0014095777,0.0032200655,0.00006179795,0.000075237054,0.000150517,0.0018710245,0.9804354,0.008789821,0.0011337803,0.0013540889,0.00068291434],"about_ca_topic_score_codex":0.00029894063,"about_ca_topic_score_gemma":0.000012180006,"teacher_disagreement_score":0.8867172,"about_ca_system_score_codex":0.00031567403,"about_ca_system_score_gemma":0.000110662324,"threshold_uncertainty_score":0.9999318},"labels":[],"label_agreement":null},{"id":"W4285272860","doi":"10.1109/tiv.2022.3188662","title":"Prediction-Uncertainty-Aware Decision-Making for Autonomous Vehicles","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":188,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Field (mathematics); Motion (physics); Process (computing); Artificial intelligence; Machine learning; Data mining; Mathematics","score_opus":0.017662385844540357,"score_gpt":0.24320909420043132,"score_spread":0.22554670835589097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285272860","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":[0.1527542,0.00021190882,0.84175396,0.00015859706,0.0017051481,0.0006406952,0.00069137703,0.0018664724,0.00021765873],"genre_scores_gemma":[0.99644303,0.00012136143,0.0018611324,0.00017236164,0.00007525369,0.0009918362,0.000016678285,0.000098789555,0.00021954368],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805737,0.0000507478,0.00059131806,0.00046454076,0.00031034733,0.00052570103],"domain_scores_gemma":[0.99865717,0.00062624575,0.00006799994,0.00048532194,0.000067295165,0.00009594673],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032294609,0.0003388863,0.00032145766,0.00041403904,0.0011842002,0.000040970746,0.0004887586,0.00020232763,0.00060708483],"category_scores_gemma":[0.000008833803,0.00038207267,0.0003012221,0.00039244542,0.00011208932,0.00014174878,0.000007918225,0.00080067,0.00007777686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017375778,0.00013790632,0.00003316462,0.000026931644,0.000123714,0.0000062049667,0.00032253406,0.7340526,0.00045931034,0.00029138368,0.0008982997,0.26347417],"study_design_scores_gemma":[0.0006881419,0.00062829367,0.00015123132,0.000085318265,0.00012052625,0.00007495121,0.0015918157,0.9216698,0.030895099,0.0060155615,0.03739118,0.0006881074],"about_ca_topic_score_codex":0.000012689237,"about_ca_topic_score_gemma":0.000039167466,"teacher_disagreement_score":0.84368885,"about_ca_system_score_codex":0.0005859482,"about_ca_system_score_gemma":0.000071315786,"threshold_uncertainty_score":0.99986315},"labels":[],"label_agreement":null},{"id":"W4285277238","doi":"10.1109/tiv.2022.3190308","title":"Cooperative Computation Offloading in Blockchain-Based Vehicular Edge Computing Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":90,"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":"China Postdoctoral Science Foundation; Beihang University; National Natural Science Foundation of China","keywords":"Computer science; Computation offloading; Cloud computing; Distributed computing; Edge computing; Server; Computer network; Mobile edge computing; Vehicular ad hoc network; Wireless ad hoc network; Wireless; Operating system","score_opus":0.01682781739652026,"score_gpt":0.25031833328871295,"score_spread":0.2334905158921927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285277238","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":[0.23471607,0.00009961382,0.7633982,0.0007469681,0.00032067564,0.0003635472,0.0000070087,0.00030862505,0.000039331204],"genre_scores_gemma":[0.9937361,0.000008065215,0.005390668,0.00060412596,0.000020800473,0.00019561888,0.0000041341277,0.000018460227,0.00002201186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980122,0.00026972953,0.0004531372,0.00058524695,0.00030299646,0.00037671934],"domain_scores_gemma":[0.9989518,0.00033544356,0.0001199042,0.00042867282,0.000090784495,0.00007339466],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00056901167,0.00022148721,0.00024098798,0.00049979554,0.0009500189,0.00006907012,0.0007289343,0.000112920694,0.00003560078],"category_scores_gemma":[0.0000041930484,0.00025283278,0.00011585118,0.0014068146,0.00010025417,0.000043657,0.000018859786,0.00091824535,0.000017445298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001826099,0.00041723336,0.000060778322,0.00000526951,0.000019522698,0.000012438186,0.00055278046,0.936197,0.0002655172,0.003942563,0.000039605307,0.05846904],"study_design_scores_gemma":[0.00033848535,0.00018928274,0.00007793554,0.000021936597,0.000007964125,0.000013071097,0.00025678435,0.9790244,0.018836042,0.0006818602,0.0003052789,0.00024696963],"about_ca_topic_score_codex":0.000073093484,"about_ca_topic_score_gemma":0.000054049575,"teacher_disagreement_score":0.75902003,"about_ca_system_score_codex":0.00028613769,"about_ca_system_score_gemma":0.000083625084,"threshold_uncertainty_score":0.9999924},"labels":[],"label_agreement":null},{"id":"W4285297333","doi":"10.1109/tiv.2022.3180665","title":"An Intelligent Congestion Avoidance Mechanism Based on Generalized Regression Neural Network for Heterogeneous Vehicular Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","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":"Polytechnique Montréal","funders":"","keywords":"Network congestion; Computer science; Support vector machine; Packet loss; Artificial neural network; Intelligent transportation system; Network packet; Traffic congestion; Decision tree; Network traffic control; Computer network; Artificial intelligence; Engineering","score_opus":0.0168689811771119,"score_gpt":0.2409802109224935,"score_spread":0.2241112297453816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285297333","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":[0.20907657,0.0005947819,0.7845716,0.00011239126,0.003412194,0.0012693249,0.00010829836,0.0008400074,0.000014857789],"genre_scores_gemma":[0.9937116,0.00031597711,0.002756432,0.0008375189,0.00046788892,0.0014080165,0.00016626914,0.00025252416,0.00008376751],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99623746,0.00047183112,0.0007503204,0.00082809286,0.00068058225,0.0010317382],"domain_scores_gemma":[0.99814284,0.00032974288,0.00014583509,0.0009145661,0.00010744422,0.00035958554],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00066887616,0.0006872835,0.0005482986,0.0002845468,0.0010447932,0.00012830847,0.0005940611,0.00027273447,0.0003308722],"category_scores_gemma":[0.0000053766958,0.000717998,0.0005023992,0.00050250004,0.00006996329,0.00015324682,0.000006627367,0.0011185096,0.00002578261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073004037,0.00032745118,0.0000065953604,0.00003966602,0.00012680989,0.000039720155,0.00008524932,0.96484005,0.002287322,0.00022943398,0.0005998656,0.030687785],"study_design_scores_gemma":[0.0006554534,0.0011942616,0.000008214902,0.000106070875,0.00011643064,0.000038199298,0.00004620872,0.93201554,0.061773278,0.0004339875,0.0029294402,0.0006829082],"about_ca_topic_score_codex":0.000025189942,"about_ca_topic_score_gemma":0.0000754216,"teacher_disagreement_score":0.784635,"about_ca_system_score_codex":0.0006022397,"about_ca_system_score_gemma":0.000041394047,"threshold_uncertainty_score":0.9995271},"labels":[],"label_agreement":null},{"id":"W4286656139","doi":"10.1109/tiv.2022.3188942","title":"Experimental Investigation of a Maneuver Selection Algorithm for Vehicles in Low Adhesion Conditions","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Soil Mechanics and Vehicle Dynamics","field":"Engineering","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":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke","keywords":"Algorithm; Notation; Mathematics; Inertial frame of reference; Estimator; Computer science; Statistics; Physics; Arithmetic","score_opus":0.017740964625558887,"score_gpt":0.2455964396093595,"score_spread":0.22785547498380063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286656139","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":[0.76012814,0.00004758001,0.2385659,0.00001735622,0.000507918,0.00040224785,0.00019217297,0.00012463029,0.000014068939],"genre_scores_gemma":[0.99806273,0.00003484676,0.0010844211,0.000040311454,0.000023960889,0.0006271532,0.000039479128,0.000047255373,0.000039816492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892163,0.00005158627,0.00037481173,0.0002151523,0.00022276444,0.00021404374],"domain_scores_gemma":[0.9996261,0.000090865746,0.00005316348,0.00012743469,0.000043865635,0.000058583944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016117589,0.00016504958,0.00017573296,0.00035920835,0.00020307672,0.000015652271,0.00012156012,0.000076117394,0.0000975161],"category_scores_gemma":[0.0000013774212,0.00020387971,0.0001221592,0.0003715092,0.00003331009,0.00011456629,0.0000022769962,0.00026436723,0.000005831571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011309947,0.0005135314,0.000115374925,0.00010502297,0.0000836708,0.000002809449,0.001342361,0.66421574,0.24656574,0.00037852497,0.00013870384,0.08642541],"study_design_scores_gemma":[0.0002697958,0.00021683628,0.000118805845,0.000025991112,0.000011388411,0.00000460055,0.00057634886,0.5551109,0.44326708,0.00024445518,0.000035377,0.000118434364],"about_ca_topic_score_codex":0.000049734772,"about_ca_topic_score_gemma":0.000030070072,"teacher_disagreement_score":0.23793463,"about_ca_system_score_codex":0.00034057713,"about_ca_system_score_gemma":0.000029770292,"threshold_uncertainty_score":0.83139735},"labels":[],"label_agreement":null},{"id":"W4289792821","doi":"10.1109/tiv.2022.3196396","title":"Verification and Validation Methods for Decision-Making and Planning of Automated Vehicles: A Review","year":2022,"lang":"en","type":"review","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","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 Waterloo","funders":"","keywords":"Computer science; Systems engineering; Hierarchy; Range (aeronautics); Reliability engineering; Engineering","score_opus":0.06416434948146378,"score_gpt":0.39219444330842346,"score_spread":0.3280300938269597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289792821","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000034835895,0.5726779,0.42589328,0.0000050548097,0.00018720004,0.00069922075,0.00006088892,0.00042493275,0.000016666225],"genre_scores_gemma":[0.0015572304,0.97710985,0.020617483,0.000017578943,0.000013486047,0.0005781038,0.00002099524,0.00007618123,0.000009093554],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982271,0.00017258982,0.00088613544,0.00038747792,0.000119803306,0.00020689779],"domain_scores_gemma":[0.9971547,0.002175431,0.00023463732,0.0003439267,0.000043149405,0.00004813202],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00078025286,0.00035616755,0.001042195,0.00038782056,0.0002196046,0.000019828827,0.00020442296,0.0003278172,0.000058308622],"category_scores_gemma":[0.00007146779,0.00035199974,0.0002390705,0.00034992184,0.000089688394,0.00007713048,0.0000051399743,0.0005010397,0.0000035784335],"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":[0.000013965539,0.00002812972,2.2208458e-7,0.015798364,0.00018286919,7.8635003e-7,0.000098189324,0.0022751873,0.000011850801,0.000042739855,0.00006554343,0.98148215],"study_design_scores_gemma":[0.00018809126,0.00023958895,0.000003244541,0.05945416,0.0021188406,0.00009313659,0.00015880816,0.075389855,0.0018301429,0.0005603418,0.8591122,0.0008515907],"about_ca_topic_score_codex":0.0000019975153,"about_ca_topic_score_gemma":6.3310137e-7,"teacher_disagreement_score":0.9806306,"about_ca_system_score_codex":0.00013719019,"about_ca_system_score_gemma":0.00004677259,"threshold_uncertainty_score":0.9998932},"labels":[],"label_agreement":null},{"id":"W4313555044","doi":"10.1109/tiv.2023.3234253","title":"Formulating Vehicle Aggressiveness Towards Social Cognitive Autonomous Driving","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Perspective (graphical); Cognition; Computer science; Hazard; Focus (optics); Motion (physics); Conceptual model; Collision; Simulation; Artificial intelligence; Psychology; Computer security","score_opus":0.02323955149143908,"score_gpt":0.2635297275746575,"score_spread":0.2402901760832184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313555044","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":[0.73735225,0.000049375027,0.25768197,0.000152326,0.0005659082,0.00023720614,0.000049065904,0.003141804,0.0007701055],"genre_scores_gemma":[0.9990648,0.00015715227,0.00012161231,0.0000560466,0.000089454916,0.00013367392,0.00001108615,0.00008788543,0.00027826053],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983424,0.00004714948,0.00043191,0.00034628273,0.00023196224,0.00060029246],"domain_scores_gemma":[0.9994031,0.00017372832,0.0000642975,0.00019391513,0.00007159979,0.00009333414],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021976115,0.00031837058,0.00031135356,0.00038223233,0.00064312917,0.000048749673,0.00025596362,0.00033135622,0.00014409835],"category_scores_gemma":[0.000010220109,0.00034751216,0.00021292827,0.0006680153,0.00015231276,0.00020627648,0.0000055508644,0.0006390702,0.0007144835],"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":[0.000058060654,0.000101108,0.0001987521,0.00007163512,0.00025178984,0.00004950068,0.0019088414,0.12338215,0.0066764196,0.00023472634,0.00009915759,0.86696786],"study_design_scores_gemma":[0.00063006,0.0001506302,0.0076244455,0.00023412184,0.00010451965,0.0000191637,0.0016135013,0.3744831,0.6126159,0.0011202231,0.0006210896,0.0007832343],"about_ca_topic_score_codex":0.000025407518,"about_ca_topic_score_gemma":0.000040320363,"teacher_disagreement_score":0.8661846,"about_ca_system_score_codex":0.00018858329,"about_ca_system_score_gemma":0.00004557196,"threshold_uncertainty_score":0.99989766},"labels":[],"label_agreement":null},{"id":"W4316876944","doi":"10.1109/tiv.2023.3237703","title":"Disturbance Observer-Based Cooperative Control of Vehicle Platoons Subject to Mismatched Disturbance","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Traffic control and management","field":"Engineering","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Platoon; Disturbance (geology); Control theory (sociology); Stability (learning theory); Computer science; Acceleration; Sliding mode control; String (physics); Mode (computer interface); Vehicle dynamics; Engineering; Control engineering; Control (management); Automotive engineering; Mathematics; Artificial intelligence; Physics","score_opus":0.018770914812744274,"score_gpt":0.22910725212505148,"score_spread":0.21033633731230722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316876944","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":[0.41668305,0.00014615718,0.58040357,0.0004097609,0.0006318324,0.00061544456,0.00022675026,0.00074513623,0.00013830225],"genre_scores_gemma":[0.99871725,0.000066333225,0.00014836376,0.00020710003,0.000032703247,0.00028515392,0.0000077109335,0.00005063793,0.00048475797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985415,0.000043273063,0.00042143912,0.00031858336,0.0002832866,0.00039196492],"domain_scores_gemma":[0.9991079,0.00025571542,0.00003935409,0.00037382843,0.00007884743,0.0001443341],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001484745,0.00028037332,0.00036300547,0.00021142403,0.000120482335,0.000035362893,0.00026268305,0.000077917175,0.000064473315],"category_scores_gemma":[0.000008384821,0.0002733799,0.00019387332,0.0006923093,0.00006098924,0.00008511141,0.0000017519455,0.00021159045,0.00026980985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020946792,0.00014986223,0.00006310519,0.00009844334,0.00017056392,0.000008575901,0.00045323622,0.9624643,0.016275346,0.00011982459,0.0010388574,0.018948462],"study_design_scores_gemma":[0.0022089118,0.00050413614,0.008514977,0.00027163833,0.00016354813,0.0000010366158,0.0005005859,0.5027321,0.47362056,0.000058832797,0.01065549,0.0007681437],"about_ca_topic_score_codex":0.00004913358,"about_ca_topic_score_gemma":0.00020248251,"teacher_disagreement_score":0.5820342,"about_ca_system_score_codex":0.00010757784,"about_ca_system_score_gemma":0.000025877727,"threshold_uncertainty_score":0.99997187},"labels":[],"label_agreement":null},{"id":"W4364322321","doi":"10.1109/tiv.2023.3265866","title":"Joint Optimization of Platoon Control and Resource Scheduling in Cooperative Vehicle-Infrastructure System","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Traffic control and management","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":"University of Waterloo","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Platoon; Scheduling (production processes); Cell Transmission Model; Computer science; Reliability (semiconductor); Mathematical optimization; Engineering; Control (management); Mathematics; Traffic congestion","score_opus":0.010256840367367409,"score_gpt":0.20386189449860567,"score_spread":0.19360505413123827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4364322321","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":[0.35860926,0.000121021534,0.640063,0.00006931541,0.00021250804,0.00035343904,0.000030256284,0.0003854591,0.00015578688],"genre_scores_gemma":[0.99931335,0.00018526848,0.000347626,0.000018789195,0.00001917018,0.000050732528,0.000003439018,0.000027267592,0.000034322824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990829,0.000040317682,0.00035209366,0.00018426722,0.00014645714,0.00019391961],"domain_scores_gemma":[0.9996586,0.000086781736,0.000033905846,0.00013421262,0.000033317217,0.000053225674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015591196,0.00015912639,0.0002424434,0.00033100828,0.00006271381,0.00002525074,0.000069327194,0.00008427128,0.000013603833],"category_scores_gemma":[0.0000033000813,0.00015554087,0.00005399887,0.00034670497,0.000035867157,0.000075609925,0.0000013749872,0.00019536499,0.000011655163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000636176,0.000019205263,0.000016526441,0.00015228317,0.00007240797,0.0000054644865,0.00049298815,0.9795581,0.0029551974,0.00016290385,0.000031314656,0.016469998],"study_design_scores_gemma":[0.00069768087,0.00006365298,0.00056575873,0.0001979953,0.000032838856,0.0000022618735,0.0015892062,0.9843134,0.012267839,0.000006718572,0.00012644348,0.00013615329],"about_ca_topic_score_codex":0.00001729734,"about_ca_topic_score_gemma":0.00003294382,"teacher_disagreement_score":0.64070415,"about_ca_system_score_codex":0.00008854643,"about_ca_system_score_gemma":0.000009195297,"threshold_uncertainty_score":0.6342773},"labels":[],"label_agreement":null},{"id":"W4366667794","doi":"10.1109/tiv.2023.3269207","title":"Retracted: From Formula One to Autonomous One: History, Achievements, and Future Perspectives","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":12,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Race (biology); Consistency (knowledge bases); Entertainment; Value (mathematics); Power (physics); Engineering; Computer science; Operations research; Sociology; Artificial intelligence; Political science; Law","score_opus":0.020255892249684458,"score_gpt":0.22085394499371971,"score_spread":0.20059805274403525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366667794","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":[0.82169026,0.0013954618,0.16851331,0.0019338147,0.0012873268,0.0005222301,0.00017011196,0.0034878885,0.0009996184],"genre_scores_gemma":[0.99435496,0.0033696552,0.0012291776,0.00013372912,0.00017281175,0.000088198714,0.0000121952735,0.00006728517,0.0005719797],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986501,0.000027511473,0.0003251625,0.00041130156,0.00020247327,0.00038348624],"domain_scores_gemma":[0.99930847,0.000090095746,0.000033212385,0.00037541613,0.000040725394,0.0001520545],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001373755,0.0002709081,0.000284135,0.00040379292,0.00018532372,0.000023535586,0.00022584014,0.00036440606,0.00041859577],"category_scores_gemma":[0.000003238697,0.00031070257,0.00009790427,0.00031591754,0.00009156333,0.00015228915,0.0000045462675,0.0007152263,0.0005436576],"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":[0.00031997776,0.00069673365,0.00013890638,0.00008604775,0.0010970369,0.000032521926,0.015981445,0.028076991,0.04715261,0.0026982913,0.0033281802,0.9003913],"study_design_scores_gemma":[0.0019074618,0.0012946334,0.029688727,0.0003824806,0.00043367338,0.000021335072,0.015234471,0.061639693,0.6410137,0.004450419,0.24094462,0.0029887941],"about_ca_topic_score_codex":0.000092771756,"about_ca_topic_score_gemma":0.00014365088,"teacher_disagreement_score":0.89740247,"about_ca_system_score_codex":0.00053848355,"about_ca_system_score_gemma":0.000030221967,"threshold_uncertainty_score":0.9999345},"labels":[],"label_agreement":null},{"id":"W4381886295","doi":"10.1109/tiv.2023.3289069","title":"Privacy-Preserving Proxy Re-Encryption With Decentralized Trust Management for MEC-Empowered VANETs","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Broadcast encryption; Encryption; Computer network; Computer security; Server; Cryptography; Information privacy; Public-key cryptography","score_opus":0.01982797455167324,"score_gpt":0.24882916267325308,"score_spread":0.22900118812157985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381886295","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":[0.21607782,0.00013400447,0.7783446,0.0002398551,0.00086919015,0.0019919036,0.000053858268,0.0017504119,0.00053836836],"genre_scores_gemma":[0.9901333,0.0014372172,0.006310231,0.00005200135,0.00007757361,0.00082851027,0.00005896439,0.00016925432,0.0009329599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976961,0.00005609618,0.0004880676,0.00050410524,0.00045334385,0.0008022906],"domain_scores_gemma":[0.998848,0.00014985428,0.00006290313,0.00065876445,0.00007990516,0.00020057913],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030543105,0.00041202785,0.00033187022,0.00038274858,0.00026402273,0.00012055215,0.0004578685,0.00016860396,0.00022160004],"category_scores_gemma":[0.0000054353104,0.0003966048,0.00020580814,0.000723005,0.000052184783,0.00024834898,0.0000064950145,0.00032585536,0.00023943669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029769642,0.00010392421,0.000032748158,0.0002970642,0.0004114212,0.000027657043,0.00045733192,0.9734346,0.0016915816,0.00010654962,0.0028568693,0.020282539],"study_design_scores_gemma":[0.0011138407,0.00022995198,0.0002198402,0.00035073204,0.00019299761,0.000010923211,0.00047155225,0.9041031,0.07180086,0.0005361386,0.020398637,0.00057140074],"about_ca_topic_score_codex":0.000017328923,"about_ca_topic_score_gemma":0.000099572644,"teacher_disagreement_score":0.7740555,"about_ca_system_score_codex":0.00024420352,"about_ca_system_score_gemma":0.00001971769,"threshold_uncertainty_score":0.9998486},"labels":[],"label_agreement":null},{"id":"W4384787430","doi":"10.1109/tiv.2023.3296435","title":"Adaptive Pure Pursuit: A Real-Time Path Planner Using Tracking Controllers to Plan Safe and Kinematically Feasible Paths","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Robotic Path Planning Algorithms","field":"Computer Science","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":"Concordia University","funders":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Planner; Path (computing); Tracking (education); Plan (archaeology); Computer science; Motion planning; Control theory (sociology); Artificial intelligence; Computer vision; Real-time computing; Mathematical optimization; Mathematics; Robot; Control (management); Geography; Psychology","score_opus":0.05724224967894712,"score_gpt":0.2861207910976258,"score_spread":0.22887854141867867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384787430","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":[0.061225053,0.00002195805,0.9360395,0.00070552056,0.00051485363,0.00059743464,0.00008430177,0.0005605718,0.00025077126],"genre_scores_gemma":[0.87085503,0.00008120408,0.12787738,0.00033608577,0.00009386463,0.00008995295,0.0000045524444,0.000060837105,0.00060108927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973948,0.00016216596,0.0004954648,0.0007012726,0.0006119522,0.00063436705],"domain_scores_gemma":[0.9983139,0.00060508127,0.00010977764,0.00049804326,0.00011725059,0.0003559393],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057182775,0.00034832783,0.00043860968,0.00053423346,0.00040460934,0.0003064307,0.00061969034,0.00015498316,0.000030316405],"category_scores_gemma":[0.000029052175,0.0003254333,0.00013161563,0.00088534626,0.00007737792,0.00033707195,0.00001461627,0.00033452112,0.00039671655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002942944,0.00028483596,0.000049660935,0.00006224803,0.00024891176,0.00037505868,0.0064552696,0.8361222,0.026627045,0.0007548444,0.0007529395,0.12797269],"study_design_scores_gemma":[0.0005059955,0.00050704856,0.000528064,0.00038414542,0.000052354655,0.00008144943,0.00051908207,0.98463494,0.011623732,0.0006345079,0.00005715725,0.00047153162],"about_ca_topic_score_codex":0.00008335304,"about_ca_topic_score_gemma":0.0000085009015,"teacher_disagreement_score":0.80963,"about_ca_system_score_codex":0.00012586098,"about_ca_system_score_gemma":0.000099516634,"threshold_uncertainty_score":0.9999198},"labels":[],"label_agreement":null},{"id":"W4386634590","doi":"10.1109/tiv.2023.3314731","title":"Medium-Fidelity Evaluation and Modeling for Perception Systems of Intelligent and Connected Vehicles","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","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":"Huawei Technologies (Canada); University of Waterloo","funders":"","keywords":"Perception; Computer science; Fidelity; Benchmark (surveying); Process (computing); Domain (mathematical analysis); Probabilistic logic; Artificial intelligence; Machine learning; Human–computer interaction","score_opus":0.049554929980068586,"score_gpt":0.28842931063567495,"score_spread":0.23887438065560637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386634590","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":[0.5651059,0.00027646497,0.43336165,0.00006846464,0.00025294293,0.0004828467,0.00004417433,0.00039380888,0.000013708907],"genre_scores_gemma":[0.997311,0.0019821806,0.00036223666,0.000011147612,0.00002871706,0.00022301555,0.0000168787,0.000037513913,0.000027318714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986632,0.00006238818,0.0005168289,0.00029321897,0.00021538071,0.00024900946],"domain_scores_gemma":[0.9992228,0.0002527479,0.00005299916,0.0002092534,0.00018858972,0.00007361489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007439798,0.00020532894,0.00028244947,0.0003334817,0.0001792137,0.000024748611,0.00010012382,0.00024747793,0.000016758837],"category_scores_gemma":[0.000021873951,0.00021047224,0.000075141055,0.00025399265,0.0001193431,0.00012674805,0.0000029626096,0.00022844835,0.00001421891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009797338,0.00004758073,0.000092527705,0.0002842005,0.0001323456,5.335696e-7,0.0010222079,0.85387313,0.024575308,0.0002597952,0.000038470163,0.11957591],"study_design_scores_gemma":[0.0002831243,0.00012129079,0.0004973088,0.00009633071,0.00009825996,0.0000060941848,0.0018362821,0.9432,0.052647825,0.00097410625,0.0000522731,0.00018714087],"about_ca_topic_score_codex":0.000053250853,"about_ca_topic_score_gemma":0.000058091493,"teacher_disagreement_score":0.4329994,"about_ca_system_score_codex":0.00010176382,"about_ca_system_score_gemma":0.00002689241,"threshold_uncertainty_score":0.8582809},"labels":[],"label_agreement":null},{"id":"W4387490417","doi":"10.1109/tiv.2023.3323518","title":"MCHFormer: A Multi-Cross Hybrid Former of Point-Image for 3D Object Detection","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Advanced Neural Network Applications","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":"McMaster University","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Point cloud; Computer science; Artificial intelligence; Computer vision; Object detection; Feature extraction; Feature (linguistics); Pattern recognition (psychology)","score_opus":0.03565442813648844,"score_gpt":0.3113261137491017,"score_spread":0.27567168561261324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387490417","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":[0.033996828,0.000019335925,0.963851,0.00018074375,0.00053264294,0.0007779109,0.00007682729,0.00052517303,0.000039552233],"genre_scores_gemma":[0.91711926,0.00017134818,0.08141598,0.00009981241,0.000042643824,0.000576253,0.0000056215863,0.000036148052,0.00053290883],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982926,0.000034343557,0.00048587704,0.0005038331,0.00024914247,0.000434184],"domain_scores_gemma":[0.99854356,0.000412115,0.00015571473,0.0005882895,0.00019816202,0.00010218803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023372399,0.00021992877,0.00022154437,0.00032317798,0.0003889121,0.00007034566,0.00054186635,0.00006945514,0.000014053653],"category_scores_gemma":[0.0000147292585,0.00021688214,0.00025691616,0.00088634563,0.00011988709,0.0006026568,0.0000074857103,0.0002083613,0.00020736741],"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":[0.00016665636,0.0004332755,0.000015396427,0.00009858307,0.00009116995,0.000003614423,0.0005815448,0.07692189,0.10815371,0.00050549716,0.0002249958,0.8128037],"study_design_scores_gemma":[0.00029096127,0.00018594228,0.00007827047,0.00002129251,0.000013547295,0.000010663639,0.00003882893,0.3346822,0.661847,0.001222505,0.0014387381,0.00017005556],"about_ca_topic_score_codex":0.00002067653,"about_ca_topic_score_gemma":0.00006663836,"teacher_disagreement_score":0.88312244,"about_ca_system_score_codex":0.000083404084,"about_ca_system_score_gemma":0.000034398152,"threshold_uncertainty_score":0.8844197},"labels":[],"label_agreement":null},{"id":"W4387587631","doi":"10.1109/tiv.2023.3323973","title":"Secure Communication With UAV-Enabled Aerial RIS: Learning Trajectory With Reflection Optimization","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Eavesdropping; Computer science; Reflection (computer programming); Trajectory; Software deployment; Secrecy; Provisioning; Wireless; Physical layer; Trajectory optimization; Wireless network; Drone; Distributed computing; Computer network; Computer security; Telecommunications","score_opus":0.01830568952911047,"score_gpt":0.24165990167056592,"score_spread":0.22335421214145545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387587631","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":[0.065078266,0.00015869683,0.92939985,0.0001283154,0.00013346675,0.00033216586,0.00000709058,0.0043379976,0.00042414124],"genre_scores_gemma":[0.9785944,0.004782646,0.015950011,0.0000125696415,0.000019104298,0.00029018536,0.00003584487,0.00009404515,0.00022118173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887145,0.0000976463,0.00028118872,0.00023688984,0.00023768375,0.00027516123],"domain_scores_gemma":[0.998979,0.000166885,0.00008057765,0.0006065217,0.00011625087,0.000050789975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013986255,0.00024774502,0.00019975966,0.00043293094,0.00043800496,0.00006401404,0.00031781,0.0001666739,0.000057288653],"category_scores_gemma":[0.0000063317048,0.0002258798,0.000050331615,0.001095028,0.00013406284,0.0003355363,0.0000033152005,0.0007243931,0.00006883653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015451408,0.00004418645,0.000017235854,0.000031100106,0.00008006283,0.000001475741,0.00054113514,0.96634763,0.0021529496,0.00005589138,0.000054250377,0.030519594],"study_design_scores_gemma":[0.0005962641,0.00042075012,0.00005274546,0.00024056347,0.00005050824,0.000014887502,0.002980432,0.71408767,0.2790426,0.00012139379,0.001918661,0.00047353996],"about_ca_topic_score_codex":0.00002159187,"about_ca_topic_score_gemma":0.00015474559,"teacher_disagreement_score":0.91351616,"about_ca_system_score_codex":0.00020099555,"about_ca_system_score_gemma":0.000025125153,"threshold_uncertainty_score":0.9211111},"labels":[],"label_agreement":null},{"id":"W4387789981","doi":"10.1109/tiv.2023.3326136","title":"Pavement Defect Detection With Deep Learning: A Comprehensive Survey","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":60,"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":"China Postdoctoral Science Foundation","keywords":"Robustness (evolution); Computer science; Adaptability; Artificial intelligence; Deep learning; Field (mathematics); Machine learning; Key (lock); Data mining","score_opus":0.020539286441842424,"score_gpt":0.23326166471498505,"score_spread":0.21272237827314264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387789981","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":[0.53416294,0.000032616917,0.4642247,0.0000038940066,0.0007284487,0.00014405325,0.000006225809,0.0006271929,0.00006995126],"genre_scores_gemma":[0.9992024,0.00031682962,0.0001119175,0.000017136645,0.00006395023,0.000075078104,0.000007195978,0.000057596295,0.00014795105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99896336,0.00006048081,0.00019681215,0.00021771627,0.00021178577,0.00034983433],"domain_scores_gemma":[0.99948585,0.00015316499,0.000026020483,0.0001684036,0.00009101345,0.00007553935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011941114,0.0002181711,0.00016806198,0.0002444427,0.00021070646,0.000043732824,0.00009291282,0.000082282735,0.000035798937],"category_scores_gemma":[0.0000028951572,0.00019257242,0.000100716956,0.0004919195,0.00003989244,0.000079295794,0.0000010427555,0.00039973113,0.00023764443],"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":[0.00007375958,0.000016916603,0.00044583334,0.00004006847,0.00015356322,0.00001078653,0.00044228308,0.8949118,0.016135791,0.000002624443,0.000029017729,0.08773754],"study_design_scores_gemma":[0.0003579136,0.00046418473,0.014381142,0.00009962764,0.00005728526,0.000017152637,0.0010396095,0.075196326,0.9049124,0.000044471373,0.0029752657,0.00045458224],"about_ca_topic_score_codex":0.00009426208,"about_ca_topic_score_gemma":0.00049817155,"teacher_disagreement_score":0.88877666,"about_ca_system_score_codex":0.0001292718,"about_ca_system_score_gemma":0.000008222279,"threshold_uncertainty_score":0.78528756},"labels":[],"label_agreement":null},{"id":"W4388208052","doi":"10.1109/tiv.2023.3327715","title":"DriveLLM: Charting the Path Toward Full Autonomous Driving With Large Language Models","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":80,"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":"Path (computing); Computer science; Programming language","score_opus":0.01539693520225278,"score_gpt":0.2261415528987847,"score_spread":0.21074461769653194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388208052","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":[0.42352578,0.000081721955,0.5729529,0.0003178042,0.00021961483,0.00022017311,0.000029858118,0.0021992982,0.00045284367],"genre_scores_gemma":[0.9987312,0.00024789493,0.00034597068,0.00008713261,0.000045927216,0.00012254078,0.0000057689554,0.000077853845,0.00033571754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985378,0.000041018753,0.00032725278,0.00030290463,0.00020707009,0.00058395456],"domain_scores_gemma":[0.99925196,0.00015616897,0.00004679286,0.00043125657,0.00003227174,0.00008156603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002990766,0.000281805,0.00022888888,0.00021261106,0.00041110447,0.00004495598,0.0003485831,0.00018176006,0.00009343578],"category_scores_gemma":[0.000002811461,0.00021305535,0.00012553947,0.0005126926,0.000105603125,0.00017112665,0.0000050497333,0.0006725855,0.0004611652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048574922,0.000098038516,0.00007246094,0.000055267777,0.00021982999,0.00005932885,0.008616942,0.89161533,0.0061792973,0.0011747713,0.0001749889,0.09168517],"study_design_scores_gemma":[0.00026137405,0.00013774211,0.00016698237,0.00008161683,0.000055341327,0.00002909393,0.0045956927,0.8827114,0.11030434,0.0004891419,0.0007785225,0.00038874205],"about_ca_topic_score_codex":0.000018326115,"about_ca_topic_score_gemma":0.00012667484,"teacher_disagreement_score":0.57520545,"about_ca_system_score_codex":0.00010868772,"about_ca_system_score_gemma":0.000026582144,"threshold_uncertainty_score":0.86881447},"labels":[],"label_agreement":null},{"id":"W4388579662","doi":"10.1109/tiv.2023.3331709","title":"An Information Fusion Based Incipient Fault Diagnosis Method for Railway Vehicle Door System","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","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 British Columbia, Okanagan Campus; University of British Columbia","funders":"Aeronautical Science Foundation of China; National Natural Science Foundation of China","keywords":"Fault (geology); Reliability (semiconductor); Information fusion; Computer science; Data mining; Fuzzy logic; Sensor fusion; Divergence (linguistics); Reliability engineering; Artificial intelligence; Engineering; Real-time computing","score_opus":0.01808365605280952,"score_gpt":0.30233814082451005,"score_spread":0.2842544847717005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388579662","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":[0.121614784,0.000018115525,0.87197495,0.00015180102,0.00072513905,0.0011241426,0.00034281515,0.003934145,0.00011408717],"genre_scores_gemma":[0.98034954,0.00015107759,0.01567233,0.00017745915,0.00006595932,0.0033885792,0.000096006,0.00008015671,0.000018888973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981643,0.00010462688,0.0006261544,0.00029353955,0.00037914346,0.00043222256],"domain_scores_gemma":[0.9984996,0.00059975445,0.000076305376,0.0004894909,0.00015202576,0.00018286757],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00063152506,0.00033566792,0.00030509726,0.00072435156,0.00027816964,0.00012988987,0.00032769717,0.00022328815,0.00007372693],"category_scores_gemma":[0.000018646491,0.00033537,0.00023464796,0.0006644905,0.00002725798,0.0005493441,0.0000022835313,0.00027899072,0.0002809944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086234264,0.00018689696,0.00010036326,0.00048239902,0.00006105478,0.0000022588977,0.00058058696,0.7172582,0.007329683,0.00016766685,0.0031292832,0.27061537],"study_design_scores_gemma":[0.00022519106,0.00018989126,0.00011954227,0.000119335695,0.000036230835,0.0000011621077,0.00020399832,0.56172377,0.4299673,0.000026873933,0.0071601346,0.00022658597],"about_ca_topic_score_codex":0.00013451773,"about_ca_topic_score_gemma":0.000073214804,"teacher_disagreement_score":0.8587348,"about_ca_system_score_codex":0.00033031945,"about_ca_system_score_gemma":0.000025839036,"threshold_uncertainty_score":0.9999098},"labels":[],"label_agreement":null},{"id":"W4390533271","doi":"10.1109/tiv.2024.3349466","title":"Retracted: Integrating Large Language Models and Metaverse in Autonomous Racing: An Education-Oriented Perspective","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"AI in Service Interactions","field":"Computer Science","cited_by":15,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Metaverse; Context (archaeology); Autonomy; Perspective (graphical); Competition (biology); Sociology; Computer science; Knowledge management; Public relations; Virtual reality; Human–computer interaction; Political science; Artificial intelligence; Ecology; History","score_opus":0.019184859407812,"score_gpt":0.311818380668769,"score_spread":0.292633521260957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390533271","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":[0.13500942,0.00045949334,0.8603043,0.00071823644,0.0013851968,0.00027041128,0.00002727816,0.00044224618,0.0013834329],"genre_scores_gemma":[0.98711133,0.00010367954,0.011743967,0.00030874024,0.000066900466,0.00009227528,0.0000044697163,0.000030291296,0.0005383618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981794,0.00014577732,0.00039359287,0.00069940754,0.00026081802,0.00032103635],"domain_scores_gemma":[0.998832,0.00028431957,0.000057782647,0.0005012903,0.00017527552,0.00014933647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000336684,0.00025100086,0.00020121859,0.000711589,0.00021158063,0.00036854105,0.00038173504,0.00014279362,0.00008794185],"category_scores_gemma":[0.00001476619,0.00024501368,0.00010641806,0.00083032105,0.00004823195,0.0020530445,0.000008415828,0.0009019512,0.00008808985],"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":[0.0001219676,0.0037084916,0.00006646979,0.00017358952,0.0003694526,0.00016168084,0.26061752,0.044065166,0.009515746,0.33951294,0.00026046505,0.34142652],"study_design_scores_gemma":[0.00013987519,0.00018119224,0.000057026224,0.00030514697,0.00004174002,0.000084091844,0.04371722,0.917746,0.02966945,0.006505768,0.001183229,0.00036923317],"about_ca_topic_score_codex":0.0012463757,"about_ca_topic_score_gemma":0.0019772763,"teacher_disagreement_score":0.8736809,"about_ca_system_score_codex":0.00047660305,"about_ca_system_score_gemma":0.00026794864,"threshold_uncertainty_score":0.99913675},"labels":[],"label_agreement":null},{"id":"W4390831799","doi":"10.1109/tiv.2024.3353254","title":"V2VFormer: Vehicle-to-Vehicle Cooperative Perception With Spatial-Channel Transformer","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","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 Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Information retrieval","score_opus":0.011501496264136315,"score_gpt":0.22601023268377193,"score_spread":0.2145087364196356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390831799","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":[0.2992762,0.00015188404,0.696198,0.00056686095,0.0005279856,0.00048353226,0.0000787434,0.0018330353,0.0008838047],"genre_scores_gemma":[0.9977317,0.00050049875,0.0004085627,0.00022161916,0.00007904254,0.00025113212,0.000008998544,0.00012837723,0.0006700871],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99814266,0.000039245035,0.0004375732,0.0005259924,0.00029225883,0.0005622608],"domain_scores_gemma":[0.99925005,0.00010801399,0.000017473772,0.00034239603,0.00008029255,0.0002017547],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016377,0.00045313867,0.00032831606,0.00046664666,0.00031266842,0.00010116941,0.00023956542,0.00031082088,0.00048588187],"category_scores_gemma":[0.0000016245465,0.00039277447,0.00017425121,0.0006478638,0.00016682806,0.00033947875,0.0000013295113,0.0008393987,0.0015713794],"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":[0.00032739487,0.00020127729,0.000017358396,0.00016152419,0.00042352543,0.00005363287,0.004100196,0.42575434,0.041210823,0.00033127939,0.00039147315,0.5270272],"study_design_scores_gemma":[0.0004991944,0.0012716807,0.0004981493,0.00042395096,0.00018010466,0.00007937882,0.0014588749,0.37161785,0.61540586,0.0003241048,0.00719625,0.0010446013],"about_ca_topic_score_codex":0.000100484445,"about_ca_topic_score_gemma":0.00044672165,"teacher_disagreement_score":0.6984555,"about_ca_system_score_codex":0.000295795,"about_ca_system_score_gemma":0.00006056246,"threshold_uncertainty_score":0.9998524},"labels":[],"label_agreement":null},{"id":"W4392124644","doi":"10.1109/tiv.2024.3369324","title":"Uncertainty-Aware Decision Making and Planning for ICV Based on Asymmetric Driving Aggressiveness","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Psychology","score_opus":0.02262730012647406,"score_gpt":0.29500859970038934,"score_spread":0.27238129957391527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392124644","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":[0.14001462,0.00018762732,0.8577242,0.00006233431,0.0010721695,0.0002989778,0.00009711955,0.00046928294,0.000073682684],"genre_scores_gemma":[0.9977677,0.00006310904,0.0017747128,0.00009363276,0.000049603277,0.00016094543,0.000014416782,0.00004958131,0.000026323723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897176,0.000014287815,0.00032023867,0.00028712524,0.00019164362,0.00021491693],"domain_scores_gemma":[0.99870324,0.0009724592,0.000021761416,0.00016663095,0.00007605815,0.00005982033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015769593,0.0001988899,0.00015389582,0.0007208496,0.00019860783,0.00011730557,0.00008835641,0.000104992556,0.000042061565],"category_scores_gemma":[0.000013708366,0.00019637575,0.00010987606,0.00062696636,0.000033484153,0.00011562517,3.8685795e-7,0.00025419035,0.000015066245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004520597,0.00003826307,0.000062705105,0.00014112763,0.000039326413,0.0000056861622,0.00023223957,0.74143994,0.00033134373,0.00019453319,0.00008394681,0.25738567],"study_design_scores_gemma":[0.00022105948,0.000108691864,0.00064673787,0.0016311249,0.00005962912,0.0000029811754,0.00029854503,0.9725467,0.021663392,0.00034940033,0.002206895,0.00026482774],"about_ca_topic_score_codex":0.0000042206834,"about_ca_topic_score_gemma":0.000032689637,"teacher_disagreement_score":0.85775304,"about_ca_system_score_codex":0.000111782014,"about_ca_system_score_gemma":0.000029940918,"threshold_uncertainty_score":0.8007971},"labels":[],"label_agreement":null},{"id":"W4393032789","doi":"10.1109/tiv.2024.3379730","title":"Robust Car-Following Control of Connected and Autonomous Vehicles: A Stochastic Model Predictive Control Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of British Columbia","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Model predictive control; Control (management); Computer science; Control theory (sociology); Control engineering; Engineering; Artificial intelligence","score_opus":0.01351126331061037,"score_gpt":0.20279885268892994,"score_spread":0.18928758937831958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393032789","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":[0.14484887,0.0015612072,0.85111743,0.00003329239,0.00056125264,0.0007157554,0.0004038173,0.0005645964,0.0001938006],"genre_scores_gemma":[0.9991093,0.00006763622,0.00026447928,0.00003090258,0.000058307924,0.00027353072,0.000005452215,0.00009861915,0.000091736605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981399,0.00007074855,0.00062033295,0.00045025226,0.0003091008,0.0004096691],"domain_scores_gemma":[0.99907565,0.0003416246,0.000050049483,0.00028126355,0.00008123122,0.00017017059],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027143204,0.0003832674,0.00058226945,0.00032136103,0.00012772929,0.00011346061,0.0001836487,0.00021899716,0.000009139179],"category_scores_gemma":[0.000006070193,0.0003725804,0.00030799638,0.00026359456,0.00010091609,0.00018394252,0.0000015379869,0.00047085746,0.0000113968845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014092358,0.00009702838,0.000008545131,0.00023612862,0.00078722666,0.000008577675,0.00083477166,0.97839224,0.011841016,0.00046146475,0.00001236013,0.0071797227],"study_design_scores_gemma":[0.0011563445,0.00018267895,0.000020020498,0.00024325961,0.00040583985,0.000019432937,0.00044089858,0.9951951,0.0018559189,0.00014327418,0.000009984254,0.00032728532],"about_ca_topic_score_codex":0.00006776956,"about_ca_topic_score_gemma":0.00001766488,"teacher_disagreement_score":0.85426044,"about_ca_system_score_codex":0.00017834324,"about_ca_system_score_gemma":0.00007105722,"threshold_uncertainty_score":0.9998726},"labels":[],"label_agreement":null},{"id":"W4393032793","doi":"10.1109/tiv.2024.3380000","title":"Distributed Robust Learning Based Formation Control of Mobile Robots Based on Bioinspired Neural Dynamics","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; McMaster University; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dynamics (music); Computer science; Mobile robot; Control (management); Artificial intelligence; Robot; Control engineering; Engineering; Psychology","score_opus":0.011662574693332229,"score_gpt":0.2280762721392409,"score_spread":0.21641369744590866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393032793","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":[0.01159822,0.000079819736,0.98648477,0.00009857026,0.00028058072,0.00035129176,0.00048358444,0.0005831114,0.000040083643],"genre_scores_gemma":[0.9976918,0.00004014144,0.0018250211,0.000027558,0.000024390203,0.00021946519,0.000105212035,0.000043793534,0.000022595583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907607,0.000026144695,0.00033988027,0.00018634844,0.00017399467,0.00019756457],"domain_scores_gemma":[0.99944395,0.00021689395,0.000035872363,0.0001830456,0.00005364567,0.00006661619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000070636845,0.00018991042,0.00017526015,0.00020853929,0.00012204411,0.000043997665,0.0000998534,0.000084688225,0.000049965496],"category_scores_gemma":[0.0000028516272,0.00018681416,0.00015270624,0.00037693526,0.000042344916,0.00014035148,3.920978e-7,0.00032827334,0.000032030282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025949003,0.00008641001,0.0000036397232,0.00009329275,0.000021237778,0.000001559784,0.0000280049,0.924334,0.0022613693,0.000054449403,0.000011608875,0.07307846],"study_design_scores_gemma":[0.00021418242,0.00015805224,0.000016852024,0.00011744634,0.000038162896,0.0000011539931,0.00007712073,0.9092867,0.08950322,0.000020832347,0.00041025013,0.0001560332],"about_ca_topic_score_codex":0.000009516699,"about_ca_topic_score_gemma":0.000019794215,"teacher_disagreement_score":0.9860936,"about_ca_system_score_codex":0.0001833538,"about_ca_system_score_gemma":0.000017924252,"threshold_uncertainty_score":0.761806},"labels":[],"label_agreement":null},{"id":"W4394585820","doi":"10.1109/tiv.2024.3385789","title":"Classification of User Preference for Self-Driving Mode and Behaviors of Autonomous Vehicle","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","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":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preference; Self driving; Mode (computer interface); Psychology; Mode choice; Computer science; Human–computer interaction; Automotive engineering; Transport engineering; Engineering; Mathematics; Statistics","score_opus":0.02425153808048602,"score_gpt":0.2587284482333635,"score_spread":0.23447691015287747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394585820","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":[0.6116086,0.00016147956,0.38730106,0.000033489978,0.00015919386,0.00020396919,0.000049740316,0.00043027464,0.00005219159],"genre_scores_gemma":[0.997188,0.00037887783,0.0022160965,0.0000033708327,0.000009714744,0.00009496392,0.000002518172,0.000033939174,0.00007256079],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991468,0.0000145965705,0.0003731243,0.00021214368,0.00008874118,0.00016455226],"domain_scores_gemma":[0.999506,0.00016200512,0.000036559213,0.00020758281,0.000047451584,0.00004042728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010890987,0.00014801256,0.00019595644,0.00021174832,0.00006153577,0.0000131489005,0.0001262681,0.0001823912,0.000019332105],"category_scores_gemma":[0.0000021541819,0.0001515045,0.000093543546,0.00016381891,0.00009679669,0.00012406573,0.0000015179406,0.00021625732,0.000006557394],"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":[0.000104870756,0.00049719965,0.0012093246,0.0013369323,0.00049564557,0.000002547088,0.0032536434,0.13848062,0.46261075,0.010789886,0.000093216506,0.38112536],"study_design_scores_gemma":[0.00010287788,0.0001423755,0.0018550149,0.00011968273,0.00010006477,0.0000034975594,0.00014182401,0.3967851,0.5997432,0.00047089497,0.00038960073,0.00014583349],"about_ca_topic_score_codex":0.000015303678,"about_ca_topic_score_gemma":0.00003466032,"teacher_disagreement_score":0.38557935,"about_ca_system_score_codex":0.00006882564,"about_ca_system_score_gemma":0.000031556836,"threshold_uncertainty_score":0.61781746},"labels":[],"label_agreement":null},{"id":"W4394744983","doi":"10.1109/tiv.2024.3386915","title":"Extending Operational Design Domain for Perception Systems Through Robust Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Neural Networks and Applications","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Perception; Domain (mathematical analysis); Computer science; Human–computer interaction; Systems engineering; Cognitive science; Psychology; Engineering; Mathematics","score_opus":0.06804657316376415,"score_gpt":0.2956738788484827,"score_spread":0.2276273056847185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394744983","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":[0.0020576986,0.00022753824,0.994705,0.0008463492,0.0010785557,0.0005748323,0.0000098537685,0.00042575938,0.000074407144],"genre_scores_gemma":[0.91279465,0.0002886605,0.08508508,0.00010782463,0.000208571,0.00054031983,0.0000050993026,0.000024540304,0.0009452395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873465,0.00009113481,0.00027540704,0.00045077837,0.00020793414,0.00024008467],"domain_scores_gemma":[0.999246,0.00039161256,0.00003180055,0.00020616183,0.00006650784,0.00005792175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002884825,0.00015954135,0.000121702404,0.00010803426,0.0005847874,0.0005989533,0.0002960343,0.00007361416,0.00003202794],"category_scores_gemma":[0.0000019362285,0.00014358625,0.00013208836,0.00033488247,0.000035697394,0.0005275101,0.00000238414,0.00025504144,0.000132857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011051738,0.00005592069,7.7093074e-7,0.00002963382,0.000028999726,0.0000020324112,0.0005255145,0.9095921,0.00524708,0.035528686,0.0006769332,0.04830129],"study_design_scores_gemma":[0.000070544396,0.00014982966,0.0000052169466,0.00012163915,0.000015631791,0.000026746025,0.00022993363,0.97621095,0.008253646,0.0022166376,0.012511761,0.00018748245],"about_ca_topic_score_codex":0.000019907457,"about_ca_topic_score_gemma":0.000002638513,"teacher_disagreement_score":0.910737,"about_ca_system_score_codex":0.00011067165,"about_ca_system_score_gemma":0.000043300308,"threshold_uncertainty_score":0.5855277},"labels":[],"label_agreement":null},{"id":"W4394862936","doi":"10.1109/tiv.2024.3389640","title":"An Online Spatial-Temporal Graph Trajectory Planner for Autonomous Vehicles","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Robotic Path Planning 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":"Planner; Trajectory; Computer science; Graph; Artificial intelligence; Theoretical computer science; Physics","score_opus":0.04757777007765936,"score_gpt":0.2944212223293077,"score_spread":0.2468434522516483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394862936","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":[0.026325757,0.0003414725,0.9676367,0.0005489091,0.0030918317,0.00045645126,0.00025532933,0.0013141653,0.000029375857],"genre_scores_gemma":[0.8977498,0.00005416302,0.101055905,0.00024711172,0.00026104052,0.0001306263,0.000036469777,0.000060595,0.00040431757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757004,0.00011525065,0.00051779137,0.00087055017,0.0003863694,0.00054000143],"domain_scores_gemma":[0.99848604,0.00040004333,0.00006728118,0.0006954608,0.00009452202,0.00025662355],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037335226,0.0003691751,0.00030705487,0.0005437476,0.00030352955,0.00037930865,0.0008944814,0.00018995334,0.00002706661],"category_scores_gemma":[0.000005407837,0.00034569323,0.00029809,0.0004580555,0.00010582244,0.00055290584,0.0000035383525,0.00044829524,0.000114491406],"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":[0.00009286448,0.0010215043,0.000045376284,0.00013274558,0.00021709259,0.00011207781,0.0024253367,0.24400881,0.0041322242,0.0008520739,0.0008895061,0.7460704],"study_design_scores_gemma":[0.00024191476,0.00090753473,0.00024974498,0.00017447362,0.00005530526,0.000053009262,0.00013826533,0.93481874,0.058130533,0.0015104663,0.0032110363,0.0005089738],"about_ca_topic_score_codex":0.00031861925,"about_ca_topic_score_gemma":0.00012372859,"teacher_disagreement_score":0.871424,"about_ca_system_score_codex":0.00012922593,"about_ca_system_score_gemma":0.00019619969,"threshold_uncertainty_score":0.9998995},"labels":[],"label_agreement":null},{"id":"W4396909936","doi":"10.1109/tiv.2024.3401051","title":"Bayesian Fault Injection Safety Testing for Highly Automated Vehicles With Uncertainty","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"National Natural Science Foundation of China","keywords":"Monte Carlo method; Computer science; Fault (geology); Bayesian probability; Reliability engineering; Collision; Bayesian network; Dynamic Bayesian network; Reliability (semiconductor); Software deployment; Simulation; Data mining; Engineering; Artificial intelligence; Statistics","score_opus":0.06249454754738269,"score_gpt":0.3446773087093376,"score_spread":0.2821827611619549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396909936","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":[0.051830288,0.00016453456,0.9430534,0.0017638606,0.0008073741,0.0004801533,0.00019656829,0.0012104771,0.0004933383],"genre_scores_gemma":[0.992309,0.0001175464,0.005215358,0.00014180201,0.000121265366,0.00011016971,0.000010698766,0.0000515946,0.0019225714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99612373,0.0002334408,0.0010396237,0.00097266195,0.0011518671,0.00047866645],"domain_scores_gemma":[0.99454033,0.003926235,0.00016191209,0.000584642,0.00058653345,0.00020033242],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016148302,0.00036795667,0.00046220105,0.0008951121,0.00085455325,0.0006381303,0.0005151675,0.00019578777,0.00017326916],"category_scores_gemma":[0.00017146587,0.0002563552,0.0004245064,0.0027944043,0.00019427293,0.0004956687,0.0000032994033,0.00041085653,0.00034401807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043315176,0.00011556907,0.00009268399,0.000023799026,0.00018598586,0.000010545732,0.0005304895,0.5537703,0.0011253868,0.00021812417,0.0007932356,0.4427007],"study_design_scores_gemma":[0.00028448176,0.00082639186,0.00024078721,0.00020616029,0.00021926295,0.000039437702,0.001448541,0.9569658,0.023593698,0.0060912743,0.00967269,0.00041149848],"about_ca_topic_score_codex":0.00044835618,"about_ca_topic_score_gemma":0.00086907827,"teacher_disagreement_score":0.9404787,"about_ca_system_score_codex":0.0002378411,"about_ca_system_score_gemma":0.00020378277,"threshold_uncertainty_score":0.99998885},"labels":[],"label_agreement":null},{"id":"W4398788580","doi":"10.1109/tiv.2024.3405330","title":"Uncertainty-Aware DRL for Autonomous Vehicle Crowd Navigation in Shared Space","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Space vehicle; Space (punctuation); Human–computer interaction; Aeronautics; Aerospace engineering; Engineering; Operating system","score_opus":0.015612254480999774,"score_gpt":0.24963387219144087,"score_spread":0.2340216177104411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398788580","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":[0.23771162,0.000927978,0.7569015,0.00037652525,0.0016991443,0.0008348844,0.00018903943,0.0011993517,0.0001599346],"genre_scores_gemma":[0.9979409,0.00020427055,0.0006238708,0.000048905586,0.00014924051,0.00038905922,0.000052037554,0.00012555087,0.00046618207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982763,0.000041855852,0.00045669594,0.00045190554,0.00023129224,0.0005419854],"domain_scores_gemma":[0.9991771,0.0002799857,0.000023665987,0.00031737436,0.00006494625,0.00013692839],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024314436,0.00033214476,0.00027211418,0.00032379566,0.00012062217,0.0001897944,0.00021853848,0.00022870074,0.00014773433],"category_scores_gemma":[0.0000041208928,0.00035979677,0.0002377495,0.00053280394,0.000051478957,0.00028208428,0.000001726739,0.0005502643,0.00025983257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048601367,0.00006639807,0.000007240099,0.00024338471,0.00009175432,0.000026757738,0.0006217401,0.943079,0.003702621,0.00016927607,0.0008977262,0.0510455],"study_design_scores_gemma":[0.0002503632,0.00009997086,0.000029264944,0.00046807493,0.000044940327,0.000013367525,0.00016657567,0.9160077,0.070629604,0.0004930061,0.011439944,0.00035718456],"about_ca_topic_score_codex":0.00009344896,"about_ca_topic_score_gemma":0.0004048024,"teacher_disagreement_score":0.76022923,"about_ca_system_score_codex":0.000578904,"about_ca_system_score_gemma":0.00006234808,"threshold_uncertainty_score":0.9998854},"labels":[],"label_agreement":null},{"id":"W4399073776","doi":"10.1109/tiv.2024.3406582","title":"A Terramechanics-based Dynamic Model for Motion Control of Unmanned Tracked Vehicles","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Soil Mechanics and Vehicle Dynamics","field":"Engineering","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":"Motion control; Computer science; Motion (physics); Control engineering; Automotive engineering; Control (management); Aerospace engineering; Control theory (sociology); Engineering; Artificial intelligence; Robot","score_opus":0.018145328043014015,"score_gpt":0.2511110525994274,"score_spread":0.23296572455641337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399073776","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":[0.1432349,0.00024139494,0.853936,0.00011317944,0.000848892,0.00054087065,0.00048778014,0.000584701,0.000012284341],"genre_scores_gemma":[0.9977533,0.00017353865,0.0015204446,0.00006020054,0.000026262338,0.0002442493,0.000018828707,0.00012255959,0.00008062015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851036,0.00002705153,0.0005198461,0.00034682994,0.00024003432,0.00035588758],"domain_scores_gemma":[0.99923646,0.00021377359,0.00004198932,0.000305352,0.00010032911,0.00010209696],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023720761,0.00030897296,0.00032102992,0.00038255425,0.000095714706,0.00006667106,0.00020789105,0.00022802244,0.000019337089],"category_scores_gemma":[0.0000049374153,0.00031961437,0.00038078034,0.0002700123,0.000039438193,0.00013377244,7.438013e-7,0.0003087664,0.000021847029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013034351,0.00013258457,5.330913e-7,0.00034777314,0.00014663782,0.0000017666196,0.00021768165,0.7740277,0.02576847,0.0011251449,0.000015848358,0.19808553],"study_design_scores_gemma":[0.00048173155,0.00019863751,0.0000021127435,0.00019578499,0.00013900352,0.0000024820074,0.000085054926,0.9034219,0.092384756,0.0027698872,0.000046960104,0.000271689],"about_ca_topic_score_codex":0.000010066519,"about_ca_topic_score_gemma":0.000059356284,"teacher_disagreement_score":0.8545184,"about_ca_system_score_codex":0.00019276298,"about_ca_system_score_gemma":0.000052366566,"threshold_uncertainty_score":0.9999256},"labels":[],"label_agreement":null},{"id":"W4399125187","doi":"10.1109/tiv.2024.3406951","title":"Resilient Neural-Sliding-Mode Distance Regulation in Merging Control of Heterogeneous Connected Automated Vehicles Subject to Deception Attacks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Smart Grid Security and Resilience","field":"Engineering","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":"Deception; Subject (documents); Mode (computer interface); Computer science; Control (management); Psychology; Computer security; Artificial intelligence; Cognitive psychology; Social psychology; Human–computer interaction; World Wide Web","score_opus":0.011525600332537663,"score_gpt":0.2596556880437592,"score_spread":0.24813008771122155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399125187","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":[0.613602,0.0004429877,0.3840972,0.00007714301,0.00073124934,0.0002966682,0.000048158796,0.0006869174,0.000017673196],"genre_scores_gemma":[0.9993932,0.00024461854,0.00013634264,0.00003110027,0.000043752065,0.00007015627,0.000006588234,0.00004744247,0.000026793856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983447,0.000094164694,0.0005601608,0.0003660277,0.0002968637,0.00033804862],"domain_scores_gemma":[0.999265,0.00028023886,0.000035154473,0.00025101044,0.00006325476,0.00010535022],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000174929,0.00025009547,0.00028102924,0.0005004916,0.000096608404,0.00006280731,0.00016738813,0.00013809263,0.000045667726],"category_scores_gemma":[0.00001138215,0.0002529854,0.00014050853,0.00073461985,0.00005393766,0.00017571448,0.0000015884511,0.00028431235,0.00005083994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014581726,0.00006831269,0.00008668209,0.00014766882,0.000049333878,0.00001313266,0.0011702846,0.85846806,0.13069966,0.00006916668,0.00006606465,0.009015835],"study_design_scores_gemma":[0.00013920332,0.0000843931,0.00053963193,0.00030863428,0.000021451891,0.000008359554,0.000087211774,0.61954767,0.3788887,0.000038004026,0.00016949796,0.00016728276],"about_ca_topic_score_codex":0.00008671061,"about_ca_topic_score_gemma":0.000634565,"teacher_disagreement_score":0.3857912,"about_ca_system_score_codex":0.00023345731,"about_ca_system_score_gemma":0.000022580147,"threshold_uncertainty_score":0.99999225},"labels":[],"label_agreement":null},{"id":"W4399400291","doi":"10.1109/tiv.2024.3409468","title":"Car-Following Models: A Multidisciplinary Review","year":2024,"lang":"en","type":"review","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Transportation and Mobility Innovations","field":"Engineering","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":"University of New Brunswick","funders":"U.S. Department of Homeland Security; National Science Foundation","keywords":"Multidisciplinary approach; Psychology; Sociology; Social science","score_opus":0.08055116983468454,"score_gpt":0.33946614836292005,"score_spread":0.25891497852823553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399400291","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000035144856,0.83438367,0.1607605,0.00003039925,0.0019889702,0.001030996,0.00036190508,0.0010070488,0.0004329946],"genre_scores_gemma":[0.00045590068,0.99722,0.00031622226,0.000050631024,0.0000595615,0.0009348306,0.000114113835,0.0001882367,0.0006605083],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99761456,0.000053365133,0.0011742573,0.00049916597,0.0003195915,0.00033903867],"domain_scores_gemma":[0.99907225,0.00011566447,0.00006381834,0.00056610757,0.00005355528,0.0001286255],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021230322,0.0006567085,0.0012138576,0.0005117801,0.00013777122,0.000058934726,0.00030956126,0.00032240973,0.00013203199],"category_scores_gemma":[0.0000018644863,0.000575004,0.0014585461,0.0010009895,0.000043526125,0.00017094264,0.0000012794588,0.0010711788,0.00092644687],"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":[0.0000016190888,0.0000943113,1.4787436e-8,0.07438466,0.000642436,0.000027916536,0.00018835167,0.018208649,0.000001823672,0.00019946828,0.0011461695,0.9051046],"study_design_scores_gemma":[0.000070825976,0.00004251249,6.607663e-8,0.11081163,0.005705035,0.00002584405,0.00006413648,0.005582374,0.00008388873,0.0002021346,0.87649983,0.0009117436],"about_ca_topic_score_codex":0.000011203583,"about_ca_topic_score_gemma":0.000040296396,"teacher_disagreement_score":0.90419286,"about_ca_system_score_codex":0.0002686191,"about_ca_system_score_gemma":0.0001266567,"threshold_uncertainty_score":0.99985147},"labels":[],"label_agreement":null},{"id":"W4399767129","doi":"10.1109/tiv.2024.3414653","title":"DynaNav-SVO: Dynamic Stereo Visual Odometry With Semantic-Aware Perception for Autonomous Navigation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visual odometry; Computer vision; Artificial intelligence; Computer science; Perception; Odometry; Robot; Psychology; Mobile robot; Neuroscience","score_opus":0.011417436237330856,"score_gpt":0.25712663232042837,"score_spread":0.24570919608309752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399767129","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":[0.22732288,0.000076834884,0.7707866,0.00006746249,0.0007241639,0.00036591355,0.000064687534,0.000563086,0.000028402555],"genre_scores_gemma":[0.9977055,0.00015455193,0.0014472326,0.000028567549,0.000057729954,0.000082221784,0.000095429044,0.00010191,0.00032684163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988777,0.00002371844,0.0003140478,0.00031039552,0.00021237523,0.0002617612],"domain_scores_gemma":[0.99953157,0.00011380841,0.000022602373,0.00017002693,0.00007962444,0.00008235799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000982992,0.00024838373,0.00018152004,0.00033269654,0.00015427609,0.00016369077,0.00008560101,0.00014268816,0.00007985444],"category_scores_gemma":[0.0000012031558,0.00023074598,0.00013288985,0.00037346134,0.000044417877,0.00019247671,5.7074004e-7,0.0002296873,0.000105612875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056403194,0.000077014345,0.000008444437,0.00040833303,0.00012216454,0.0000065865165,0.00037975068,0.88527304,0.006628546,0.00006773784,0.000052219908,0.10691977],"study_design_scores_gemma":[0.00016696613,0.0003213129,0.000067070265,0.0003330238,0.00010960236,0.00001702508,0.00039914183,0.9665287,0.031302597,0.000078210265,0.00039145365,0.0002848801],"about_ca_topic_score_codex":0.000022660883,"about_ca_topic_score_gemma":0.00007104177,"teacher_disagreement_score":0.77038264,"about_ca_system_score_codex":0.00031839844,"about_ca_system_score_gemma":0.00003119947,"threshold_uncertainty_score":0.9409548},"labels":[],"label_agreement":null},{"id":"W4400488018","doi":"10.1109/tiv.2024.3425811","title":"Accurate Detection and Localization of Individual Free Street Parking Spaces Using AI and Innovative Global Motion Estimation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Telus","keywords":"Motion (physics); Estimation; Artificial intelligence; Computer science; Computer vision; Geography; Engineering; Systems engineering","score_opus":0.034638674272956076,"score_gpt":0.3021070232837427,"score_spread":0.26746834901078664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400488018","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":[0.40684733,0.00022328795,0.5923067,0.00000985352,0.00028931123,0.0001441225,0.000046117457,0.00012188869,0.00001138126],"genre_scores_gemma":[0.9992895,0.000108468026,0.0005119609,0.0000053867006,0.000035418354,0.000018091761,0.0000044134486,0.000023084953,0.0000036888432],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989872,0.00006409026,0.00030090328,0.00020814748,0.00028434128,0.00015533796],"domain_scores_gemma":[0.999604,0.00009401122,0.000035142173,0.00012119689,0.00010182556,0.000043817017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026257758,0.00015190813,0.00014170723,0.00031990113,0.000111380075,0.00017668298,0.000070228816,0.0001115735,0.0000069638468],"category_scores_gemma":[0.000014679187,0.00015781928,0.00002642815,0.00074184657,0.00007989861,0.00035764865,0.0000033968295,0.00019059099,0.0000032303012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024647488,0.000020232408,0.0005398625,0.00039179492,0.00013498335,0.000002099723,0.00072464877,0.70478296,0.0057126195,0.000102186226,0.000016060707,0.28754792],"study_design_scores_gemma":[0.000097707634,0.000048579426,0.00055268774,0.00029739802,0.000034177338,0.000012973711,0.00026537746,0.82407725,0.17425324,0.00021186797,0.00003756356,0.00011115338],"about_ca_topic_score_codex":0.00015432075,"about_ca_topic_score_gemma":0.00021398136,"teacher_disagreement_score":0.59244215,"about_ca_system_score_codex":0.00015100493,"about_ca_system_score_gemma":0.000021778575,"threshold_uncertainty_score":0.64356834},"labels":[],"label_agreement":null},{"id":"W4400770599","doi":"10.1109/tiv.2024.3429489","title":"Constructing Context-Aware GNSS Stochastic Model for Code-Based Resilient Positioning in Urban Environment","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Natural Science Foundation of China","keywords":"GNSS applications; Computer science; Context (archaeology); Code (set theory); Global Positioning System; Geography; Telecommunications; Programming language","score_opus":0.019914425487551176,"score_gpt":0.23594797763381728,"score_spread":0.2160335521462661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400770599","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":[0.020371493,0.00031048298,0.977267,0.0001572631,0.00042406464,0.00043500276,0.0002213891,0.00077862816,0.000034641937],"genre_scores_gemma":[0.9979884,0.000038437323,0.0014862369,0.00004634707,0.000019702959,0.0002448556,0.000017672275,0.00005468849,0.000103686056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989334,0.000015951899,0.000328143,0.00028142924,0.00015596698,0.00028514426],"domain_scores_gemma":[0.9995264,0.00020605956,0.000019599538,0.0001770508,0.000023312738,0.00004757522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000110089466,0.00021052359,0.00017865309,0.00036517478,0.00012379608,0.000070929695,0.00012291117,0.0001458035,0.000036626014],"category_scores_gemma":[0.000004650855,0.00022355745,0.00012100949,0.00017221812,0.000091285474,0.000095486364,0.0000011481726,0.00029163333,0.00003307496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035029316,0.00003544615,0.00000919681,0.000110337416,0.000033273933,0.0000033375468,0.00038628903,0.9648369,0.00064980175,0.0012891036,0.000120601704,0.032490704],"study_design_scores_gemma":[0.00019512359,0.00005258381,0.0000014204782,0.00025071157,0.000027474174,0.0000031064733,0.0007399789,0.837674,0.16039164,0.00035871737,0.00011170589,0.00019351068],"about_ca_topic_score_codex":0.0000074662894,"about_ca_topic_score_gemma":0.00007161735,"teacher_disagreement_score":0.9776169,"about_ca_system_score_codex":0.00041760626,"about_ca_system_score_gemma":0.000030231507,"threshold_uncertainty_score":0.9116408},"labels":[],"label_agreement":null},{"id":"W4400905112","doi":"10.1109/tiv.2024.3432075","title":"Intelligent EV Charging Control and Management: From Microscale Battery Cell to Macroscale Grid Synergy","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Advanced Battery Technologies Research","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":"Université du Québec à Montréal","funders":"","keywords":"Microscale chemistry; Battery (electricity); Control (management); Grid; Grid cell; Computer science; Automotive engineering; Engineering; Physics; Artificial intelligence; Power (physics); Geography","score_opus":0.011896633280409649,"score_gpt":0.24658170142067565,"score_spread":0.234685068140266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400905112","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":[0.08979047,0.001499422,0.9040027,0.00044913517,0.0014884209,0.0005360679,0.00035845602,0.0012998797,0.00057541975],"genre_scores_gemma":[0.991561,0.0044628032,0.0020592287,0.00023135109,0.00009422394,0.0002815123,0.000008512477,0.000114973605,0.0011864043],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980791,0.000033227454,0.0003972787,0.0006031974,0.0002885163,0.0005986744],"domain_scores_gemma":[0.99905044,0.00026591346,0.000015917247,0.00045365965,0.000028618662,0.00018545194],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011411856,0.0003670857,0.00028208017,0.00058066857,0.00015654886,0.00022049501,0.00036625538,0.00015226153,0.00024569343],"category_scores_gemma":[0.000002325207,0.00037199428,0.00012754908,0.00043832205,0.00011149405,0.00019255497,0.000009599568,0.0005736436,0.00079085754],"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":[0.00013808272,0.00018748354,0.00003832075,0.0006968461,0.00045657592,0.0001883608,0.0006588362,0.23071302,0.27975076,0.0001604127,0.002929234,0.48408204],"study_design_scores_gemma":[0.00019881538,0.000101853606,0.000048961767,0.00034380963,0.000056997444,0.000009219041,0.0003767727,0.048190378,0.9202817,0.00043618755,0.029506978,0.00044829916],"about_ca_topic_score_codex":0.000030276591,"about_ca_topic_score_gemma":0.000032805245,"teacher_disagreement_score":0.9019435,"about_ca_system_score_codex":0.00025528483,"about_ca_system_score_gemma":0.00000774258,"threshold_uncertainty_score":0.9999871},"labels":[],"label_agreement":null},{"id":"W4401246758","doi":"10.1109/tiv.2024.3436599","title":"XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting Considering High Volatility","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Probabilistic logic; Volatility (finance); Term (time); Econometrics; Probabilistic forecasting; Economics; Computer science; Artificial intelligence; Physics","score_opus":0.035891139015439645,"score_gpt":0.2631902058648972,"score_spread":0.22729906684945758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401246758","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":[0.17970625,0.00030759754,0.81239516,0.00003985802,0.0020294054,0.0009809864,0.000053767562,0.0044148345,0.000072144125],"genre_scores_gemma":[0.99269927,0.00022510244,0.0063134027,0.000031750496,0.00021164717,0.000386783,0.000020576917,0.00006495191,0.000046507434],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845827,0.000050861436,0.00047254082,0.00044525808,0.00018919338,0.00038390065],"domain_scores_gemma":[0.999228,0.00024835282,0.000023687393,0.00029669443,0.00006836372,0.00013489558],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036703236,0.0003102951,0.00029396187,0.00016207047,0.00023120281,0.00016555944,0.00014039098,0.00015131026,0.00003816705],"category_scores_gemma":[0.0000080823265,0.0003012689,0.00020486861,0.00019666768,0.0000644078,0.00025136315,0.0000028436853,0.0003902296,0.00000722053],"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":[0.00032328485,0.0002826551,0.000042672385,0.0012100858,0.0006002786,0.000027791724,0.0006557856,0.40365937,0.0012641938,0.0012205952,0.0043718615,0.58634144],"study_design_scores_gemma":[0.00018483115,0.00020591504,0.00019611638,0.00038770717,0.00024961418,0.000019522953,0.000058015117,0.97274673,0.022017982,0.0020263079,0.0015592481,0.0003480318],"about_ca_topic_score_codex":0.000008960652,"about_ca_topic_score_gemma":0.00022922562,"teacher_disagreement_score":0.81299305,"about_ca_system_score_codex":0.00016770525,"about_ca_system_score_gemma":0.00002053742,"threshold_uncertainty_score":0.999944},"labels":[],"label_agreement":null},{"id":"W4401607529","doi":"10.1109/tiv.2024.3443755","title":"ReSeleCT: A New Approach for Continual Learning With Application to Vehicle State Estimation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Fault Detection and Control Systems","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 Ottawa; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"State (computer science); Estimation; Computer science; Artificial intelligence; Machine learning; Engineering; Systems engineering; Algorithm","score_opus":0.012265978024806608,"score_gpt":0.24826521653155428,"score_spread":0.23599923850674767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401607529","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":[0.022368245,0.00009504524,0.9750709,0.00009457996,0.00024130291,0.0008496059,0.000015306363,0.00096028973,0.00030472357],"genre_scores_gemma":[0.99316084,0.000019033176,0.003401523,0.000028629118,0.00006292639,0.00064366375,0.0000072146013,0.000062192725,0.0026139603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990456,0.000025284666,0.00024023728,0.0002845569,0.0001757746,0.0002285297],"domain_scores_gemma":[0.99956185,0.00010584149,0.00001686843,0.00014335787,0.00004788656,0.00012418379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015560578,0.00016842056,0.00015179215,0.00022280298,0.0001231253,0.00014829398,0.00008780782,0.0000657914,0.000016989386],"category_scores_gemma":[0.000004001276,0.00015541184,0.00007574225,0.0003968602,0.000013758213,0.00012797925,4.0511094e-7,0.00024351275,0.00019493415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012096985,0.0000189026,0.0000012083431,0.00007601257,0.00006431086,4.0323604e-7,0.00056190975,0.7170457,0.013150485,0.000037497888,0.00031459835,0.268608],"study_design_scores_gemma":[0.00019809025,0.00021063421,0.0000050595304,0.000059164704,0.00002939456,0.0000066871244,0.00017462083,0.87009656,0.115979224,0.000049389208,0.013022443,0.0001687298],"about_ca_topic_score_codex":0.00008831092,"about_ca_topic_score_gemma":0.000081595295,"teacher_disagreement_score":0.9716694,"about_ca_system_score_codex":0.00012772012,"about_ca_system_score_gemma":0.000030347152,"threshold_uncertainty_score":0.63375103},"labels":[],"label_agreement":null},{"id":"W4401878944","doi":"10.1109/tiv.2024.3449830","title":"AVATAR: Autonomous Vehicle Assessment Through Testing of Adversarial Patches in Real-Time","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Adversarial Robustness in Machine Learning","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Avatar; Adversarial system; Computer science; Human–computer interaction; Computer security; Artificial intelligence","score_opus":0.035103200692350976,"score_gpt":0.3060655858895797,"score_spread":0.27096238519722876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401878944","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":[0.08084205,0.000055437955,0.91509074,0.00035097997,0.0011825666,0.000260808,0.000013163217,0.0005184178,0.0016858443],"genre_scores_gemma":[0.90028965,0.00006367766,0.09923623,0.000039293485,0.0000818849,0.000034449193,0.0000018686973,0.000033874956,0.00021905583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976129,0.00020697054,0.0006630341,0.0006579378,0.00045810008,0.00040107055],"domain_scores_gemma":[0.9980925,0.0011005462,0.00011515638,0.00053731527,0.00008077387,0.00007375589],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00058393093,0.00027946613,0.00034305127,0.00033241895,0.00015972316,0.00015230029,0.0007274555,0.00014338981,0.000099331395],"category_scores_gemma":[0.00002883084,0.00027865657,0.00016676683,0.0010161765,0.0001125087,0.000775822,0.00001829548,0.00067723513,0.00012442213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005679972,0.00040864048,0.0003702983,0.00013579732,0.00011733096,0.000100325145,0.0036964773,0.75073224,0.039895672,0.005356338,0.000064681815,0.19906542],"study_design_scores_gemma":[0.0002928734,0.0002916008,0.00035020968,0.0003635716,0.000034158253,0.00001247995,0.00018304326,0.87807685,0.11705606,0.002581062,0.0004217225,0.00033639828],"about_ca_topic_score_codex":0.0011903498,"about_ca_topic_score_gemma":0.00004861176,"teacher_disagreement_score":0.81944764,"about_ca_system_score_codex":0.0003279835,"about_ca_system_score_gemma":0.00030270434,"threshold_uncertainty_score":0.99996656},"labels":[],"label_agreement":null},{"id":"W4402124582","doi":"10.1109/tiv.2024.3453208","title":"Coaxial Tilt-Rotor UAV: Fixed-Time Control, Mixer, and Flight Test","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Aerospace Engineering and Control Systems","field":"Engineering","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":"National Natural Science Foundation of China","keywords":"Rotor (electric); Coaxial; Tilt (camera); Flight test; Control theory (sociology); Fixed wing; Aerospace engineering; Computer science; Control (management); Engineering; Electrical engineering; Mechanical engineering; Artificial intelligence","score_opus":0.005065517978872086,"score_gpt":0.1885706563692885,"score_spread":0.1835051383904164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402124582","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":[0.08395944,0.0037831727,0.9055997,0.00028088092,0.0027464442,0.00055903295,0.00021177137,0.0022236356,0.000635932],"genre_scores_gemma":[0.9969621,0.00036939632,0.00009892325,0.000029009605,0.00025700938,0.00014937896,0.0000023671123,0.000086838954,0.0020450177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988457,0.00002395766,0.00030709285,0.00029028972,0.00020117969,0.0003317664],"domain_scores_gemma":[0.99921465,0.0003560101,0.000013798356,0.00022985468,0.000028376711,0.00015732892],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013260602,0.00030124263,0.0002929836,0.00017302901,0.00010615979,0.00016192153,0.00012678771,0.00015727781,0.00013087488],"category_scores_gemma":[0.000005825201,0.00028263155,0.0001487077,0.00018349913,0.000051167193,0.00013560086,8.643007e-7,0.00035963513,0.00074810395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016033689,0.0002841921,0.00010247843,0.00092918886,0.0012006387,0.00011449233,0.0015231735,0.57793885,0.27168202,0.00032008285,0.010128496,0.13561608],"study_design_scores_gemma":[0.0005799913,0.00029705447,0.00008081132,0.0004207561,0.00016775451,0.000058822283,0.00009917652,0.84820276,0.11568075,0.000041807496,0.033743873,0.0006264152],"about_ca_topic_score_codex":0.00003223176,"about_ca_topic_score_gemma":0.000016978844,"teacher_disagreement_score":0.9130026,"about_ca_system_score_codex":0.00010130706,"about_ca_system_score_gemma":0.000018369605,"threshold_uncertainty_score":0.99996257},"labels":[],"label_agreement":null},{"id":"W4402215265","doi":"10.1109/tiv.2024.3454608","title":"Evaluation of Control Modalities in Highly Automated Vehicles: A Virtual Reality Simulation-Based Study","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Virtual reality; Modalities; Human–computer interaction; Computer science; Control (management); Engineering; Computer graphics (images); Artificial intelligence; Sociology","score_opus":0.030444820827981476,"score_gpt":0.29360152264061756,"score_spread":0.26315670181263606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402215265","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":[0.6451245,0.00018986018,0.3523029,0.0000404639,0.00053811615,0.0008998754,0.00011634568,0.0007028841,0.000085035674],"genre_scores_gemma":[0.99954236,0.000010178571,0.000010487748,0.0000138347095,0.0000361317,0.00030322082,0.000005839319,0.000051815692,0.00002612528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99743223,0.0003879898,0.0007812024,0.000309965,0.0008344657,0.00025416716],"domain_scores_gemma":[0.9986475,0.0006724521,0.000048101,0.0003140184,0.00025177997,0.00006611905],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015957684,0.00025418468,0.000361615,0.00048531953,0.00006338126,0.000082417784,0.00015424327,0.0001336325,0.000047238103],"category_scores_gemma":[0.000016732105,0.00025440918,0.00015940558,0.00047420012,0.00004078136,0.00015472103,6.8812204e-7,0.0002690201,0.000028227949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080312944,0.0003466972,0.0001445237,0.00008659221,0.00025690653,0.0000046396503,0.0009724806,0.9652471,0.0016071043,0.00008768562,0.0000058403007,0.031160148],"study_design_scores_gemma":[0.0013059692,0.00027076373,0.0008183274,0.00020025173,0.00022803262,4.3121057e-7,0.000860943,0.99308276,0.0028619154,0.00013025638,0.000017998702,0.00022234146],"about_ca_topic_score_codex":0.00043360516,"about_ca_topic_score_gemma":0.00066532363,"teacher_disagreement_score":0.35441786,"about_ca_system_score_codex":0.00044387166,"about_ca_system_score_gemma":0.00012853942,"threshold_uncertainty_score":0.9999908},"labels":[],"label_agreement":null},{"id":"W4403294977","doi":"10.1109/tiv.2024.3478052","title":"An Online Self-Learning Graph-Based Lateral Controller for Self-Driving Cars","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Industrial Technology and Control Systems","field":"Engineering","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":"Self driving; Computer science; Psychology; Automotive engineering; Engineering","score_opus":0.01805492320974613,"score_gpt":0.244179544748987,"score_spread":0.22612462153924087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403294977","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":[0.2764739,0.0002998131,0.71578836,0.00013935014,0.0017909325,0.00060875097,0.00006359911,0.0047812006,0.0000540828],"genre_scores_gemma":[0.99839354,0.00007051915,0.000862561,0.00003888032,0.00021798354,0.00022575834,0.000010289799,0.00007806344,0.000102383805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987544,0.00006600183,0.00037157582,0.000300794,0.00013843717,0.00036882976],"domain_scores_gemma":[0.9994138,0.00023606588,0.00002468947,0.00018502142,0.000053255993,0.0000871928],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021822793,0.00026000725,0.00028205442,0.0003706818,0.00022014542,0.000105414016,0.00018119479,0.00034328684,0.000031979176],"category_scores_gemma":[0.0000031831232,0.00025112453,0.00024846024,0.00025914298,0.000029284247,0.00014348527,4.4218547e-7,0.0006696468,0.000043474025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011069453,0.0002786428,0.000081726794,0.00015272081,0.0007524689,0.00001455366,0.00055099034,0.8989014,0.012577706,0.00038635693,0.00013402668,0.08605872],"study_design_scores_gemma":[0.00063829176,0.00036113817,0.000016082073,0.00014947836,0.00016000858,0.0000071051522,0.00015425912,0.9365697,0.052222412,0.00014454692,0.009301353,0.00027562317],"about_ca_topic_score_codex":0.000024837478,"about_ca_topic_score_gemma":0.000063319574,"teacher_disagreement_score":0.72191966,"about_ca_system_score_codex":0.00013644532,"about_ca_system_score_gemma":0.000034796125,"threshold_uncertainty_score":0.9999941},"labels":[],"label_agreement":null},{"id":"W4404179575","doi":"10.1109/tiv.2024.3494873","title":"Vehicle-Specific Virtual Traffic Control Strategy to Reduce the Start-Up Delay for Autonomous Heavy Trucks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Transportation and Mobility Innovations","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":"British Columbia Institute of Technology; University of Windsor","funders":"","keywords":"Truck; Control (management); Automotive engineering; Computer science; Aeronautics; Transport engineering; Engineering","score_opus":0.029794587522929107,"score_gpt":0.2652304623690429,"score_spread":0.2354358748461138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404179575","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":[0.3045956,0.00024290738,0.6901182,0.0007365206,0.002030896,0.00092208333,0.0004908192,0.00079390395,0.00006912475],"genre_scores_gemma":[0.9978228,0.00013070798,0.00022465951,0.00023965508,0.00013537788,0.00067355664,0.000021056661,0.00007915742,0.0006729822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983923,0.0000332428,0.00058465794,0.00037753506,0.00022517549,0.0003870774],"domain_scores_gemma":[0.999006,0.00036981527,0.000020191566,0.00034879244,0.00011309026,0.00014212326],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023879852,0.00029195176,0.00022206809,0.00027752356,0.0002834794,0.0001925146,0.00023847248,0.00013386179,0.00021803103],"category_scores_gemma":[0.0000026983244,0.00025072563,0.00024124235,0.0005491474,0.0000847254,0.00016358888,3.8066065e-7,0.0004290359,0.00022133876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015031557,0.000077695295,3.1721345e-7,0.000036064946,0.0001481194,0.0000032939356,0.0013900831,0.7527284,0.0060338806,0.0024649685,0.0016436019,0.23532328],"study_design_scores_gemma":[0.0013967974,0.00147586,0.00017495106,0.00020844571,0.00029336617,0.00003313094,0.0034229022,0.5337974,0.2427607,0.0004423118,0.21489112,0.0011030063],"about_ca_topic_score_codex":0.000012543863,"about_ca_topic_score_gemma":0.00020620637,"teacher_disagreement_score":0.6932273,"about_ca_system_score_codex":0.00017576311,"about_ca_system_score_gemma":0.00008546684,"threshold_uncertainty_score":0.9999945},"labels":[],"label_agreement":null},{"id":"W4404562786","doi":"10.1109/tiv.2024.3502552","title":"Retraction Notice: Integrating Large Language Models and Metaverse in Autonomous Racing: An Education-Oriented Perspective","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Impact of AI and Big Data on Business and Society","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Perspective (graphical); Metaverse; Expression (computer science); Sociology; Human–computer interaction; Computer science; Artificial intelligence; Programming language; Virtual reality","score_opus":0.07202793197568509,"score_gpt":0.3963649011412965,"score_spread":0.32433696916561144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404562786","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":[0.45657238,0.00057305506,0.5392361,0.0006699801,0.001687377,0.00022336292,0.00011668387,0.00011074371,0.00081026944],"genre_scores_gemma":[0.99749106,0.00022474882,0.0009199288,0.00023845836,0.00010448412,0.000022605147,0.000011000518,0.000019283643,0.0009684498],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99789095,0.00016468536,0.00048935,0.0005971258,0.00058687286,0.0002710421],"domain_scores_gemma":[0.9987047,0.00045232085,0.000076929355,0.0003465478,0.00027992562,0.00013961921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013956993,0.00020807504,0.00024456094,0.0005562001,0.0002888073,0.0005671536,0.00021031342,0.00018565088,0.00026521742],"category_scores_gemma":[0.000100594014,0.00015886094,0.00013961183,0.0009514052,0.0000767726,0.0016947673,0.000003972385,0.0006140157,0.00007800959],"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":[0.00037005177,0.0028967771,0.00024347779,0.00008162443,0.00019242229,0.00005819837,0.13715401,0.021091359,0.005532207,0.03815022,0.0013481536,0.7928815],"study_design_scores_gemma":[0.00044553215,0.000370413,0.0009154053,0.00034521683,0.00011713625,0.000055293083,0.37443373,0.57934296,0.022256065,0.013718708,0.0073441705,0.00065535173],"about_ca_topic_score_codex":0.0008840881,"about_ca_topic_score_gemma":0.0007583092,"teacher_disagreement_score":0.79222614,"about_ca_system_score_codex":0.00024523988,"about_ca_system_score_gemma":0.00024794164,"threshold_uncertainty_score":0.6478161},"labels":[{"model":"gemma","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4404563006","doi":"10.1109/tiv.2024.3502532","title":"Retraction Notice: From Formula One to Autonomous One: History, Achievements, and Future Perspectives","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Pharmaceutical studies and practices","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Expression (computer science); Computer science","score_opus":0.06090062163939386,"score_gpt":0.33231424968986706,"score_spread":0.2714136280504732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404563006","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":[0.5562048,0.059379756,0.2751167,0.08626441,0.010354662,0.0023741222,0.0002924634,0.0011371592,0.008875892],"genre_scores_gemma":[0.97910637,0.015732136,0.0014746008,0.0012135633,0.0008851821,0.00005794681,0.0000059107315,0.000038880466,0.0014853874],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985888,0.00005052015,0.00028081375,0.0004828426,0.0003423236,0.00025472234],"domain_scores_gemma":[0.99915844,0.00027578906,0.000039587012,0.00019760171,0.000075962,0.00025263237],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018327565,0.00021569685,0.00027027974,0.00017616022,0.0001917705,0.00006724633,0.00006966653,0.00016548751,0.0015952927],"category_scores_gemma":[0.000011181526,0.00019792143,0.00012618267,0.0001722104,0.000073675976,0.00024940094,0.00000334003,0.00080488605,0.0002748874],"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":[0.002275803,0.001805072,0.000047921934,0.00025172072,0.0017982441,0.00005369098,0.011074237,0.00012469434,0.029150348,0.0009695503,0.0037805228,0.9486682],"study_design_scores_gemma":[0.00058263924,0.001075161,0.0020043235,0.00039916614,0.0010241516,0.000023376071,0.0051662857,0.002404704,0.07149872,0.0002604915,0.9151555,0.00040552934],"about_ca_topic_score_codex":0.00032918341,"about_ca_topic_score_gemma":0.00008893702,"teacher_disagreement_score":0.9482627,"about_ca_system_score_codex":0.0006769056,"about_ca_system_score_gemma":0.00006410363,"threshold_uncertainty_score":0.9993174},"labels":[],"label_agreement":null},{"id":"W4404795724","doi":"10.1109/tiv.2024.3509315","title":"Improving Takeover Requests in Automated Vehicles: The Role of Dynamic Alerts and Cognitive State","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Transportation and Mobility Innovations","field":"Engineering","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":"State (computer science); Cognition; Computer science; Computer security; Business; Internet privacy; Psychology; Neuroscience","score_opus":0.007762492682576885,"score_gpt":0.24119095997968915,"score_spread":0.23342846729711225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404795724","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":[0.86392295,0.0006528846,0.133981,0.000056271914,0.00024199691,0.0002994441,0.00016105513,0.0005762615,0.00010814474],"genre_scores_gemma":[0.9994054,0.00031947962,0.00009180758,0.000027477046,0.0000049881837,0.00006322733,0.000007918962,0.000029184072,0.00005050866],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991451,0.000025506304,0.00035986406,0.0001796631,0.00012776119,0.00016209015],"domain_scores_gemma":[0.9995778,0.00019465171,0.000021523689,0.000117225696,0.000053262105,0.000035492027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013616665,0.00014210027,0.00012556066,0.00024125785,0.000054754186,0.000042112802,0.000067075125,0.00006328384,0.000030537933],"category_scores_gemma":[0.000003477081,0.00012180048,0.00005244864,0.00042013972,0.000097673335,0.00015446653,5.241278e-7,0.00027074758,0.000015754156],"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":[0.00007016774,0.00017477406,0.00028665026,0.000343144,0.00025316278,0.000013478757,0.0084610665,0.09105499,0.07124735,0.00044401863,0.000019548708,0.82763165],"study_design_scores_gemma":[0.00019389142,0.00006688351,0.006907458,0.00037270103,0.000069007074,0.0000067283477,0.0017930786,0.74919075,0.24040008,0.00053698313,0.0002530993,0.00020933773],"about_ca_topic_score_codex":0.00016095428,"about_ca_topic_score_gemma":0.0009290868,"teacher_disagreement_score":0.8274223,"about_ca_system_score_codex":0.00005508355,"about_ca_system_score_gemma":0.000031575466,"threshold_uncertainty_score":0.49668798},"labels":[],"label_agreement":null},{"id":"W4411270382","doi":"10.1109/tiv.2025.3578935","title":"Lateral Control for Autonomous Vehicles: A Robust Bounded Back-Stepping Technique","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Vehicle Dynamics and Control Systems","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":"McGill University","funders":"","keywords":"Bounded function; Control theory (sociology); Control (management); Computer science; Mathematics; Artificial intelligence; Mathematical analysis","score_opus":0.012348101216902824,"score_gpt":0.22526030242478848,"score_spread":0.21291220120788565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411270382","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":[0.023663372,0.00020675406,0.9711241,0.00023040225,0.0012028161,0.001645968,0.00015002457,0.00063595164,0.0011406181],"genre_scores_gemma":[0.9953005,0.00007659936,0.0013776178,0.00023529222,0.000076319004,0.0012120141,0.000006473869,0.00007465364,0.0016405353],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982329,0.000055107223,0.0006381242,0.0003864942,0.00016201826,0.0005253415],"domain_scores_gemma":[0.9990893,0.00023215149,0.000057119483,0.0003961016,0.00011550755,0.00010979806],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028386738,0.00036698638,0.0004528965,0.0003573125,0.0002711823,0.00017123311,0.00030923684,0.00026794683,0.000051078372],"category_scores_gemma":[0.000004000033,0.00038377443,0.00034182065,0.00028726662,0.0000649253,0.0001420315,0.0000015766884,0.00036961745,0.00007120309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033200026,0.00024328244,0.000063956795,0.0004899881,0.00076612947,0.000006700459,0.0002494163,0.88633215,0.050289307,0.002271132,0.0005679466,0.058387976],"study_design_scores_gemma":[0.001382962,0.00016557693,0.000046116293,0.00034151008,0.0001215186,0.000010957117,0.00011266019,0.92584616,0.06311179,0.00083968515,0.007547574,0.0004735074],"about_ca_topic_score_codex":0.00007875828,"about_ca_topic_score_gemma":0.00012649345,"teacher_disagreement_score":0.9716371,"about_ca_system_score_codex":0.00034473222,"about_ca_system_score_gemma":0.00006414066,"threshold_uncertainty_score":0.9998614},"labels":[],"label_agreement":null}]}