{"meta":{"query_hash":"fd48f6d97481","filters":{"topic":"Autonomous Vehicle Technology and Safety"},"cohort_total":721,"direct_labels_cover":0,"predictions_cover":721,"exported":721,"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/fd48f6d97481","api":"https://metacan.xera.ac/api/v1/cohort?topic=Autonomous+Vehicle+Technology+and+Safety"},"results":[{"id":"W1183861445","doi":"10.1016/j.comcom.2015.08.010","title":"A real-time lane marking localization, tracking and communication system","year":2015,"lang":"en","type":"article","venue":"Computer Communications","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Hough transform; Computer vision; Artificial intelligence; Line (geometry); Kalman filter; Tracking (education); Filter (signal processing); Probabilistic logic; Tracking system; Pattern recognition (psychology); Image (mathematics)","score_opus":0.021138602947175022,"score_gpt":0.22406486004019147,"score_spread":0.20292625709301645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1183861445","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":[0.0500091,0.0056032315,0.8826586,0.0016859996,0.00022704243,0.000627999,0.000017677356,0.00702912,0.052141223],"genre_scores_gemma":[0.938227,0.0005552734,0.06102182,0.000026890932,0.000022486398,0.000023418317,0.00007434493,0.000024322262,0.000024468758],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993373,0.000114702394,0.00024571025,0.000104448474,0.000065142856,0.00013265522],"domain_scores_gemma":[0.9982082,0.00011904115,0.000047558475,0.0014680233,0.000089102505,0.00006804435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031119038,0.00010823582,0.00015176124,0.000086598884,0.00024624798,0.000047389385,0.000663109,0.000109638524,0.0000026455075],"category_scores_gemma":[0.0000093158815,0.0001222455,0.000019716186,0.00018637092,0.0001334124,0.00014818998,0.00036362698,0.0001982052,0.00005397664],"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":[0.00003092127,0.00031786048,0.023966646,0.0004900713,0.0005650437,0.0000145456515,0.015994191,0.094773315,0.001215758,0.5002179,0.027780462,0.33463326],"study_design_scores_gemma":[0.00024455294,0.000013044453,0.0023262058,0.00008423349,0.000017601766,0.000041046496,0.00015134703,0.986188,0.00003747656,0.00080281997,0.009941156,0.00015252901],"about_ca_topic_score_codex":0.000034384728,"about_ca_topic_score_gemma":0.000018743074,"teacher_disagreement_score":0.8914147,"about_ca_system_score_codex":0.000110250854,"about_ca_system_score_gemma":0.00001912355,"threshold_uncertainty_score":0.49850267},"labels":[],"label_agreement":null},{"id":"W1185608482","doi":"","title":"Distance perception in a driving simulator","year":2004,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Driving simulator; Simulation; Perception; Computer science; Psychology; Neuroscience","score_opus":0.016655501562549337,"score_gpt":0.287625867777049,"score_spread":0.27097036621449966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1185608482","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":[0.9974608,0.000039344668,0.00035036638,0.00017671305,0.0001648926,0.00014944666,0.0000011571383,0.00027281724,0.0013844967],"genre_scores_gemma":[0.99833524,0.0000035367348,0.0015333388,0.000050665494,0.000020337571,0.000024180346,0.0000011592766,0.000014174243,0.000017357755],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9986992,0.000028199034,0.00023731458,0.00038013604,0.00016397458,0.0004911807],"domain_scores_gemma":[0.99957156,0.00004583665,0.000037730093,0.00018166898,0.000038603037,0.00012462193],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00038662367,0.00016903308,0.00018948763,0.00021862096,0.00018915567,0.000018477467,0.0003770994,0.00015635544,0.000038776256],"category_scores_gemma":[0.00016703356,0.00017296903,0.000027536375,0.0010369424,0.0028515356,0.00042724656,0.00008629008,0.00037029057,0.00009411743],"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":[0.0000047440176,0.000055319768,0.09499021,0.000010803982,0.0000056190506,0.00016223306,0.0016268304,0.06592436,0.8339665,0.0025081313,0.0000020274874,0.0007432151],"study_design_scores_gemma":[0.00047691536,0.0001786738,0.81000483,0.0000760073,0.0000039878423,0.00014430846,0.0004048822,0.033250365,0.119219385,0.035834603,0.000025234307,0.00038083142],"about_ca_topic_score_codex":0.00003780104,"about_ca_topic_score_gemma":0.000019994957,"teacher_disagreement_score":0.7150146,"about_ca_system_score_codex":0.00048641404,"about_ca_system_score_gemma":0.00013844736,"threshold_uncertainty_score":0.99986213},"labels":[],"label_agreement":null},{"id":"W1529909900","doi":"10.20381/ruor-2776","title":"A Lane Detection, Tracking and Recognition System for Smart Vehicles","year":2015,"lang":"en","type":"dissertation","venue":"uO Research (University of Ottawa)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Tracking (education); Tracking system; Artificial intelligence; Computer vision; Real-time computing; Kalman filter; Psychology","score_opus":0.044001224475518616,"score_gpt":0.27090827362904374,"score_spread":0.2269070491535251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1529909900","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":[0.9845562,0.00025018127,0.0009074075,0.000040373972,0.0001812305,0.0005211663,0.00011468377,0.0004001911,0.013028597],"genre_scores_gemma":[0.99735725,0.00011963696,0.000534391,4.6317206e-7,0.00003170119,0.000003627941,0.00027927588,0.000027061007,0.0016466138],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9991896,0.000052189567,0.00011141474,0.00019991666,0.00020799594,0.00023884153],"domain_scores_gemma":[0.99920607,0.00010996449,0.000052377036,0.00014505567,0.0004154056,0.00007111257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071398186,0.000111504625,0.00022977537,0.00046392344,0.0002560204,0.000012710794,0.0001809983,0.0004568744,0.000011046234],"category_scores_gemma":[0.000056659217,0.00014946699,0.00005239705,0.00021691514,0.0000890813,0.00013044463,0.000021133756,0.00046096763,0.000020329191],"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":[0.00015592691,0.00006315285,0.0012703083,0.006774813,0.00044284164,0.00006303381,0.0033232134,0.00008254534,0.016443266,0.001907691,0.0033396317,0.9661336],"study_design_scores_gemma":[0.014665289,0.0035706388,0.16486582,0.005313599,0.001193002,0.00022428505,0.26288408,0.09492607,0.20248431,0.037895247,0.20741415,0.004563501],"about_ca_topic_score_codex":0.00008244494,"about_ca_topic_score_gemma":0.002638966,"teacher_disagreement_score":0.9615701,"about_ca_system_score_codex":0.00018420021,"about_ca_system_score_gemma":0.000057672776,"threshold_uncertainty_score":0.6095087},"labels":[],"label_agreement":null},{"id":"W1577567706","doi":"10.1109/wowmom.2015.7158210","title":"Opportunistic calibration of smartphone orientation in a vehicle","year":2015,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Intelligent Mechatronic Systems (Canada)","funders":"","keywords":"Telematics; Accelerometer; Global Positioning System; Orientation (vector space); Computer science; Gyroscope; Calibration; Real-time computing; Process (computing); Sensor fusion; Probabilistic logic; Embedded system; Artificial intelligence; Engineering; Telecommunications","score_opus":0.022340241974998693,"score_gpt":0.2236561902118403,"score_spread":0.2013159482368416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1577567706","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":[0.9616504,0.00002578221,0.030036233,0.000079055666,0.00008505289,0.00006263552,0.000002072349,0.00022803123,0.007830755],"genre_scores_gemma":[0.99931395,0.000004341612,0.0005512018,0.000013065688,0.000005585998,0.000005077415,0.000009819624,0.0000054336247,0.00009154493],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996986,0.000007021069,0.00013746483,0.000047600544,0.00004299427,0.00006635442],"domain_scores_gemma":[0.99986154,0.000010075004,0.000012566846,0.00007724333,0.000013287397,0.000025318028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000091182585,0.000037209986,0.000066920176,0.000056959718,0.0000051246197,0.0000016402757,0.000036682795,0.000057887526,0.00002792644],"category_scores_gemma":[0.000015101573,0.00003776807,0.000007707079,0.00012788078,0.000019856954,0.000080925565,0.000007463546,0.00005021002,0.000008378987],"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":[0.0002024564,0.0004351691,0.29813218,0.00024954134,0.00009586618,0.00008592085,0.0073248553,0.15503155,0.11773873,0.22916602,0.007364899,0.18417282],"study_design_scores_gemma":[0.0010929162,0.0000877169,0.022745932,0.0000123440805,0.0000068617346,0.000004453287,0.0006637519,0.9224726,0.04676995,0.0055031227,0.0004735343,0.00016681061],"about_ca_topic_score_codex":0.000031477146,"about_ca_topic_score_gemma":0.000065357206,"teacher_disagreement_score":0.76744103,"about_ca_system_score_codex":0.00003232449,"about_ca_system_score_gemma":0.000020647072,"threshold_uncertainty_score":0.15401372},"labels":[],"label_agreement":null},{"id":"W1602479445","doi":"10.1002/atr.184","title":"An innovative method for the analysis of vehicle movements in roundabouts based on image processing","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministero dell'Università e della Ricerca; Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Roundabout; Software; Computer science; Image processing; Computer vision; Field (mathematics); Data processing; Feature (linguistics); Artificial intelligence; Simulation; Image (mathematics); Engineering; Transport engineering; Mathematics","score_opus":0.012949973891783574,"score_gpt":0.28403193548880135,"score_spread":0.27108196159701775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1602479445","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":[0.54616916,0.000025270092,0.45364583,0.000014996859,0.000025178637,0.00007695298,0.000008594286,0.000012815374,0.000021206777],"genre_scores_gemma":[0.92062867,0.0000079500405,0.07930403,0.000027663915,0.0000054509396,0.000007651179,0.000008587383,0.000009121396,8.8218576e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9993215,0.000013676663,0.00042085678,0.00006335337,0.00009370218,0.000086901586],"domain_scores_gemma":[0.9994292,0.00006136883,0.00022623164,0.0000823708,0.00018681979,0.000013991626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033558038,0.0000708731,0.00019667124,0.0003141717,0.000029322757,0.0000030763329,0.000105199186,0.00004876727,0.0000072352914],"category_scores_gemma":[0.000010616673,0.000054622076,0.00006139165,0.0008016069,0.00002321676,0.00029905682,3.4105472e-7,0.00014315562,5.870079e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031894835,0.000096783086,0.0060588783,0.00003161721,0.0001657007,0.0000032205744,0.004507583,0.88973475,0.027726311,0.00014942224,2.7888743e-7,0.0712065],"study_design_scores_gemma":[0.0007733029,0.00017074613,0.6892962,0.000030707408,0.00012211951,1.2019453e-7,0.0007827214,0.2690564,0.03920903,0.00048446876,0.000013541076,0.000060635142],"about_ca_topic_score_codex":0.0000056585236,"about_ca_topic_score_gemma":0.0000772002,"teacher_disagreement_score":0.6832373,"about_ca_system_score_codex":0.0000364946,"about_ca_system_score_gemma":0.000024683479,"threshold_uncertainty_score":0.22274236},"labels":[],"label_agreement":null},{"id":"W1646124233","doi":"10.1109/ntms.2015.7266486","title":"Distance measurement system for smart vehicles","year":2015,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Advanced driver assistance systems; Computer science; Automotive industry; Distance measurement; Lidar; Ranging; Computer vision; Intelligent transportation system; Real-time computing; Object detection; Measure (data warehouse); Position (finance); Artificial intelligence; Engineering; Telecommunications; Pattern recognition (psychology); Data mining; Remote sensing; Transport engineering; Aerospace engineering","score_opus":0.032767523628985454,"score_gpt":0.20614672198364106,"score_spread":0.1733791983546556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1646124233","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":[0.049367175,0.0013010211,0.8810045,0.00038540462,0.0007713092,0.0004964496,0.00001017383,0.004714371,0.06194956],"genre_scores_gemma":[0.9974898,0.000002194897,0.0021993697,0.000014254962,0.000028435248,0.000057215144,0.0000014113017,0.000014916833,0.0001924208],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99953353,0.000004708181,0.00012082517,0.000086926964,0.000097277996,0.00015673703],"domain_scores_gemma":[0.9997149,0.000009504087,0.000010075922,0.00015441496,0.000060393126,0.000050679853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002726635,0.00007517207,0.00010071231,0.000021947879,0.00003279271,0.0000066552643,0.00009776075,0.000073483425,0.0000023785046],"category_scores_gemma":[0.000014052712,0.000067341105,0.000027432747,0.000046535137,0.00001847997,0.000042726922,0.000010341677,0.00005281575,0.000052212596],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019910846,0.00013206799,0.01052201,0.0013529148,0.00051281205,0.00002295347,0.0009121697,0.024822723,0.012251719,0.71673465,0.11278761,0.119749285],"study_design_scores_gemma":[0.0031153427,0.00020822271,0.0029393896,0.00014273178,0.00007181159,0.00002815976,0.0020324911,0.347617,0.13144402,0.0057110037,0.50570786,0.0009819799],"about_ca_topic_score_codex":0.0000053652743,"about_ca_topic_score_gemma":0.00003304793,"teacher_disagreement_score":0.9481226,"about_ca_system_score_codex":0.00020425316,"about_ca_system_score_gemma":0.000016988073,"threshold_uncertainty_score":0.27460906},"labels":[],"label_agreement":null},{"id":"W1647522455","doi":"","title":"Technology-Independent Algorithm for Collision Warning System at Semi-Controlled Intersections","year":2010,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Toronto Metropolitan University","funders":"","keywords":"Collision; Intersection (aeronautics); Lane departure warning system; Acceleration; Warning system; Advanced driver assistance systems; Computer science; Active safety; Simulation; Engineering; Algorithm; Automotive engineering; Transport engineering; Computer security; Artificial intelligence; Telecommunications","score_opus":0.0035403012018857013,"score_gpt":0.1967435598453517,"score_spread":0.193203258643466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1647522455","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":[0.44567317,0.00008737476,0.53575844,0.00022823495,0.0016513051,0.0009695516,0.00001823224,0.0061150705,0.009498623],"genre_scores_gemma":[0.9828998,0.0000062118424,0.014164558,0.000010134122,0.00006983793,0.00036349823,0.0000073730407,0.00003568087,0.0024429297],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990981,0.0000067239316,0.000296039,0.00022103201,0.00007256625,0.00030553548],"domain_scores_gemma":[0.9994629,0.000079693564,0.000045014454,0.00030658706,0.000056573215,0.00004924982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022570233,0.00017095482,0.00030807185,0.00027118818,0.00027972343,0.000013210846,0.00020931376,0.0005590359,0.00005833312],"category_scores_gemma":[0.000031374213,0.00015138635,0.00011493343,0.00019162016,0.00005868543,0.00006816836,0.00007804799,0.00050347357,0.000098071665],"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":[0.00037863705,0.00024954954,0.0036969383,0.00035330487,0.0015384611,0.00007481726,0.000722931,0.021344136,0.36608985,0.0793919,0.007574621,0.51858485],"study_design_scores_gemma":[0.0031718714,0.00006797607,0.00009420791,0.000023882656,0.000048579946,0.00015222353,0.0006925902,0.9243055,0.06038053,0.00021327684,0.010588179,0.00026117233],"about_ca_topic_score_codex":0.000007911383,"about_ca_topic_score_gemma":0.00018565137,"teacher_disagreement_score":0.9029614,"about_ca_system_score_codex":0.0001897902,"about_ca_system_score_gemma":0.000013845559,"threshold_uncertainty_score":0.6173356},"labels":[],"label_agreement":null},{"id":"W1671591471","doi":"10.1109/ntms.2015.7266483","title":"Design of lane keeping assist system for autonomous vehicles","year":2015,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Intelligent transportation system; Systems engineering; Lane departure warning system; Tracking (education); Systems design; Engineering; Artificial intelligence; Software engineering; Transport engineering","score_opus":0.03244492774959163,"score_gpt":0.22276178080048584,"score_spread":0.1903168530508942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1671591471","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":[0.030466545,0.000303244,0.958214,0.000063022766,0.00021225553,0.00029485417,0.000007404859,0.0016793752,0.008759309],"genre_scores_gemma":[0.97098786,0.000002253917,0.028773455,0.000011068361,0.000026259635,0.00003406736,0.0000034041861,0.000022020067,0.0001396417],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994197,0.0000143216075,0.00022567573,0.000102179656,0.000055592922,0.00018255957],"domain_scores_gemma":[0.9996139,0.00007719148,0.000033076372,0.00018256756,0.00004394538,0.000049331822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031097763,0.000097102326,0.00020485962,0.0000636394,0.000031283693,0.000005683598,0.0001432354,0.00014373656,0.000004061564],"category_scores_gemma":[0.000018858485,0.00009271601,0.00003502071,0.00007523486,0.0000355752,0.00005272614,0.000018659166,0.00007484503,0.000024270626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023306844,0.0001415061,0.0033565185,0.0016347661,0.0006397142,0.000032898308,0.0013308453,0.65506965,0.035883736,0.18846284,0.008407323,0.104807116],"study_design_scores_gemma":[0.0012321195,0.00019492386,0.0006714456,0.00006663482,0.000049306418,0.00003464747,0.0007040877,0.8745487,0.117486395,0.0015914354,0.0030539914,0.00036636888],"about_ca_topic_score_codex":0.000011270508,"about_ca_topic_score_gemma":0.0000028922686,"teacher_disagreement_score":0.9405213,"about_ca_system_score_codex":0.000103547085,"about_ca_system_score_gemma":0.000035550416,"threshold_uncertainty_score":0.3780849},"labels":[],"label_agreement":null},{"id":"W1724623617","doi":"10.3141/2019-08","title":"Automated Road Safety Analysis Using Video Data","year":2007,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science","score_opus":0.013702720065490764,"score_gpt":0.24763348383936803,"score_spread":0.23393076377387728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1724623617","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":[0.23690295,0.0010081516,0.74990505,0.00022847106,0.000116061696,0.0003327556,0.00017724892,0.010374299,0.0009550167],"genre_scores_gemma":[0.9102473,0.00014948288,0.088752575,0.00030381628,0.000082442115,0.000021543561,0.00025508314,0.00009357606,0.000094160954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971433,0.00005907034,0.0008279987,0.0005982554,0.0002899571,0.0010813976],"domain_scores_gemma":[0.9970345,0.00010861313,0.00014585539,0.0023631342,0.000068961315,0.00027892835],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016858815,0.00043411765,0.00061372755,0.0011273073,0.0003114408,0.00007270132,0.0012915877,0.00068445166,0.00008724491],"category_scores_gemma":[0.00011507011,0.000476802,0.00019857551,0.0023892703,0.00013936558,0.0005064736,0.0004525991,0.00069544883,0.000032442436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027255324,0.00034902152,0.11253846,0.00011510676,0.0038062164,0.00057739974,0.00048576976,0.65019214,0.04156201,0.009792839,0.0041106795,0.17619781],"study_design_scores_gemma":[0.00027777615,0.000019651583,0.0944457,0.00001543651,0.00035185873,0.00007077182,0.00006178771,0.89639866,0.0052288696,0.0001706116,0.0024908492,0.00046803209],"about_ca_topic_score_codex":0.0033508257,"about_ca_topic_score_gemma":0.0056670606,"teacher_disagreement_score":0.6733444,"about_ca_system_score_codex":0.0007233526,"about_ca_system_score_gemma":0.00010341364,"threshold_uncertainty_score":0.9997684},"labels":[],"label_agreement":null},{"id":"W1895572989","doi":"10.1109/cig.2015.7317925","title":"Automatic mapping of human behavior data to personality model parameters for traffic simulations in virtual environments","year":2015,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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; University of New Brunswick","funders":"Bundesministerium für Bildung und Forschung","keywords":"Computer science; Baseline (sea); Human behavior; Implementation; Artificial intelligence; Virtual reality; Data modeling; Personality; Virtual actor; Simulation; Machine learning; Database","score_opus":0.12679552678596306,"score_gpt":0.30542397926834325,"score_spread":0.17862845248238018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1895572989","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":[0.9245688,0.000005778784,0.074741155,0.000032479686,0.000018333336,0.00034079654,0.00009686057,0.00014787303,0.00004797748],"genre_scores_gemma":[0.9839989,2.9485778e-7,0.015803957,0.000014306282,0.0000028145803,0.000037308488,0.00007112651,0.000013252976,0.00005803842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937165,0.000008657221,0.00024518676,0.00015272752,0.00007334537,0.00014841124],"domain_scores_gemma":[0.99952626,0.000034812103,0.000019753179,0.00036503957,0.0000045155225,0.000049620125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017906664,0.00008513208,0.00015057871,0.00009397134,0.000026018977,0.000003523686,0.00024470798,0.00008718008,0.000013166011],"category_scores_gemma":[0.000021833306,0.00009255451,0.000020205509,0.00007727884,0.000032931574,0.000110367015,0.00006518467,0.000075567536,0.0000050259805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020230448,0.000061169376,0.000936021,0.000010378668,0.00001207828,3.679296e-7,0.0006800952,0.9905656,0.0022131456,0.00027878617,0.00006565366,0.0051746885],"study_design_scores_gemma":[0.0003494158,0.000036026224,0.004231783,0.0000070715087,0.000013931702,3.3578e-7,0.00027942943,0.9945238,0.00032875128,0.000098038356,0.000031285803,0.00010010717],"about_ca_topic_score_codex":0.000010275899,"about_ca_topic_score_gemma":0.000075034965,"teacher_disagreement_score":0.05943016,"about_ca_system_score_codex":0.00008500204,"about_ca_system_score_gemma":0.000012976649,"threshold_uncertainty_score":0.37742636},"labels":[],"label_agreement":null},{"id":"W1938642296","doi":"","title":"Map-based lane and obstacle-free area detection","year":2014,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Western University","funders":"","keywords":"Obstacle; Computer science; Computer vision; Advanced driver assistance systems; Artificial intelligence; Geography","score_opus":0.0038283073519429947,"score_gpt":0.15188939748471803,"score_spread":0.14806109013277502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1938642296","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":[0.61003983,0.00010913817,0.37089035,0.00042152818,0.00016759417,0.000082157385,0.0000027944252,0.0023068888,0.015979711],"genre_scores_gemma":[0.99820805,0.0000033959966,0.00153125,0.000064316,0.000016194741,0.000004958208,0.0000016753324,0.000010067977,0.00016007008],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971443,0.0000058631504,0.00006660436,0.00007779348,0.000029088482,0.00010618837],"domain_scores_gemma":[0.999739,0.000032421005,0.000006580579,0.00019107884,0.000005843158,0.000025082021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006329712,0.000063912834,0.00006836534,0.00003946739,0.00003701393,0.0000057994957,0.00006554197,0.00009952787,0.00006538382],"category_scores_gemma":[0.000013386477,0.000059428225,0.000012304734,0.00003243945,0.000026778096,0.000030075214,0.000014751505,0.00009777888,0.0000478199],"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":[0.000045871813,0.00005149514,0.013515563,0.00021704673,0.00008665044,0.0000111528425,0.0001187003,0.015446341,0.041459177,0.02175293,0.0038255036,0.90346956],"study_design_scores_gemma":[0.0010607869,0.00010879718,0.024366278,0.000010240284,0.000016660384,0.00001508092,0.000027529997,0.82484704,0.10366867,0.007959128,0.037592467,0.0003273133],"about_ca_topic_score_codex":0.00000948151,"about_ca_topic_score_gemma":0.000105755134,"teacher_disagreement_score":0.9031423,"about_ca_system_score_codex":0.000014209942,"about_ca_system_score_gemma":0.0000018483712,"threshold_uncertainty_score":0.24234127},"labels":[],"label_agreement":null},{"id":"W1939600401","doi":"10.5539/mas.v9n12p118","title":"A Survey on Vehicle Detection based on Vision","year":2015,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Span (engineering); Shadow (psychology); Monocular vision; Computer science; Computer vision; Artificial intelligence; Class (philosophy); Monocular; Engineering; Psychology","score_opus":0.019542107635719192,"score_gpt":0.2330293344258092,"score_spread":0.21348722679009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1939600401","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":[0.6577709,0.000007680192,0.32836094,0.00004710552,0.00016696808,0.00014553437,0.0000039099273,0.00077345694,0.012723545],"genre_scores_gemma":[0.9995466,4.2253785e-7,0.00027017147,0.00012493086,0.000012209202,0.000014551033,0.0000017107092,0.000013995903,0.000015401607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990302,0.00001251292,0.00010547367,0.00027833378,0.00030871975,0.00026478939],"domain_scores_gemma":[0.99945974,0.00004482652,0.000017929866,0.0003455048,0.00003090821,0.00010106038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008593742,0.00010938884,0.000094641895,0.0001573458,0.00019211283,0.000038885126,0.00028224563,0.0000928871,0.000003997863],"category_scores_gemma":[0.00003786812,0.00010277477,0.000014433058,0.00046447615,0.0002285708,0.00007071351,0.000029661052,0.00021313097,0.00017065536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118865595,0.000074044445,0.00039569326,0.0000045496977,0.0000024139285,0.0000025304782,0.00017365185,0.57724845,0.25649112,0.0008219826,0.00011034395,0.16455634],"study_design_scores_gemma":[0.00025798348,0.000092190996,0.019133361,0.0000037770062,9.2884267e-7,2.637758e-7,0.0000056456,0.9016896,0.07770939,0.0009117998,0.00008271973,0.00011235217],"about_ca_topic_score_codex":0.0000134177535,"about_ca_topic_score_gemma":0.000044526707,"teacher_disagreement_score":0.34177575,"about_ca_system_score_codex":0.00019086926,"about_ca_system_score_gemma":0.000060942322,"threshold_uncertainty_score":0.41910335},"labels":[],"label_agreement":null},{"id":"W1969726398","doi":"10.2118/86846-ms","title":"Risk Management and Analysis of Driving Hazard Using Bow Tie Model","year":2004,"lang":"en","type":"article","venue":"SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production","topic":"Autonomous Vehicle Technology and Safety","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":"Shell (Canada)","funders":"","keywords":"Bow tie; Hazard; Computer science; Process (computing); Hazard analysis; Risk analysis (engineering); Risk management; Reliability engineering; Engineering; Business; Telecommunications","score_opus":0.031151667019623942,"score_gpt":0.2528912544204033,"score_spread":0.22173958740077934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969726398","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":[0.8349675,0.0013103003,0.08747901,0.07350684,0.00029637877,0.0002443717,0.000045022472,0.00009933039,0.0020512377],"genre_scores_gemma":[0.6234351,0.37429324,0.0021334342,0.000029619241,0.000015136599,0.0000061695405,0.000025003561,0.000005155835,0.00005717722],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992919,0.000020528612,0.00026068816,0.00022993714,0.000099944526,0.000096975185],"domain_scores_gemma":[0.999788,0.0000080102545,0.00007509203,0.00008014003,0.000009202777,0.000039541592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000214466,0.000103294224,0.00015887046,0.00023805401,0.00009767872,0.000014810417,0.000029213357,0.000055550525,0.000008446973],"category_scores_gemma":[0.000004812747,0.00010580446,0.000013610571,0.00007406389,0.000081173195,0.00018692407,0.000027065393,0.00011609079,5.9404584e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044709548,0.000031354084,0.0015678642,0.00005000715,0.000096261385,6.036279e-7,0.000855517,0.32695615,0.00011032118,0.012367814,0.0000018510415,0.65791756],"study_design_scores_gemma":[0.0005920348,0.0000935182,0.011463624,0.00009733282,0.00008462882,0.0000052730607,0.0006708184,0.7996117,0.00017394144,0.18668805,0.00033509423,0.00018399202],"about_ca_topic_score_codex":0.00004205595,"about_ca_topic_score_gemma":0.00012478726,"teacher_disagreement_score":0.65773356,"about_ca_system_score_codex":0.00007947822,"about_ca_system_score_gemma":0.000008158119,"threshold_uncertainty_score":0.43145806},"labels":[],"label_agreement":null},{"id":"W1970505940","doi":"10.4271/2011-01-0432","title":"The State of the Art of Driver Model Development","year":2011,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Autonomous Vehicle Technology and Safety","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 Windsor","funders":"","keywords":"State (computer science); Computer science; Programming language","score_opus":0.011774597467570832,"score_gpt":0.2001152518774141,"score_spread":0.18834065440984327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970505940","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":[0.91408736,0.0004506043,0.00002619779,0.00090434577,0.0002388102,0.0009743328,0.000043102427,0.002385279,0.08088997],"genre_scores_gemma":[0.9940331,0.000368259,0.0044894516,0.00019432142,0.0000119524975,0.00013937347,0.000003979547,0.00007887254,0.0006807036],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99720937,0.00006413609,0.00110218,0.0004518799,0.0005203869,0.0006520479],"domain_scores_gemma":[0.9979072,0.00021524096,0.00021300818,0.0014552869,0.00009033366,0.00011893694],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057284476,0.00047807765,0.0006121051,0.00009509729,0.00032145338,0.000011989347,0.0015704939,0.00052984734,0.00009823715],"category_scores_gemma":[0.00016695358,0.00030207197,0.00031967694,0.00048663595,0.0018453121,0.00017226137,0.00049946614,0.0010858417,0.000051347255],"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":[0.00015486788,0.00017538184,0.00024243952,0.000049090577,0.00011187605,0.0000051468287,0.00019211345,0.00080803613,0.96338797,0.020374345,0.0014053903,0.0130933765],"study_design_scores_gemma":[0.00032294623,0.00022237602,0.9634809,0.00011196548,0.000046340585,0.000017485607,0.00004971297,0.000005306317,0.008606886,0.009822474,0.016919946,0.00039365428],"about_ca_topic_score_codex":0.0000074203217,"about_ca_topic_score_gemma":0.0176436,"teacher_disagreement_score":0.9632385,"about_ca_system_score_codex":0.00015816274,"about_ca_system_score_gemma":0.000119759774,"threshold_uncertainty_score":0.99994314},"labels":[],"label_agreement":null},{"id":"W1976840195","doi":"10.1109/tits.2012.2210881","title":"Pedestrian Safety Analysis in Mixed Traffic Conditions Using Video Data","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pedestrian; Collision; SAFER; Computer science; Collision avoidance; Pedestrian crossing; Simulation; Transport engineering; Engineering; Computer security","score_opus":0.04719003252579666,"score_gpt":0.2782891438373436,"score_spread":0.23109911131154692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976840195","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":[0.36363086,0.00027979427,0.6334332,0.000014531408,0.0009398762,0.00031062315,0.0009237363,0.0004081614,0.000059181417],"genre_scores_gemma":[0.9987933,0.00019992598,0.0002833076,0.00000906902,0.000043404267,0.00006175783,0.00052390347,0.000042086773,0.000043225526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980399,0.00008102365,0.00090934307,0.0003247625,0.00021805512,0.00042689795],"domain_scores_gemma":[0.9989621,0.00012557935,0.00008033114,0.00065755355,0.000038417023,0.00013598484],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043572186,0.00026721734,0.00043319544,0.0008503905,0.00017139375,0.000024261688,0.00030883885,0.0002855251,0.00018987706],"category_scores_gemma":[0.0000020487187,0.0002983794,0.00016743134,0.0014203965,0.000066512206,0.0005132081,3.5919626e-7,0.00041330888,0.000072027404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021531994,0.00015254742,0.0015625622,0.000059769263,0.0004962017,0.000004331469,0.000687114,0.99439967,0.00033692256,0.00015311682,0.000018570116,0.002107662],"study_design_scores_gemma":[0.0006875491,0.00003539943,0.01844367,0.000110914305,0.0013599107,0.00001507536,0.0023821222,0.9677391,0.005329606,0.000009384403,0.0031858126,0.00070146477],"about_ca_topic_score_codex":0.00021510392,"about_ca_topic_score_gemma":0.0016514058,"teacher_disagreement_score":0.6351625,"about_ca_system_score_codex":0.0002251539,"about_ca_system_score_gemma":0.000034946926,"threshold_uncertainty_score":0.99994683},"labels":[],"label_agreement":null},{"id":"W1977792982","doi":"10.1007/s00500-006-0140-0","title":"Merits and limitations of using fuzzy inference system for temporal integration of INS/GPS in vehicular navigation","year":2006,"lang":"en","type":"article","venue":"Soft Computing","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Threat Reduction Agency","keywords":"Global Positioning System; GPS/INS; Inertial navigation system; Computer science; Kalman filter; GNSS applications; Sensor fusion; Real-time computing; Navigation system; Heading (navigation); Adaptive neuro fuzzy inference system; Dead reckoning; Fuzzy logic; Assisted GPS; Artificial intelligence; Fuzzy control system; Engineering; Orientation (vector space); Telecommunications","score_opus":0.02597155745665254,"score_gpt":0.24371793712614703,"score_spread":0.2177463796694945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977792982","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":[0.7624726,0.000110650784,0.2370883,0.0000063662665,0.000037732425,0.0001145401,0.0000037695256,0.00008190104,0.000084146195],"genre_scores_gemma":[0.9774497,0.0000017124223,0.022501726,8.135365e-7,0.000013720899,0.0000026494329,0.000021190755,0.00000796113,5.2174204e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994707,0.000014266696,0.00030886978,0.000078437995,0.000041073843,0.00008665678],"domain_scores_gemma":[0.99961746,0.00016876029,0.00008837549,0.000062466,0.000055415003,0.000007521902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001643111,0.00006496189,0.00014603836,0.00009203591,0.00003844985,0.000004213624,0.000041753203,0.000086361884,5.043928e-8],"category_scores_gemma":[0.000048641792,0.00007216176,0.000019918969,0.00015893255,0.00003685755,0.000068154244,0.0000147747905,0.00008121428,1.1140853e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011562604,0.00003575351,0.05843151,0.0007939746,0.000024862935,0.0000016737933,0.001207786,0.7156311,0.12053701,0.037043747,0.0000021659303,0.06627881],"study_design_scores_gemma":[0.00022878287,0.000018251852,0.017870005,0.00031353012,0.000009403096,0.0000031415245,0.00030518958,0.9576393,0.022068307,0.0014653336,0.0000056581807,0.00007313245],"about_ca_topic_score_codex":0.00008675456,"about_ca_topic_score_gemma":0.000050457726,"teacher_disagreement_score":0.24200812,"about_ca_system_score_codex":0.00004151885,"about_ca_system_score_gemma":0.000015145254,"threshold_uncertainty_score":0.29426715},"labels":[],"label_agreement":null},{"id":"W1982175918","doi":"10.4271/2013-01-0623","title":"Collision Prevention While Driving in Real Traffic Flow Using Emotional Learning Fuzzy Inference Systems","year":2013,"lang":"en","type":"article","venue":"SAE International Journal of Transportation Safety","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Federal Highway Administration","keywords":"Fuzzy inference system; Collision; Inference; Computer science; Fuzzy inference; Fuzzy logic; Adaptive neuro fuzzy inference system; Artificial intelligence; Fuzzy control system; Computer security","score_opus":0.013613317033595432,"score_gpt":0.25455319408097415,"score_spread":0.24093987704737874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982175918","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":[0.94965464,0.00009484306,0.048690315,0.00010887252,0.00088509277,0.00015248176,0.000014389406,0.00009187607,0.00030748858],"genre_scores_gemma":[0.9957231,0.00037640752,0.0036315124,0.000005832782,0.00012862388,0.000005722421,0.00007447155,0.000020295896,0.000033997938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9982819,0.000058764457,0.0009544181,0.00012474573,0.0004103855,0.0001697943],"domain_scores_gemma":[0.99910456,0.00009565658,0.0002899037,0.000067044915,0.00038517494,0.000057676196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039403318,0.00014480436,0.00023645614,0.00037980214,0.000066269546,0.00004379874,0.00024518714,0.00016527904,0.00011805115],"category_scores_gemma":[0.000027545182,0.000150622,0.00011043946,0.00019379814,0.000034568184,0.00078114925,0.0000054461752,0.00047530534,0.000013873407],"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":[0.000031118936,0.000035396533,0.018784018,0.000016870667,0.00007473013,0.00001978004,0.00047725186,0.9667932,0.001860825,0.00047266518,0.000017829147,0.011416356],"study_design_scores_gemma":[0.0010946207,0.00006839557,0.5802982,0.00050259987,0.000023966835,0.000051603034,0.0005451134,0.41623908,0.00016111278,0.00045588225,0.0003709382,0.0001885145],"about_ca_topic_score_codex":0.00004464856,"about_ca_topic_score_gemma":0.00017082907,"teacher_disagreement_score":0.56151414,"about_ca_system_score_codex":0.0003137522,"about_ca_system_score_gemma":0.000092170834,"threshold_uncertainty_score":0.6142187},"labels":[],"label_agreement":null},{"id":"W1987205927","doi":"10.1002/atr.5670430104","title":"Lane change algorithm for autonomous vehicles via virtual curvature method","year":2009,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Hong Kong Polytechnic University","keywords":"Curvature; Path (computing); Radius of curvature; Controller (irrigation); Computer science; Position (finance); Turning radius; Algorithm; Simulation; RADIUS; Engineering; Mathematics; Mean curvature; Geometry; Aerospace engineering","score_opus":0.009153036181507333,"score_gpt":0.2535962328710799,"score_spread":0.24444319668957257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987205927","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":[0.06784421,0.00089262874,0.9299957,0.00041548055,0.0003986345,0.00020637007,0.000035629117,0.00018559901,0.000025709674],"genre_scores_gemma":[0.69136924,0.0001686297,0.3080473,0.00012111327,0.00021479467,0.000008989166,0.000033990545,0.000020048978,0.000015930502],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992104,0.000010085071,0.00040066184,0.000093910596,0.00010604425,0.00017889177],"domain_scores_gemma":[0.9995639,0.000046629833,0.00015587446,0.00008374117,0.000095825555,0.000054038817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017603043,0.00013660543,0.00025809908,0.00012398459,0.000049545724,0.0000055854252,0.00011678114,0.00017442557,0.000005995001],"category_scores_gemma":[0.0000051772504,0.00013025495,0.00011801764,0.00012636698,0.000014552528,0.0003903176,5.38459e-7,0.0003029161,0.0000014145601],"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":[0.0000526475,0.00003263562,0.000025891897,0.000013010638,0.000044719123,0.000020179701,0.00072927464,0.03777504,0.009567078,0.0006203658,0.00003385468,0.9510853],"study_design_scores_gemma":[0.013910208,0.0069750245,0.47610232,0.00043666983,0.0009185391,0.00042498478,0.0016745196,0.24717732,0.12464752,0.05291201,0.072655596,0.0021652835],"about_ca_topic_score_codex":6.456167e-7,"about_ca_topic_score_gemma":0.0000060515067,"teacher_disagreement_score":0.94892,"about_ca_system_score_codex":0.000051863968,"about_ca_system_score_gemma":0.00001507224,"threshold_uncertainty_score":0.5311642},"labels":[],"label_agreement":null},{"id":"W1990633464","doi":"10.1109/crv.2013.37","title":"Blinded by the Light: Exploiting the Deficiencies of a Laser Rangefinder for Rover Attitude Estimation","year":2013,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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 Toronto","funders":"","keywords":"Lidar; Inclinometer; Traverse; Computer science; Odometry; Orientation (vector space); Remote sensing; Ephemeris; Computer vision; Sky; Artificial intelligence; Geodesy; Satellite; Mobile robot; Geology; Robot; Geography; Engineering; Mathematics; Meteorology; Aerospace engineering","score_opus":0.013622452325451324,"score_gpt":0.21360386755946503,"score_spread":0.19998141523401372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990633464","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":[0.8361355,0.0002432427,0.15162726,0.0039791283,0.00007427464,0.0006160587,0.000005022823,0.00037563685,0.006943893],"genre_scores_gemma":[0.9979368,0.0000046688615,0.0014645733,0.00013472672,0.000010061702,0.0001370917,0.000001893559,0.000011184319,0.00029899727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999552,0.000008079606,0.00015393092,0.00007073025,0.00006624546,0.00014899764],"domain_scores_gemma":[0.99960953,0.00013327731,0.000030107647,0.0001838407,0.000033111035,0.000010105679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016159883,0.000076326854,0.0000837197,0.000020092695,0.00011604875,0.000014906309,0.00017610892,0.00007616084,0.000082853825],"category_scores_gemma":[0.00003856429,0.00003988383,0.000041605406,0.00008415715,0.000060829567,0.00010833745,0.000023039285,0.000093157316,0.00003392057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064523454,0.00029726978,0.003832591,0.000403118,0.0006491539,8.051771e-7,0.011474433,0.28579864,0.12566164,0.137715,0.1854587,0.24864414],"study_design_scores_gemma":[0.00079777016,0.000052474978,0.0043780776,0.0000182679,0.000045585482,0.0000054260536,0.0019657454,0.7570369,0.22312868,0.0065587335,0.0057569467,0.00025538588],"about_ca_topic_score_codex":0.000019226025,"about_ca_topic_score_gemma":0.000014973379,"teacher_disagreement_score":0.4712383,"about_ca_system_score_codex":0.000014972946,"about_ca_system_score_gemma":0.0000064941364,"threshold_uncertainty_score":0.16264154},"labels":[],"label_agreement":null},{"id":"W2000642578","doi":"10.5267/j.ijiec.2012.08.004","title":"Reliability prediction for the vehicles equipped with advanced driver assistance systems (ADAS) and passive safety systems (PSS)","year":2012,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Advanced driver assistance systems; Active safety; Reliability (semiconductor); Automotive engineering; Warning system; Crash; Lane departure warning system; System safety; Vehicle safety; Engineering; Transport engineering; Computer science; Reliability engineering; Telecommunications","score_opus":0.01226791066436055,"score_gpt":0.22370961725035057,"score_spread":0.21144170658599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000642578","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":[0.2907941,0.0010338493,0.70226866,0.00034265936,0.0048326114,0.00041519274,0.000112262154,0.00016424374,0.000036406716],"genre_scores_gemma":[0.9976547,0.00007637862,0.0014097391,0.0000051696547,0.00078171666,0.000031329622,0.000008916769,0.00002120598,0.000010866241],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899477,0.00002122579,0.00048266942,0.00008460671,0.0002376293,0.000179104],"domain_scores_gemma":[0.99871445,0.0005909861,0.00018648355,0.00009650172,0.00034072294,0.00007085857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033278426,0.00013570077,0.00019695789,0.00012269944,0.00009680758,0.000054909233,0.00020793102,0.00013376203,0.0000013837774],"category_scores_gemma":[0.00015790174,0.000100915466,0.000052594034,0.00011313636,0.000051153533,0.0003866864,0.000021831782,0.0003753163,6.8349664e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009354954,0.00001985551,0.0012447891,0.000015521511,0.0002917627,0.0000020928771,0.00011981627,0.9891724,0.00028177773,0.0029644696,0.00020504661,0.0055889026],"study_design_scores_gemma":[0.0020729343,0.00013360607,0.017195448,0.00030754812,0.0001050687,0.00022071558,0.0003434114,0.9647022,0.00019509699,0.000033694974,0.014504424,0.000185854],"about_ca_topic_score_codex":0.000006286111,"about_ca_topic_score_gemma":8.0363435e-7,"teacher_disagreement_score":0.7068606,"about_ca_system_score_codex":0.00024923575,"about_ca_system_score_gemma":0.00004591153,"threshold_uncertainty_score":0.41152135},"labels":[],"label_agreement":null},{"id":"W2001323931","doi":"10.4271/2011-01-0587","title":"A Study of Crash Rates for Vehicles with Advanced Crash Avoidance Features","year":2011,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Crash; Aeronautics; Computer science; Collision avoidance; Computer security; Automotive engineering; Engineering; Operating system","score_opus":0.011994838999518725,"score_gpt":0.23945592158785262,"score_spread":0.2274610825883339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001323931","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":[0.9774143,0.00049890106,0.000007783611,0.00035321125,0.00013904565,0.0021314453,0.000066021785,0.004760758,0.014628514],"genre_scores_gemma":[0.98985976,0.00023336278,0.008160913,0.00024396222,0.000050327133,0.0010567075,0.000016296279,0.00019214935,0.00018653205],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99644876,0.000081322105,0.0010209586,0.0010028878,0.0004922146,0.0009538801],"domain_scores_gemma":[0.9974997,0.0004323138,0.00022880784,0.0014701546,0.00014879026,0.00022020737],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043095095,0.00084407977,0.0011959844,0.00024281343,0.0003354802,0.000029880839,0.0012183728,0.00089955714,0.00010957208],"category_scores_gemma":[0.00026581917,0.0006918598,0.0002894434,0.00070670986,0.0010593764,0.00042219146,0.00024227577,0.0013067706,0.000022333821],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010094431,0.0011379356,0.002455752,0.000142491,0.00022417078,0.000055648805,0.00027504508,0.00022490545,0.97559917,0.008671296,0.000924655,0.009279516],"study_design_scores_gemma":[0.0019142771,0.00518263,0.981597,0.00019516864,0.00014121734,0.000061897554,0.00076396,2.8565043e-7,0.0038850813,0.0026981349,0.0027557244,0.00080464606],"about_ca_topic_score_codex":0.000020685056,"about_ca_topic_score_gemma":0.050227247,"teacher_disagreement_score":0.97914124,"about_ca_system_score_codex":0.0001569383,"about_ca_system_score_gemma":0.00006191139,"threshold_uncertainty_score":0.99955326},"labels":[],"label_agreement":null},{"id":"W2002455209","doi":"10.4271/2015-01-0292","title":"Over-The-Air Testing and Certification for Connected vehicles","year":2015,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Autonomous Vehicle Technology and Safety","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":"Canadian Standards Association","funders":"","keywords":"Certification; Computer science; Aeronautics; Computer security; Automotive engineering; Engineering","score_opus":0.02536276367240515,"score_gpt":0.24024424192100924,"score_spread":0.2148814782486041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002455209","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":[0.9724666,0.0007973215,0.000014277192,0.0053124996,0.00022982938,0.0013747904,0.00006565565,0.007900803,0.011838218],"genre_scores_gemma":[0.9935017,0.000104643645,0.0046924837,0.00078439375,0.00011735104,0.0005371588,0.000029778163,0.00012306865,0.00010942859],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99730605,0.00007334123,0.0007817995,0.0007299375,0.00036571355,0.0007431728],"domain_scores_gemma":[0.9972914,0.0011550393,0.0001346189,0.0010003202,0.00014346551,0.0002751559],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007452281,0.0005540563,0.00060407806,0.00014327042,0.0004316012,0.00006280638,0.00072108605,0.00088574924,0.000028891305],"category_scores_gemma":[0.0018523468,0.00045033058,0.00016698851,0.00063057645,0.0010126733,0.00032170833,0.00022299883,0.0009953992,0.000037998732],"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":[0.00015293085,0.00008944779,0.0005698147,0.00005410946,0.000048321483,0.000008141657,0.000040253704,0.00019320985,0.9629069,0.016627518,0.0035466428,0.015762687],"study_design_scores_gemma":[0.00082502275,0.0006562307,0.9541304,0.00008682087,0.00007358825,0.0000824845,0.00011989212,0.000017720675,0.00054809987,0.0115459375,0.031349804,0.00056399265],"about_ca_topic_score_codex":0.000020822356,"about_ca_topic_score_gemma":0.011236398,"teacher_disagreement_score":0.96235883,"about_ca_system_score_codex":0.00024868356,"about_ca_system_score_gemma":0.000070895076,"threshold_uncertainty_score":0.99979484},"labels":[],"label_agreement":null},{"id":"W2003086425","doi":"10.1007/s10845-011-0618-1","title":"Designing an integrated driver assistance system using image sensors","year":2012,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Advanced driver assistance systems; Computer science; Smart camera; Embedded system; Artificial intelligence; Computer hardware; Real-time computing; Engineering","score_opus":0.017759003310162905,"score_gpt":0.2362456911833723,"score_spread":0.2184866878732094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003086425","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":[0.7002243,0.00034396158,0.29831716,0.0000057712246,0.0005770718,0.000054373457,0.0000014207278,0.00019125447,0.00028471104],"genre_scores_gemma":[0.96395123,0.000044479093,0.03570641,0.0000088000925,0.00022319595,7.897882e-7,8.608834e-7,0.000044311444,0.000019927787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99859744,0.000064335836,0.0006208862,0.00010083048,0.00018690756,0.0004296033],"domain_scores_gemma":[0.99925923,0.00005332732,0.00023652888,0.00019739376,0.00007708772,0.0001764072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005971435,0.00022532135,0.00033573204,0.00026776054,0.000109377346,0.00003989671,0.00026057122,0.0001709237,0.000051237894],"category_scores_gemma":[0.000019473558,0.00019553234,0.0001277935,0.00009299183,0.000051458715,0.0007691866,0.000028759829,0.00063750026,0.000029155506],"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":[0.00022225574,0.0002518087,0.008753995,0.0006522125,0.0010478824,0.00058823026,0.005221557,0.5804829,0.34801948,0.0007755135,0.00032178394,0.053662375],"study_design_scores_gemma":[0.00013523533,0.00003704515,0.0016537887,0.00022705576,0.000063190215,0.0006389195,0.0023208156,0.021942995,0.97144157,0.000018274337,0.0012695093,0.000251615],"about_ca_topic_score_codex":0.000005515761,"about_ca_topic_score_gemma":0.0000013631458,"teacher_disagreement_score":0.6234221,"about_ca_system_score_codex":0.00054809434,"about_ca_system_score_gemma":0.000021349642,"threshold_uncertainty_score":0.79735774},"labels":[],"label_agreement":null},{"id":"W2004010637","doi":"10.1080/00423114.2012.758857","title":"An impaired driver model for safe driving by control of vehicle parameters","year":2013,"lang":"en","type":"article","venue":"Vehicle System Dynamics","topic":"Autonomous Vehicle Technology and Safety","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":"Concordia University","funders":"","keywords":"Engineering; Automotive engineering; Vehicle safety; Control (management); Vehicle dynamics; Aeronautics; Computer science; Artificial intelligence","score_opus":0.0043360057636376605,"score_gpt":0.18464321414714266,"score_spread":0.180307208383505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004010637","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":[0.6132061,0.000057405305,0.38510785,0.000051104,0.000089726156,0.0006089898,0.00012592711,0.0006329947,0.00011992351],"genre_scores_gemma":[0.9957178,0.0000059462864,0.0038294338,0.000027850467,0.000016344527,0.00020530421,0.00004101121,0.00008187287,0.000074431315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846166,0.000042386237,0.0005585912,0.00029421636,0.00014537422,0.0004977669],"domain_scores_gemma":[0.99897224,0.0001133465,0.00012768293,0.0005428596,0.000108203494,0.00013568229],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024094713,0.00026404316,0.0004773567,0.0001046195,0.000117042306,0.00002970815,0.00039336612,0.00035823416,0.0000064555193],"category_scores_gemma":[0.000014420282,0.00027408,0.00013942354,0.00014603253,0.00010242784,0.00033715638,0.0000261706,0.00019575501,0.000026490752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046499514,0.0001324944,0.018884454,0.0005959644,0.00031350026,0.000002505129,0.00049500674,0.85294473,0.09487069,0.00896532,0.00061729515,0.022131547],"study_design_scores_gemma":[0.0010394639,0.00010241733,0.0016185363,0.0000463528,0.000041268704,0.0000036923482,0.00031315995,0.99451536,0.0017178869,0.00029729883,0.000014023386,0.000290562],"about_ca_topic_score_codex":0.000079721365,"about_ca_topic_score_gemma":0.000046853027,"teacher_disagreement_score":0.3825117,"about_ca_system_score_codex":0.00030693604,"about_ca_system_score_gemma":0.00002814368,"threshold_uncertainty_score":0.99997115},"labels":[],"label_agreement":null},{"id":"W2008433546","doi":"10.5539/mer.v3n1p143","title":"Fuzzy-Based Adaptive Cruise Controller with Collision Avoidance and Warning System","year":2013,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cruise control; Collision avoidance system; Collision avoidance; Collision; Crash; Computer science; Robustness (evolution); Lane departure warning system; Controller (irrigation); Automotive engineering; Fuzzy logic; Warning system; Control theory (sociology); Simulation; Real-time computing; Engineering; Control (management); Computer security; Artificial intelligence; Telecommunications","score_opus":0.014244744993391944,"score_gpt":0.2210459892904093,"score_spread":0.20680124429701735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008433546","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":[0.7384161,0.001007062,0.25523004,0.00029310433,0.0001288038,0.0012865242,0.000007897701,0.0024698675,0.001160611],"genre_scores_gemma":[0.9940566,0.00002054183,0.005455511,0.0000080989685,0.0000401387,0.00030651022,0.0000015673941,0.000059331636,0.000051688927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998466,0.000057562127,0.00021996305,0.00030124493,0.0003426341,0.0006125522],"domain_scores_gemma":[0.99905795,0.00034122966,0.000016617021,0.00025669488,0.00014506603,0.0001824228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000708592,0.00020139945,0.0003006416,0.00022138997,0.00015583681,0.000050884926,0.00021676441,0.00024457235,0.000014629783],"category_scores_gemma":[0.00010639113,0.0001687049,0.000032802192,0.00039451552,0.00007194345,0.00012234096,0.00007400463,0.0009760249,0.0001038031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019966242,0.00006996863,0.00013293067,0.0007349493,0.00020230937,0.00012269014,0.00016887813,0.7848746,0.10208702,0.09516823,0.00041614627,0.015822625],"study_design_scores_gemma":[0.00087102497,0.00022202576,0.0007046297,0.00022128124,0.0000067286082,0.000013837757,0.00010574964,0.9874285,0.009756133,0.00016346331,0.00028900945,0.00021765492],"about_ca_topic_score_codex":0.00002240352,"about_ca_topic_score_gemma":0.000002918239,"teacher_disagreement_score":0.25564054,"about_ca_system_score_codex":0.00019658843,"about_ca_system_score_gemma":0.000028730925,"threshold_uncertainty_score":0.68795854},"labels":[],"label_agreement":null},{"id":"W2008578787","doi":"10.1016/j.sbspro.2012.09.824","title":"Estimation of Safety Performance Measures from Smartphone Sensors","year":2012,"lang":"en","type":"article","venue":"Procedia - Social and Behavioral Sciences","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":49,"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":"Seventh Framework Programme; Horizon 2020 Framework Programme","keywords":"Global Positioning System; Context (archaeology); Computer science; Phone; Mobile phone; Real-time computing; The Internet; Transport engineering; Engineering; Telecommunications; Geography; World Wide Web","score_opus":0.037691295696346286,"score_gpt":0.2722728873091732,"score_spread":0.23458159161282693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008578787","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":[0.99876225,0.00028447513,0.00017156376,0.000057425073,0.00013488164,0.000055010096,0.000010217857,0.00013009358,0.00039406243],"genre_scores_gemma":[0.9991571,0.000062676714,0.00069335743,0.000004402668,0.00006204206,0.000005593487,0.000003004064,0.0000038290873,0.000008005165],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99947256,0.0000069960674,0.00012946622,0.00008076534,0.00012443186,0.00018577396],"domain_scores_gemma":[0.9998804,0.0000128169795,0.000030889798,0.000028688126,0.000013275737,0.000033982353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018389669,0.00007342471,0.000111777765,0.00003198017,0.00022666487,0.000007992859,0.000078353245,0.00009134454,0.000018895042],"category_scores_gemma":[0.0000060254106,0.00006199943,0.000018308543,0.0001590193,0.00032823574,0.00028608515,0.000020329,0.00008249657,0.0000054030943],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012146728,0.000061131024,0.3547793,0.00002871881,0.000007131201,1.9478608e-7,0.004389571,0.00043861425,0.0048411316,0.000397554,0.00004012073,0.6350044],"study_design_scores_gemma":[0.00021032461,0.00007716543,0.9483583,0.000016991733,0.000045987275,0.0000028441198,0.0007675848,0.011225838,0.038311172,0.00044592438,0.00028862897,0.0002492558],"about_ca_topic_score_codex":0.000058827918,"about_ca_topic_score_gemma":0.000014515304,"teacher_disagreement_score":0.63475513,"about_ca_system_score_codex":0.000016364758,"about_ca_system_score_gemma":0.000013315013,"threshold_uncertainty_score":0.25282636},"labels":[],"label_agreement":null},{"id":"W2022816086","doi":"10.4028/www.scientific.net/amm.432.513","title":"Using Smartphones as a Tool to Capture Road Traffic Attributes","year":2013,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Autonomous Vehicle Technology and Safety","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":"University of Waterloo","funders":"","keywords":"TRACE (psycholinguistics); Vehicle tracking system; Computer science; Tracking (education); Vehicle Information and Communication System; Transport engineering; Tracking system; Global Positioning System; Floating car data; Real-time computing; Engineering; Computer security; Traffic congestion; Road traffic; Telecommunications; Artificial intelligence","score_opus":0.010553474250292507,"score_gpt":0.2012854146051114,"score_spread":0.19073194035481889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022816086","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":[0.9839628,0.00009726157,0.014462125,0.00006572296,0.00024504203,0.0005416109,0.000022257836,0.0004469314,0.0001562457],"genre_scores_gemma":[0.99251604,0.00005088568,0.0069829305,0.0001651682,0.000047850062,0.00016376382,0.000011627813,0.0000329489,0.00002879461],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992815,0.0000059453173,0.000204378,0.00018251715,0.000055870896,0.00026978718],"domain_scores_gemma":[0.99971175,0.000009105404,0.000024724668,0.00017466187,0.000016871587,0.000062865955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000115887764,0.00017005541,0.00025173524,0.0000553425,0.00007940528,0.000057084602,0.00009888796,0.00021055047,0.00024150463],"category_scores_gemma":[0.0000049208493,0.000160163,0.000017271595,0.00007859263,0.000008101325,0.000054015884,0.000063945954,0.000069498594,0.00023051014],"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":[0.000010345405,0.0000104491655,3.0945745e-7,0.000046233385,0.00003391117,0.0000026516466,0.00022131424,0.00095046085,0.9021421,0.07937414,0.0003490277,0.016859032],"study_design_scores_gemma":[0.00094166206,0.000118445765,0.00017264666,0.00004531216,0.00008626028,0.000080782884,0.0007246797,0.00933956,0.920015,0.05701768,0.010248337,0.0012096486],"about_ca_topic_score_codex":0.00003746165,"about_ca_topic_score_gemma":0.000003491538,"teacher_disagreement_score":0.02235646,"about_ca_system_score_codex":0.000023842107,"about_ca_system_score_gemma":0.0000096414615,"threshold_uncertainty_score":0.6531258},"labels":[],"label_agreement":null},{"id":"W2022826790","doi":"10.1109/itsc.2010.5625287","title":"Threat assessment design for driver assistance system at intersections","year":2010,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Ford Motor Company","keywords":"Computer science; Advanced driver assistance systems; Computer security; Artificial intelligence","score_opus":0.010976046886005583,"score_gpt":0.23119192979083217,"score_spread":0.22021588290482658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022826790","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":[0.1266146,0.000012266945,0.8505059,0.00007476429,0.0006667198,0.000250562,0.000006054488,0.0014763761,0.02039276],"genre_scores_gemma":[0.9588299,0.000002411095,0.039640658,0.00001106883,0.00003154108,0.00012242,0.000002614043,0.000017502087,0.0013419113],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956536,0.0000056004,0.00011654676,0.00011836459,0.000036431727,0.00015767226],"domain_scores_gemma":[0.9996689,0.00006032227,0.000014152689,0.0002052842,0.000020946163,0.000030378711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010661799,0.000089168905,0.0001021332,0.000038624723,0.00012809238,0.000008323006,0.000102771766,0.00014376373,0.00009983035],"category_scores_gemma":[0.000003626803,0.00008040516,0.00004566881,0.00004957437,0.00003532831,0.000056932437,0.000020765014,0.00017521657,0.000047897378],"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":[0.00007282354,0.000113205235,0.02052177,0.0003620471,0.00053055,0.000019509574,0.0003903227,0.025613012,0.2500375,0.64992,0.034702413,0.017716901],"study_design_scores_gemma":[0.0009660072,0.00011459534,0.02486526,0.000032821506,0.00006957875,0.000075923286,0.00041566225,0.8725098,0.07280035,0.0011817432,0.026418379,0.000549897],"about_ca_topic_score_codex":0.00000410933,"about_ca_topic_score_gemma":0.00023114,"teacher_disagreement_score":0.84689677,"about_ca_system_score_codex":0.00016611863,"about_ca_system_score_gemma":0.000010113629,"threshold_uncertainty_score":0.3278827},"labels":[],"label_agreement":null},{"id":"W2023661267","doi":"10.1002/atr.5670420203","title":"A methodology on the automatic recognition of poor lane keeping","year":2008,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Leeds","keywords":"Heading (navigation); Computer science; Work (physics); Observational study; Simulation; Computer security; Transport engineering; Artificial intelligence; Engineering; Medicine","score_opus":0.03868887287540722,"score_gpt":0.25315570043043173,"score_spread":0.2144668275550245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023661267","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":[0.971393,0.00012077192,0.027863966,0.00022421493,0.00014865147,0.000065600485,0.000004498671,0.000054140488,0.0001251929],"genre_scores_gemma":[0.9742262,0.00023830737,0.02544423,0.000049404473,0.000022330005,0.000002575254,0.000004560353,0.00000937434,0.0000030320289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99936986,0.00003658342,0.00038515512,0.000040888623,0.00008877381,0.000078757366],"domain_scores_gemma":[0.9994463,0.00022182649,0.00019213092,0.00006202568,0.00006170568,0.000016037942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002451909,0.00006404045,0.00017336498,0.00009689463,0.000038631526,7.064565e-7,0.00007168558,0.000068102825,0.00002766613],"category_scores_gemma":[0.00004467631,0.000048299305,0.00006612885,0.000109929606,0.000043194985,0.00010368038,4.4756163e-7,0.00021926973,0.0000033499905],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043675632,0.00016915238,0.0021992342,0.00028637482,0.00041659074,0.00019151274,0.009230406,0.4790318,0.15873443,0.0037256978,0.00014868753,0.34542936],"study_design_scores_gemma":[0.0027463017,0.001158271,0.7056822,0.00053074915,0.000219676,0.00041260256,0.0017771855,0.0066287136,0.25343284,0.026385546,0.0006272888,0.00039859817],"about_ca_topic_score_codex":0.0000010017028,"about_ca_topic_score_gemma":0.0000059278996,"teacher_disagreement_score":0.703483,"about_ca_system_score_codex":0.000021128872,"about_ca_system_score_gemma":0.000014385052,"threshold_uncertainty_score":0.19695885},"labels":[],"label_agreement":null},{"id":"W2028470329","doi":"10.4271/2014-01-0099","title":"Professional Driving Simulator to Design First-Time-Right Race Cars","year":2014,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","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":"Dalsa Corporation","funders":"","keywords":"Computer science; Driving simulator; Race (biology); Simulation","score_opus":0.006311998619498729,"score_gpt":0.21841741899464787,"score_spread":0.21210542037514912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028470329","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":[0.8920778,0.00054926256,0.0004529954,0.0158671,0.0014862842,0.0044790576,0.000077076,0.031095348,0.05391507],"genre_scores_gemma":[0.98508525,0.00008270058,0.011183624,0.0014608415,0.00020476736,0.0005075014,0.000023587483,0.00025227948,0.0011994333],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9950431,0.0002039139,0.0011905475,0.0012923123,0.00080361497,0.0014664852],"domain_scores_gemma":[0.9960521,0.0012676748,0.00014704364,0.0018094529,0.00010129575,0.0006224456],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0011025,0.0010408923,0.0012365141,0.0003472498,0.0007258895,0.00007785384,0.0016023847,0.001698505,0.0009672251],"category_scores_gemma":[0.0009763986,0.00092517503,0.00041912872,0.0009346473,0.00072895875,0.00044731487,0.00058842846,0.0020534203,0.0011571775],"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":[0.00018229961,0.00023599491,0.00023274423,0.000060808088,0.00008253664,0.000043355816,0.000054853594,0.009289472,0.96108145,0.009664936,0.01327582,0.0057957093],"study_design_scores_gemma":[0.00082710595,0.0010691659,0.8114105,0.00038312006,0.00008646357,0.000080009806,0.0000325154,0.00004354616,0.0013167934,0.002904686,0.18051179,0.0013342897],"about_ca_topic_score_codex":0.000007649049,"about_ca_topic_score_gemma":0.0085232165,"teacher_disagreement_score":0.95976466,"about_ca_system_score_codex":0.00050686044,"about_ca_system_score_gemma":0.000090644804,"threshold_uncertainty_score":0.999946},"labels":[],"label_agreement":null},{"id":"W2031081486","doi":"10.1145/2641798.2641807","title":"Lane detection and tracking system based on the MSER algorithm, hough transform and kalman filter","year":2014,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":59,"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 Ottawa","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Hough transform; Computer vision; Artificial intelligence; Kalman filter; Computer science; Tracking (education); Pixel; Algorithm; Pattern recognition (psychology); Image (mathematics)","score_opus":0.005353732338655945,"score_gpt":0.16710991593441762,"score_spread":0.1617561835957617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031081486","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":[0.3170449,0.00007360838,0.6581439,0.0007598339,0.00013852619,0.00027774888,0.00000486332,0.0013770261,0.022179564],"genre_scores_gemma":[0.9992095,0.000006521523,0.0005962549,0.00008551533,0.000029273355,0.000012415133,8.0067343e-7,0.000013545747,0.000046149973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963367,0.000015643804,0.000088680834,0.00010066157,0.000043114036,0.000118200194],"domain_scores_gemma":[0.9997734,0.00007467667,0.000008462097,0.000116288946,0.000005791758,0.00002139767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017583098,0.000090483896,0.00008984586,0.000036518944,0.00009817713,0.000015126301,0.000043524156,0.000107878026,0.00001464317],"category_scores_gemma":[0.0000035097305,0.000060436443,0.000016018663,0.000043317625,0.00003200744,0.00004594662,0.000004411317,0.0001541011,0.000006972346],"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":[0.000010417689,0.0000075236753,0.00015395177,0.00009140747,0.000023931423,0.000002328204,0.0001486563,0.000973355,0.0011014382,0.0021998119,0.000034877055,0.9952523],"study_design_scores_gemma":[0.0003057934,0.00006850424,0.0044971937,0.000026545973,0.000013128782,0.000017289407,0.00011562394,0.9593081,0.033306554,0.00016546859,0.002054555,0.00012125954],"about_ca_topic_score_codex":0.000009277356,"about_ca_topic_score_gemma":0.00003327604,"teacher_disagreement_score":0.995131,"about_ca_system_score_codex":0.000018213535,"about_ca_system_score_gemma":0.0000013558885,"threshold_uncertainty_score":0.24645266},"labels":[],"label_agreement":null},{"id":"W2042633954","doi":"10.3141/2083-11","title":"Probabilistic Framework for Automated Analysis of Exposure to Road Collisions","year":2008,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":143,"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; Probabilistic logic; Hierarchy; Perspective (graphical); Traffic analysis; Data collection; Collision; Traffic conflict; Data science; Transport engineering; Data mining; Artificial intelligence; Computer security; Floating car data; Engineering; Traffic congestion","score_opus":0.06735339908335354,"score_gpt":0.3654327493464628,"score_spread":0.2980793502631093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042633954","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":[0.9751957,0.00014633905,0.02189211,0.0009238526,0.000230133,0.001182054,0.00022636101,0.00016183866,0.00004158514],"genre_scores_gemma":[0.9861919,0.00039110467,0.013037014,0.00001581611,0.00004598705,0.00014457053,0.000024279489,0.000045374676,0.00010394918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9956743,0.0003323043,0.0013890521,0.00027643915,0.0016563314,0.00067155814],"domain_scores_gemma":[0.9948985,0.0013367693,0.00022471507,0.00052005466,0.0027369487,0.00028300416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002368986,0.00020046125,0.00064455793,0.001925463,0.00049443194,0.000021655016,0.00093164394,0.00032006708,0.000116475974],"category_scores_gemma":[0.00042612228,0.00016059724,0.0005319524,0.005666631,0.0005062383,0.00022166916,0.0000049996574,0.0014346668,0.0000069887983],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00160336,0.0003945075,0.20763661,0.00044869335,0.0020758915,0.00008220285,0.007311696,0.754993,0.004648632,0.012100006,0.004366372,0.004338991],"study_design_scores_gemma":[0.00064542156,0.000682394,0.9782867,0.00017286158,0.00025846678,4.3460855e-7,0.00065312977,0.013669147,0.0015655515,0.0026192577,0.0012817692,0.0001649022],"about_ca_topic_score_codex":0.0007895559,"about_ca_topic_score_gemma":0.007689829,"teacher_disagreement_score":0.77065,"about_ca_system_score_codex":0.00019606506,"about_ca_system_score_gemma":0.0003654019,"threshold_uncertainty_score":0.65489656},"labels":[],"label_agreement":null},{"id":"W2045598993","doi":"10.1080/1538-950391915740","title":"The Alcohol Interlock: An Underutilized Resource for Predicting and Controlling Drunk Drivers","year":2003,"lang":"en","type":"article","venue":"Traffic Injury Prevention","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Institute on Alcohol Abuse and Alcoholism","keywords":"Drunk driving; Interlock; Drunk drivers; Poison control; Occupational safety and health; Injury prevention; Human factors and ergonomics; Resource (disambiguation); Alcohol; Driving under the influence; Engineering; Suicide prevention; Transport engineering; Business; Forensic engineering; Computer security; Environmental health; Medical emergency; Medicine; Computer science; Chemistry","score_opus":0.011453226662961311,"score_gpt":0.2487707283814751,"score_spread":0.2373175017185138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045598993","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":[0.9758951,0.00031316697,0.021766532,0.00005959105,0.00016349,0.00051255175,0.0000049744053,0.00065984245,0.00062473974],"genre_scores_gemma":[0.9987312,0.000057566707,0.00085470034,0.000011965805,0.000026577629,0.00005386097,0.00000818429,0.000023400167,0.00023257389],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992398,0.00007055558,0.00022983502,0.00016547955,0.00005719276,0.00023714559],"domain_scores_gemma":[0.99961025,0.00011604613,0.000046830242,0.00017079922,0.000017629283,0.000038457412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006062688,0.00011750284,0.00013002021,0.000045425488,0.00033778857,0.000030407282,0.000101836595,0.00014077684,0.000005541456],"category_scores_gemma":[0.000045448607,0.000103384635,0.000059411563,0.00006455079,0.0000759945,0.000114076654,0.000009185254,0.00018233326,0.0000018973091],"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":[0.00050481013,0.00011822809,0.000581458,0.000110660396,0.00042278107,0.0000021636831,0.0020099531,0.06401147,0.006198909,0.032889858,0.00077501946,0.8923747],"study_design_scores_gemma":[0.0042867046,0.00062499347,0.00078872027,0.000102525235,0.0001942262,0.000021179447,0.0029400017,0.94470215,0.00566584,0.0059538884,0.034225896,0.00049390295],"about_ca_topic_score_codex":4.893768e-7,"about_ca_topic_score_gemma":0.000016900309,"teacher_disagreement_score":0.8918808,"about_ca_system_score_codex":0.00004572163,"about_ca_system_score_gemma":0.000013295107,"threshold_uncertainty_score":0.42159033},"labels":[],"label_agreement":null},{"id":"W2059002337","doi":"10.1109/itsc.2012.6338721","title":"Motion stereo-based collision avoidance for an intelligent smart car door system","year":2012,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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","funders":"","keywords":"Collision avoidance; Computer science; Collision; Kinematics; Collision avoidance system; Advanced driver assistance systems; Controller (irrigation); Simulation; Engineering; Artificial intelligence; Computer security","score_opus":0.018269834448664087,"score_gpt":0.23192611613054528,"score_spread":0.2136562816818812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059002337","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":[0.4568771,0.00013781167,0.5397325,0.00002933436,0.00041418977,0.00034034162,0.000011340945,0.0011561823,0.0013012066],"genre_scores_gemma":[0.9948885,0.0000036008971,0.0047641024,0.0000311606,0.00007084947,0.00007715803,0.0000195454,0.00002600393,0.00011908563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993389,0.000017623699,0.00019183697,0.000118691416,0.000058815018,0.00027412528],"domain_scores_gemma":[0.9995839,0.000037526246,0.000023436776,0.00025034032,0.000029470655,0.000075332515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025896463,0.00011909124,0.0001388633,0.00006053877,0.00008129386,0.000009253643,0.000112330046,0.00017079854,0.000014975534],"category_scores_gemma":[0.0000075334674,0.00010855798,0.000046110363,0.00007620391,0.00002018297,0.00016319133,0.000011360417,0.00009823839,0.00006995002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049777597,0.0010543151,0.06145118,0.004127126,0.00039881637,0.000008471257,0.002752696,0.3060811,0.05963786,0.27103734,0.006813814,0.28613952],"study_design_scores_gemma":[0.00047615918,0.00014434164,0.0037050184,0.000053469816,0.000029356663,0.000006982999,0.00048951653,0.75395584,0.22915109,0.000062762156,0.0116000185,0.00032547626],"about_ca_topic_score_codex":0.000010920581,"about_ca_topic_score_gemma":0.000021091162,"teacher_disagreement_score":0.5380114,"about_ca_system_score_codex":0.00016078902,"about_ca_system_score_gemma":0.000008008182,"threshold_uncertainty_score":0.4426866},"labels":[],"label_agreement":null},{"id":"W2070593805","doi":"10.4271/2015-01-1406","title":"Collision Avoidance Systems - Advancements and Efficiency","year":2015,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Volvo (Canada)","funders":"","keywords":"Collision avoidance; Computer science; Collision; Computer security","score_opus":0.010981661161110582,"score_gpt":0.2287081578809259,"score_spread":0.21772649671981534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070593805","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":[0.9173714,0.0052549443,0.000031877167,0.00132326,0.0008525094,0.0014919424,0.00007566897,0.009290206,0.06430822],"genre_scores_gemma":[0.99622154,0.00095545035,0.0015537131,0.00033361345,0.00009147307,0.00028427006,0.000022390615,0.00011939762,0.0004181571],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99643767,0.000092636394,0.0009363402,0.0009215879,0.00067404757,0.0009377053],"domain_scores_gemma":[0.9979221,0.00023237712,0.00013493458,0.0011610318,0.00009713264,0.00045239835],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007228317,0.00069790665,0.00085786125,0.00020844625,0.00032963906,0.00008083082,0.00083696865,0.0010028939,0.000045562112],"category_scores_gemma":[0.00040400107,0.0006275026,0.00015599752,0.00070494664,0.000962797,0.00049253064,0.00037029348,0.0012730869,0.00016619952],"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":[0.00022733015,0.00025330533,0.00043807284,0.000119887816,0.000052832427,0.000079487385,0.000053602562,0.0023719561,0.9615996,0.025114639,0.004023434,0.0056658587],"study_design_scores_gemma":[0.0017130132,0.0016739587,0.8393658,0.00037852925,0.00007691004,0.00024197712,0.00028758773,0.0000141062455,0.00035320598,0.0025213065,0.15211989,0.0012537381],"about_ca_topic_score_codex":0.000017778364,"about_ca_topic_score_gemma":0.0047915326,"teacher_disagreement_score":0.9612464,"about_ca_system_score_codex":0.00042932987,"about_ca_system_score_gemma":0.000070615904,"threshold_uncertainty_score":0.99961764},"labels":[],"label_agreement":null},{"id":"W2098380626","doi":"10.5539/mas.v8n1p88","title":"Driving Behavior and Traffic Safety: An Acceleration-Based Safety Evaluation Procedure for Smartphones","year":2014,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":110,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Global Positioning System; Acceleration; Accelerometer; Usability; Warning system; Mobile device; Transport engineering; Simulation; Automotive engineering; Real-time computing; Human–computer interaction; Telecommunications; Engineering","score_opus":0.013305331268598704,"score_gpt":0.2446282977562307,"score_spread":0.231322966487632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098380626","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":[0.6303424,0.000018156745,0.3679426,0.000080344966,0.000067779976,0.00067236007,0.000004111591,0.00041359282,0.00045864735],"genre_scores_gemma":[0.99401337,0.0000027137078,0.0055036624,0.000057430745,0.000034784953,0.00033896483,0.000019244788,0.000022129921,0.000007676043],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879074,0.0000117050795,0.00022029624,0.00040579683,0.00025869362,0.00031275328],"domain_scores_gemma":[0.99945587,0.000059311173,0.000040803992,0.00027964663,0.00007630484,0.00008803827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012211511,0.0001531123,0.0001519107,0.00012540782,0.000573368,0.00006759756,0.00026324118,0.00012245456,0.000011230586],"category_scores_gemma":[0.000044783945,0.00015473978,0.000019758101,0.00023226356,0.00025386928,0.00024832788,0.000023632794,0.00012614182,0.000004139532],"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":[0.000032881297,0.000051012394,0.0008038421,0.00003761299,0.0000041534136,1.218521e-7,0.00062757137,0.2926168,0.20514429,0.0026935446,0.000008970175,0.4979792],"study_design_scores_gemma":[0.0005267833,0.000035696758,0.020868123,0.000006520097,0.000022507944,0.0000015790538,0.000036073383,0.9675467,0.009797611,0.00085492834,0.00011046514,0.00019303273],"about_ca_topic_score_codex":8.094822e-7,"about_ca_topic_score_gemma":0.000058899877,"teacher_disagreement_score":0.67492986,"about_ca_system_score_codex":0.00011619644,"about_ca_system_score_gemma":0.00012469754,"threshold_uncertainty_score":0.63101053},"labels":[],"label_agreement":null},{"id":"W2100357954","doi":"10.1109/fuzz-ieee.2014.6891748","title":"Fuzzy Adaptive Cruise Control system with speed sign detection capability","year":2014,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakeridge Health; Sinai Health System; Ontario Tech University","funders":"","keywords":"Cruise control; Advanced driver assistance systems; Computer science; Speed limit; Lane departure warning system; Fuzzy logic; Feature (linguistics); Fuzzy control system; Control system; Automotive engineering; Real-time computing; Control (management); Engineering; Artificial intelligence; Transport engineering","score_opus":0.004519894995131484,"score_gpt":0.16244048862878072,"score_spread":0.15792059363364924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100357954","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":[0.34669912,0.000022323009,0.59505916,0.000034410685,0.000109364475,0.00030381136,0.000003032609,0.0026604044,0.05510836],"genre_scores_gemma":[0.99931085,5.8151744e-7,0.0005280786,0.000018209397,0.000040011713,0.000017656053,5.6158876e-7,0.000019531835,0.00006450211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993695,0.000040209958,0.00015321632,0.00016715644,0.00007007189,0.00019982594],"domain_scores_gemma":[0.9995675,0.000066173576,0.000024048706,0.0002564476,0.000037863705,0.000047964506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021000895,0.00013384543,0.00019634605,0.00004558784,0.000070411654,0.000007415995,0.00008733687,0.00015684369,0.000015407893],"category_scores_gemma":[0.000011833176,0.000106132946,0.000032845484,0.00009543804,0.00006655389,0.00007154476,0.000007614773,0.00018781048,0.00010166861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014304029,0.00020019809,0.011905666,0.0006581297,0.0009960086,0.00004233595,0.0007874991,0.5053763,0.062750675,0.20169736,0.00031687226,0.21383856],"study_design_scores_gemma":[0.001842456,0.0004548743,0.017767722,0.00002977484,0.000060585156,0.000041924843,0.000476963,0.94832027,0.02942128,0.000780035,0.0003780378,0.00042605243],"about_ca_topic_score_codex":0.000060804414,"about_ca_topic_score_gemma":0.00018252422,"teacher_disagreement_score":0.65261173,"about_ca_system_score_codex":0.00013100193,"about_ca_system_score_gemma":0.000007855224,"threshold_uncertainty_score":0.43279758},"labels":[],"label_agreement":null},{"id":"W2100463880","doi":"10.1109/avss.2012.61","title":"Road Boundary Detection in Challenging Scenarios","year":2012,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Boundary (topology); Artificial intelligence; Cluster analysis; Computer vision; Range (aeronautics); Perspective (graphical); Point (geometry); Object detection; Pattern recognition (psychology); Cluster (spacecraft); Filter (signal processing); Mathematics; Engineering","score_opus":0.007510539133718056,"score_gpt":0.1986862685465133,"score_spread":0.19117572941279526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100463880","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":[0.9551977,0.0034055188,0.01714993,0.00011875492,0.00038116524,0.00009152163,3.0977606e-7,0.0014464863,0.022208618],"genre_scores_gemma":[0.99954754,0.00007935413,0.00016802741,0.000040467887,0.000044262055,0.000009960958,5.2640894e-7,0.000012803624,0.000097041855],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995582,0.0000058890996,0.000099305355,0.000056223664,0.000031829477,0.0002485363],"domain_scores_gemma":[0.9998541,0.000007315996,0.000005799438,0.000101257414,0.0000030026301,0.000028549428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000113802584,0.00006756711,0.00007161653,0.00009370943,0.000041745094,0.0000040919836,0.000050152325,0.00012228845,0.00011003495],"category_scores_gemma":[0.0000046506543,0.000069408365,0.000017453196,0.00009940037,0.000016739137,0.00016550839,0.000014995401,0.00019823069,0.00014027682],"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":[0.000010180623,0.00008091435,0.014238793,0.00005562453,0.000040326417,0.0000077032955,0.0012696567,0.010575778,0.011957336,0.0026599702,0.00029153793,0.9588122],"study_design_scores_gemma":[0.0010147276,0.000053330055,0.37032902,0.000042033378,0.000015034485,0.000087567394,0.0008383613,0.45084986,0.09318227,0.0007420545,0.081957616,0.0008881089],"about_ca_topic_score_codex":0.000016429729,"about_ca_topic_score_gemma":0.00006521811,"teacher_disagreement_score":0.95792407,"about_ca_system_score_codex":0.00007344457,"about_ca_system_score_gemma":0.000002982423,"threshold_uncertainty_score":0.2830391},"labels":[],"label_agreement":null},{"id":"W2102811245","doi":"10.3390/ijgi4031301","title":"Tracking 3D Moving Objects Based on GPS/IMU Navigation Solution, Laser Scanner Point Cloud and GIS Data","year":2015,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Point cloud; Computer vision; Inertial measurement unit; Computer science; Global Positioning System; Artificial intelligence; Mobile mapping; Lidar; Orientation (vector space); Ranging; Video tracking; Object (grammar); Remote sensing; Geography","score_opus":0.01462256813972452,"score_gpt":0.24167742947147294,"score_spread":0.22705486133174843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102811245","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":[0.505304,0.00019847137,0.47620103,0.0054420475,0.005402833,0.00030935818,0.00021308137,0.00038581548,0.006543393],"genre_scores_gemma":[0.99586624,0.00002184978,0.003292562,0.00032496126,0.00027724088,0.000001831656,0.00019768479,0.000009800548,0.000007807696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986923,0.000025654414,0.0005901411,0.000075157965,0.00046632942,0.0001504707],"domain_scores_gemma":[0.998865,0.000051659306,0.00030748828,0.000197521,0.000493199,0.00008514615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008019139,0.00013201106,0.00014540023,0.00034636777,0.00007267183,0.000115118746,0.00041473252,0.00013549563,0.000020039466],"category_scores_gemma":[0.00025677538,0.00012719305,0.00003813682,0.00010240575,0.00004284307,0.0036751966,0.0000726557,0.0003472692,0.000039636783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005212674,0.000076317156,0.0023706404,0.000087033215,0.0003307559,0.000055376524,0.0039558266,0.540647,0.00036484373,0.0014759118,0.013798407,0.43631667],"study_design_scores_gemma":[0.0017897431,0.00012420563,0.0043791123,0.00025223842,0.000034228022,0.00025922776,0.0005963571,0.97592753,0.0021499975,0.0008995634,0.0133578805,0.00022989961],"about_ca_topic_score_codex":0.000017567614,"about_ca_topic_score_gemma":0.000007739739,"teacher_disagreement_score":0.4905623,"about_ca_system_score_codex":0.00028020208,"about_ca_system_score_gemma":0.00009478879,"threshold_uncertainty_score":0.5186782},"labels":[],"label_agreement":null},{"id":"W2110695043","doi":"10.1109/itsc.2012.6338635","title":"Anti-trap protection for an intelligent smart car door system","year":2012,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Doors; Omnidirectional camera; Focus (optics); Computer science; Advanced driver assistance systems; Drone; Smoothness; Real-time computing; Engineering; Omnidirectional antenna; Embedded system; Artificial intelligence; Telecommunications","score_opus":0.0227406559633884,"score_gpt":0.22877221495914207,"score_spread":0.20603155899575368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110695043","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":[0.60214084,0.0001682642,0.38971534,0.000036977613,0.0006373715,0.00069965573,0.0000046827236,0.002514678,0.0040822015],"genre_scores_gemma":[0.9981392,0.0000052292226,0.0014512601,0.000009361973,0.0001069638,0.00014602707,0.000004271321,0.00002203046,0.00011565707],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948007,0.000008897569,0.00013993088,0.00008885024,0.00003591608,0.0002463642],"domain_scores_gemma":[0.99973625,0.000007988756,0.000013721644,0.00017027893,0.000016788754,0.00005499696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017905385,0.00009316429,0.000116958756,0.000051889725,0.00006849063,0.0000066252114,0.00007598214,0.0001586569,0.00001680823],"category_scores_gemma":[0.0000036767065,0.000083734914,0.000038822327,0.00006303549,0.000016410104,0.00014904134,0.000009238329,0.00010329636,0.00007060455],"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":[0.00012086251,0.0005128206,0.02130609,0.0028179653,0.00065484847,0.000004643551,0.0031639729,0.016151361,0.15540403,0.3343813,0.0028444787,0.4626376],"study_design_scores_gemma":[0.0005640612,0.00034232746,0.011268229,0.000056790504,0.000076439246,0.00010050586,0.0018622667,0.30870783,0.60345334,0.00028000816,0.07251773,0.0007704768],"about_ca_topic_score_codex":0.00002065311,"about_ca_topic_score_gemma":0.000013682348,"teacher_disagreement_score":0.46186715,"about_ca_system_score_codex":0.000080027065,"about_ca_system_score_gemma":0.000004607033,"threshold_uncertainty_score":0.34146106},"labels":[],"label_agreement":null},{"id":"W2122886340","doi":"10.1109/ivs.2008.4621158","title":"Realtime experiments in Markov-based lane position estimation using wireless ad-hoc network","year":2008,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Global Positioning System; Computer science; Real-time computing; Particle filter; Automatic vehicle location; Wireless ad hoc network; Positioning system; Wireless; Assisted GPS; Markov process; Filter (signal processing); Software; Hidden Markov model; Noise (video); Telecommunications; Engineering; Artificial intelligence; Computer vision","score_opus":0.014272520856794726,"score_gpt":0.2318317662578143,"score_spread":0.21755924540101956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122886340","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":[0.92420995,0.0002458792,0.07309118,0.000052833533,0.00008217252,0.00012851588,0.0000012903683,0.0006486872,0.0015394659],"genre_scores_gemma":[0.9698996,0.000029444678,0.029899992,0.00005817348,0.00001750159,0.000011158117,0.000026268372,0.000020026271,0.00003785573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993938,0.000018698363,0.00018807448,0.00011789857,0.0000649714,0.00021656448],"domain_scores_gemma":[0.9997775,0.0000198116,0.00002236177,0.0001441043,0.000009017618,0.000027221386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000078566,0.00010568088,0.00012954077,0.00007683992,0.00007877082,0.0000039981746,0.00006641587,0.00015338925,0.000067658075],"category_scores_gemma":[0.0000021944097,0.000115396375,0.000021151527,0.00017435476,0.00003158673,0.00009587173,0.000011757334,0.00013497417,0.000028139335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006527056,0.000068404384,0.0054122643,0.000029883508,0.000024825862,0.00007908002,0.00019915373,0.96064013,0.008674424,0.00044769334,0.0004306009,0.023928296],"study_design_scores_gemma":[0.000381477,0.000022097178,0.009484535,0.00003138772,0.000003925801,0.000018777599,0.000008684576,0.98225987,0.0074853874,0.00011915481,0.000048707225,0.00013602107],"about_ca_topic_score_codex":0.000027841386,"about_ca_topic_score_gemma":0.000019004132,"teacher_disagreement_score":0.0456896,"about_ca_system_score_codex":0.00015435847,"about_ca_system_score_gemma":0.000018608716,"threshold_uncertainty_score":0.47057277},"labels":[],"label_agreement":null},{"id":"W2136001549","doi":"10.1109/icmech.2011.5971269","title":"An overriding controller for vehicle lateral control system","year":2011,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Simon Fraser University","funders":"","keywords":"Robustness (evolution); Control theory (sociology); Distraction; Controller (irrigation); SIGNAL (programming language); Advanced driver assistance systems; Control signal; Control system; Computer science; Monte Carlo method; Engineering; Control engineering; Control (management); Artificial intelligence","score_opus":0.011479068979250273,"score_gpt":0.190133492419544,"score_spread":0.17865442344029372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136001549","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":[0.6534383,0.00010520488,0.31568348,0.000036223588,0.0003783585,0.00067010394,0.000029875866,0.0044269026,0.0252315],"genre_scores_gemma":[0.99822694,0.0000011826232,0.0014563203,0.00006588183,0.00004938504,0.000071967326,0.0000019620704,0.000027543689,0.00009878772],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939066,0.000010896958,0.00017528517,0.00012821816,0.00003624301,0.00025869624],"domain_scores_gemma":[0.99971193,0.00002803854,0.000018108425,0.00016533205,0.000022447319,0.00005411979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013761887,0.00011326721,0.00019388371,0.000044043383,0.00007478629,0.000009282153,0.0001380637,0.00014849342,0.000052387142],"category_scores_gemma":[0.0000036665026,0.00010056701,0.00005620671,0.00003608321,0.000022942331,0.00013938187,0.00000594994,0.00008799304,0.00004398904],"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":[0.0010303711,0.00023051334,0.032512672,0.00043678452,0.0010259604,0.000047194495,0.0020459152,0.011560068,0.11156045,0.8023598,0.002167561,0.035022665],"study_design_scores_gemma":[0.0026176146,0.00013110993,0.0063891592,0.000011073603,0.00003234942,0.000008788223,0.00015188596,0.97616816,0.012855108,0.0006096497,0.0007981324,0.00022698737],"about_ca_topic_score_codex":0.000017655935,"about_ca_topic_score_gemma":0.000008156235,"teacher_disagreement_score":0.9646081,"about_ca_system_score_codex":0.0000472896,"about_ca_system_score_gemma":0.0000053354133,"threshold_uncertainty_score":0.41010037},"labels":[],"label_agreement":null},{"id":"W2137355359","doi":"10.3141/2393-09","title":"Application of Computer Vision to Diagnosis of Pedestrian Safety Issues","year":2013,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pedestrian; Intersection (aeronautics); Computer science; Transport engineering; Collision; Computer security; Risk analysis (engineering); Engineering; Business","score_opus":0.030828906279239992,"score_gpt":0.34069546796421024,"score_spread":0.30986656168497023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137355359","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":[0.9645822,0.00012775928,0.029153377,0.004370944,0.00017834437,0.0014201533,0.000053580134,0.00004610924,0.000067503235],"genre_scores_gemma":[0.99088717,0.00065335527,0.008107882,0.000023182009,0.00007736722,0.0001436338,0.000009605628,0.000039857616,0.000057934903],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.995861,0.00032323782,0.0014283543,0.00024311182,0.0015966297,0.00054767105],"domain_scores_gemma":[0.99608123,0.0005749317,0.00022290419,0.00047534032,0.002339358,0.0003062224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022395889,0.00017849263,0.0004705556,0.0010615684,0.00017887208,0.000026800753,0.00094524643,0.00020214602,0.0002158003],"category_scores_gemma":[0.000065463115,0.00014395048,0.0002236965,0.0020122428,0.0002957784,0.0003541202,0.000009854144,0.001031641,0.000051430572],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014605349,0.000539273,0.5588938,0.0010530456,0.0003392051,0.000019623705,0.0058146208,0.085443154,0.04535318,0.003311367,0.01698444,0.28078777],"study_design_scores_gemma":[0.00059767003,0.000919928,0.9642237,0.00028112586,0.000021415712,2.1482728e-7,0.00044795964,0.001909364,0.020488765,0.0016442963,0.009325279,0.00014028973],"about_ca_topic_score_codex":0.0062689343,"about_ca_topic_score_gemma":0.008512307,"teacher_disagreement_score":0.4053299,"about_ca_system_score_codex":0.00014157325,"about_ca_system_score_gemma":0.00013344959,"threshold_uncertainty_score":0.9476795},"labels":[],"label_agreement":null},{"id":"W2144087258","doi":"10.1109/ccece.2007.306","title":"A New Driving Assistant for Automobiles","year":2007,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Simon Fraser University","funders":"","keywords":"Microcontroller; Computer science; Ultrasonic sensor; Wireless; Embedded system; Steering wheel; Computer hardware; Engineering; Real-time computing; Automotive engineering; Telecommunications","score_opus":0.005352929531908322,"score_gpt":0.21698596582750104,"score_spread":0.2116330362955927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144087258","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":[0.058883905,0.00009789785,0.9161244,0.00009600258,0.0001431431,0.00011586967,8.9129634e-7,0.0018595601,0.02267832],"genre_scores_gemma":[0.94961476,0.0000049686337,0.048708662,0.000028261278,0.00005270894,0.0000058182086,0.0000013022196,0.000016284543,0.0015672104],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995436,9.968438e-7,0.00012635691,0.00007971227,0.000031417232,0.00021793344],"domain_scores_gemma":[0.999759,0.00006225879,0.000008533909,0.00011545554,0.000007879345,0.00004687797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015229649,0.000069795686,0.00008181456,0.000048983966,0.00004103023,0.0000060669195,0.000085036925,0.00010580556,0.00010427416],"category_scores_gemma":[0.00001112294,0.00006482773,0.000037920887,0.00006789775,0.0000120672685,0.00004152022,0.000011635056,0.00007569193,0.00002700964],"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":[0.00001722277,0.00002241003,0.0047368687,0.000045343666,0.000101873906,0.000013479551,0.00025554493,0.0016825818,0.024413431,0.14840011,0.021848131,0.798463],"study_design_scores_gemma":[0.001474764,0.00015720185,0.07919529,0.00004369423,0.000045736087,0.00003650017,0.00039561297,0.08764333,0.2374742,0.017537737,0.57507914,0.00091681146],"about_ca_topic_score_codex":0.000004490493,"about_ca_topic_score_gemma":0.000117583106,"teacher_disagreement_score":0.89073086,"about_ca_system_score_codex":0.000040700434,"about_ca_system_score_gemma":0.000012795203,"threshold_uncertainty_score":0.2643598},"labels":[],"label_agreement":null},{"id":"W2164372390","doi":"10.1109/ivs.2009.5164254","title":"Design and stability analysis of a fuzzy controller for autonomous road following","year":2009,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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 New Brunswick","funders":"","keywords":"Nonholonomic system; Control theory (sociology); Fuzzy control system; Controller (irrigation); Fuzzy logic; Control engineering; Lyapunov stability; Lyapunov function; Computer science; Stability (learning theory); Vehicle dynamics; Convergence (economics); Mobile robot; Engineering; Robot; Control (management); Artificial intelligence; Nonlinear system; Automotive engineering","score_opus":0.012947254723620788,"score_gpt":0.22687287258356129,"score_spread":0.2139256178599405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164372390","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":[0.4302014,0.00024181326,0.5676729,0.00008847995,0.000019575522,0.0002894143,0.0000039891083,0.00033955034,0.0011428372],"genre_scores_gemma":[0.98157305,0.000007728993,0.018330092,0.0000360463,0.0000037887019,0.000016678709,0.000002382966,0.0000063443536,0.000023895978],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993831,0.0000151765,0.0002432539,0.0001451783,0.000041986747,0.00017129784],"domain_scores_gemma":[0.99965036,0.00011328561,0.000024992114,0.00015987226,0.000019596295,0.000031915723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033437862,0.00010254429,0.000383456,0.00013712829,0.000041445586,0.0000058056708,0.000074929594,0.00012284813,0.000016589403],"category_scores_gemma":[0.000024865374,0.000093408424,0.00015118516,0.00024950303,0.000027161017,0.00005768048,0.000007366641,0.000064450825,7.255247e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040918123,0.00027920393,0.009133993,0.000080715254,0.008780442,0.0000076041483,0.001686172,0.21886891,0.08777899,0.022435984,0.00023550946,0.6503033],"study_design_scores_gemma":[0.0008474445,0.0001234715,0.025369298,0.0000024811789,0.00066438067,4.934603e-7,0.000058220492,0.95868576,0.010230228,0.0038021193,0.000057450696,0.00015865848],"about_ca_topic_score_codex":0.0000071856243,"about_ca_topic_score_gemma":0.000007989047,"teacher_disagreement_score":0.73981684,"about_ca_system_score_codex":0.000031391464,"about_ca_system_score_gemma":0.000010391486,"threshold_uncertainty_score":0.3809085},"labels":[],"label_agreement":null},{"id":"W2170678041","doi":"10.1109/isocc.2014.7087671","title":"On-road vehicle detection with monocular camera for embedded realization: Robust algorithms and evaluations","year":2014,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Nexen (Canada)","funders":"","keywords":"Computer science; Realization (probability); Automotive industry; Reduction (mathematics); Algorithm; Monocular; Artificial intelligence; Computational complexity theory; Advanced driver assistance systems; Robustness (evolution); Computer vision; Engineering; Mathematics","score_opus":0.01115681720683402,"score_gpt":0.2296523270762674,"score_spread":0.2184955098694334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170678041","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":[0.12691523,0.000028162267,0.87048477,0.000120098295,0.000038625374,0.00022875793,0.0000026262242,0.00052019785,0.0016615244],"genre_scores_gemma":[0.983992,0.0000128843885,0.015661538,0.00010049446,0.000022059077,0.0000915929,0.000011029277,0.00002272982,0.00008571229],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955195,0.000017594746,0.00010441873,0.00014269947,0.000061406354,0.000121942074],"domain_scores_gemma":[0.9996936,0.000054864035,0.00001737926,0.00015858882,0.000044541106,0.00003106791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017435645,0.00009479806,0.00009711756,0.000057734767,0.00014624832,0.000020375985,0.00004251367,0.00009663158,0.000016650749],"category_scores_gemma":[0.000026967758,0.0000827792,0.000016654203,0.000098725846,0.000041228806,0.00008174083,0.000007192013,0.00008482227,0.000005360475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046414534,0.000030330093,0.00014442492,0.000033959517,0.00008191056,5.6603443e-7,0.00025261176,0.5796318,0.0018697337,0.03481272,0.0001291862,0.3829663],"study_design_scores_gemma":[0.000567098,0.00022011099,0.0018206519,0.000007540052,0.000022182008,0.000004920924,0.00004351478,0.9877919,0.006667572,0.0022776225,0.00045441257,0.00012244895],"about_ca_topic_score_codex":0.000015843316,"about_ca_topic_score_gemma":0.00009423347,"teacher_disagreement_score":0.8570767,"about_ca_system_score_codex":0.000027792214,"about_ca_system_score_gemma":0.000010160124,"threshold_uncertainty_score":0.33756378},"labels":[],"label_agreement":null},{"id":"W2182081311","doi":"","title":"A probabilistic framework for the automated analysis of the exposure to road collision","year":2008,"lang":"en","type":"other","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Autonomous Vehicle Technology and Safety","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 British Columbia","funders":"","keywords":"Probabilistic logic; Collision; Computer science; Data collection; Work (physics); Collision avoidance; Risk analysis (engineering); Transport engineering; Computer security; Artificial intelligence; Engineering","score_opus":0.007406733293453597,"score_gpt":0.22182591131691357,"score_spread":0.21441917802345997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182081311","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":[0.007151002,0.006967437,0.95982873,0.0023734733,0.0005392561,0.006131698,0.0010693503,0.010816022,0.0051230397],"genre_scores_gemma":[0.8151102,0.0017712734,0.12628618,0.00142412,0.0003413774,0.0063759326,0.00012681195,0.0016273407,0.046936784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816644,0.00006818135,0.00054671365,0.00039245398,0.0002786229,0.00054759195],"domain_scores_gemma":[0.99744576,0.00030047412,0.00026807532,0.0018096972,0.00007142568,0.000104569],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031999813,0.0004656742,0.00078650203,0.0007654001,0.00021952901,0.00002943273,0.0011993975,0.0012842879,0.00006947202],"category_scores_gemma":[0.00035431597,0.00031661987,0.00051373267,0.002241516,0.00018509738,0.000030131236,0.0001894433,0.0006629051,0.000008159816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009707503,0.00020198488,0.0026241725,0.00028892478,0.0045222626,0.00001320426,0.00061534846,0.6733201,0.0004275055,0.02527348,0.27600694,0.016608983],"study_design_scores_gemma":[0.0002916647,0.0001397205,0.023623887,0.00032536525,0.0016125548,0.000018148614,0.00005818201,0.9157422,0.0011745713,0.00090119656,0.055403072,0.0007094191],"about_ca_topic_score_codex":0.0017971037,"about_ca_topic_score_gemma":0.0052050585,"teacher_disagreement_score":0.8335425,"about_ca_system_score_codex":0.00031582397,"about_ca_system_score_gemma":0.00013153661,"threshold_uncertainty_score":0.9999286},"labels":[],"label_agreement":null},{"id":"W2186609048","doi":"10.1109/ssd.2015.7348214","title":"Driving style assessment based on the GPS data and fuzzy inference systems","year":2015,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Jerk; Global Positioning System; Acceleration; Computer science; Kalman filter; Fuzzy logic; Adaptive neuro fuzzy inference system; Noise (video); Hidden Markov model; Advanced driver assistance systems; Simulation; Fuzzy control system; Control theory (sociology); Artificial intelligence","score_opus":0.04009540126295531,"score_gpt":0.26909907449547593,"score_spread":0.22900367323252063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186609048","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":[0.27612883,0.00030699704,0.4918956,0.0023513006,0.00056587404,0.0005735854,0.000036638867,0.002217932,0.22592326],"genre_scores_gemma":[0.99870086,0.000008511463,0.0011084011,0.000062532265,0.000017720928,0.000011073062,0.000008523498,0.000009340108,0.00007304237],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995305,0.00002094421,0.00010348273,0.0001301429,0.000088829955,0.00012608741],"domain_scores_gemma":[0.9991682,0.00015639613,0.000014231584,0.0006052517,0.000013163859,0.00004275715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004145474,0.00008170788,0.000089053305,0.000028521614,0.00005206111,0.000033391316,0.00029395573,0.000070575275,0.000015763979],"category_scores_gemma":[0.000046424968,0.000053877524,0.0000058465334,0.000060167804,0.00003651872,0.00008171216,0.000114652976,0.00018305164,0.000023292443],"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":[0.000014411721,0.0001401273,0.11032699,0.00015264207,0.00019014505,0.00003362923,0.00047615118,0.20825617,0.00091589143,0.6119844,0.03490855,0.03260089],"study_design_scores_gemma":[0.00012806848,0.000024181487,0.0042163874,0.000017898865,0.0000052750456,0.0000016320315,0.00023891202,0.99067825,0.0000579414,0.00033075042,0.0042159874,0.00008471502],"about_ca_topic_score_codex":0.000020406966,"about_ca_topic_score_gemma":0.000024968962,"teacher_disagreement_score":0.78242207,"about_ca_system_score_codex":0.000036350837,"about_ca_system_score_gemma":0.000031887343,"threshold_uncertainty_score":0.21970616},"labels":[],"label_agreement":null},{"id":"W2187686889","doi":"","title":"Integrating Vision Derived Bearing Measurements with Differential GPS and UWB Ranges for Vehicle-to-vehicle Relative Navigation.","year":2013,"lang":"en","type":"article","venue":"Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bearing (navigation); Differential GPS; Global Positioning System; Computer science; Range (aeronautics); Differential (mechanical device); Computer vision; Remote sensing; Artificial intelligence; Geodesy; Engineering; Geology; Telecommunications; Aerospace engineering","score_opus":0.013896868519154257,"score_gpt":0.23976205642306972,"score_spread":0.22586518790391547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187686889","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":[0.9945311,0.00009521979,0.0014532808,0.0019069308,0.00041609677,0.0010566954,0.000019987743,0.00007900808,0.00044168142],"genre_scores_gemma":[0.9928787,0.000027415537,0.0069217472,0.000019698251,0.000030310137,0.000050199193,0.0000050494705,0.000027040809,0.000039846454],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998049,0.000024742225,0.0008341436,0.00025755266,0.0006531161,0.00018141538],"domain_scores_gemma":[0.9978764,0.00014463796,0.0008421921,0.00024879727,0.0008425503,0.000045399425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006120602,0.00022553494,0.0003439344,0.00010976645,0.0002118224,0.000029378754,0.0010606822,0.00018677572,0.0000045175675],"category_scores_gemma":[0.0005383338,0.00013202794,0.0001898553,0.00035793628,0.00047044575,0.0004556865,0.00051189173,0.0003654368,0.0000010209824],"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":[0.00010273505,0.00007106831,0.01211814,0.00023819135,0.00012333647,2.4085649e-8,0.000309427,0.0035090963,0.9707531,0.0035672754,0.00005533909,0.009152321],"study_design_scores_gemma":[0.0007895374,0.00013621818,0.08451777,0.0041112914,0.00008283593,0.0000036498666,0.00010782428,0.01305793,0.89006424,0.0067758826,0.00017248151,0.00018034058],"about_ca_topic_score_codex":0.00006998119,"about_ca_topic_score_gemma":0.0000067226756,"teacher_disagreement_score":0.080688804,"about_ca_system_score_codex":0.00008774738,"about_ca_system_score_gemma":0.000021572147,"threshold_uncertainty_score":0.53839433},"labels":[],"label_agreement":null},{"id":"W2203306535","doi":"10.1007/978-1-84628-974-3_17","title":"Navigation of Autonomous Mobile Robots - Invited Paper","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in control and information sciences","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Mobile robot navigation; Mobile robot; Robot; Artificial intelligence; Computer vision; Trajectory; Navigation system; Computer science; Task (project management); Collision avoidance; Position (finance); Robot control; Real-time computing; Human–computer interaction; Engineering; Collision; Systems engineering; Computer security","score_opus":0.008190048418655935,"score_gpt":0.2201638776230713,"score_spread":0.21197382920441538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2203306535","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":[0.0208312,0.010804058,0.49764356,0.0013570111,0.0011604531,0.0024291857,0.00019206636,0.0011941652,0.4643883],"genre_scores_gemma":[0.99845666,0.00023685466,0.00056450744,0.00061643124,0.000026020885,0.000011741024,0.000028371596,0.0000074038703,0.000052041414],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999083,0.000004927957,0.0004883725,0.00010235957,0.00016134998,0.00016002839],"domain_scores_gemma":[0.9995022,0.00015866826,0.00015289611,0.00011040402,0.00004958392,0.000026292064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036730742,0.00017107165,0.0002693537,0.0003583294,0.00006969728,0.000025239964,0.00014246792,0.00045390349,0.00004218172],"category_scores_gemma":[0.000031874133,0.00014568,0.000039123883,0.000109959365,0.00030341622,0.00079468894,0.000018210194,0.0003211728,0.000009857119],"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":[0.00002101535,0.0000045011734,0.00022063885,0.00013479014,0.00003412996,0.0000015057049,0.00083468243,0.26609623,0.00026838772,0.01717859,0.000026943391,0.7151786],"study_design_scores_gemma":[0.0029826267,0.0006947328,0.0036165828,0.0007218977,0.000097068485,0.00007125935,0.00007446948,0.7320607,0.0034098432,0.08471551,0.16996191,0.0015934162],"about_ca_topic_score_codex":0.000014643732,"about_ca_topic_score_gemma":0.00003031918,"teacher_disagreement_score":0.9776254,"about_ca_system_score_codex":0.00004268161,"about_ca_system_score_gemma":0.000028115008,"threshold_uncertainty_score":0.5940658},"labels":[],"label_agreement":null},{"id":"W2229807060","doi":"10.4271/2001-01-2654","title":"Synthetic Vision Databases for Runway Incursion Avoidance","year":2001,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Concordia University; University of Denver","keywords":"Runway; Computer science; Database; Artificial intelligence","score_opus":0.0115494744466143,"score_gpt":0.24794135209119356,"score_spread":0.23639187764457925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2229807060","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":[0.91352504,0.002312499,0.00016430899,0.0070792455,0.0008581722,0.0030720604,0.0003478641,0.019524839,0.053115964],"genre_scores_gemma":[0.98976016,0.0015943927,0.006281859,0.00080775894,0.00016124346,0.0006411178,0.0001171215,0.00020999764,0.00042635857],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9957782,0.0000866484,0.0010863584,0.0012348598,0.0005653898,0.001248526],"domain_scores_gemma":[0.9967432,0.0007546048,0.00015473166,0.0019309993,0.00008873595,0.00032775087],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007291098,0.00087943964,0.0009822013,0.00028832522,0.00062421436,0.000061812694,0.001231117,0.0010754884,0.00041943043],"category_scores_gemma":[0.0006577055,0.0007981405,0.00045125844,0.00076190784,0.001053347,0.00064612675,0.0003997395,0.0014886808,0.0002550022],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038199787,0.00030905986,0.00018963343,0.000099431236,0.0000471955,0.000053600445,0.000014863639,0.0002875495,0.9468942,0.022495471,0.0053325724,0.023894412],"study_design_scores_gemma":[0.0009528527,0.0011351854,0.6876601,0.0004141543,0.0000965519,0.00021382961,0.000059775695,0.0000056501126,0.0010847797,0.0037906172,0.30353144,0.0010550815],"about_ca_topic_score_codex":0.00001612914,"about_ca_topic_score_gemma":0.019242093,"teacher_disagreement_score":0.9458094,"about_ca_system_score_codex":0.0003729651,"about_ca_system_score_gemma":0.000057946592,"threshold_uncertainty_score":0.9994469},"labels":[],"label_agreement":null},{"id":"W2245257385","doi":"10.1109/ssci.2015.72","title":"A Three-Step Vehicle Detection Framework for Range Estimation Using a Single Camera","year":2015,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"York University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Vehicle tracking system; Tracking (education); Range (aeronautics); Track (disk drive); Filter (signal processing); Single camera; Kalman filter; Engineering","score_opus":0.03708494799540865,"score_gpt":0.25297166113497166,"score_spread":0.215886713139563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2245257385","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":[0.29143366,0.00008945745,0.70706964,0.000051986226,0.00016823832,0.00017729672,0.0000018401363,0.00079560897,0.0002122795],"genre_scores_gemma":[0.7804471,6.531611e-7,0.21941935,0.000028085431,0.000048343292,0.000025628318,0.0000017310252,0.000022302116,0.000006786799],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947184,0.000005410437,0.00014684073,0.00011915021,0.00006360808,0.00019313322],"domain_scores_gemma":[0.99967426,0.000050085382,0.000024602185,0.00016068143,0.000040163977,0.000050204726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012843219,0.00010212805,0.00011932084,0.00006472068,0.00009114011,0.000022774893,0.00007045126,0.00022883853,0.000011506106],"category_scores_gemma":[0.00007173233,0.000105124345,0.000038561706,0.00014522401,0.000035901397,0.00013704556,0.000014621791,0.00014209349,0.0000204028],"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":[0.00013821748,0.00010308349,0.0013556445,0.000111329246,0.00011530453,0.000004923876,0.0006753605,0.31022927,0.02129866,0.019682024,0.00028526835,0.6460009],"study_design_scores_gemma":[0.00033167156,0.000069152404,0.00015619906,0.00001319065,0.000017232205,0.0000073778483,0.00006350415,0.9647933,0.015273434,0.018771788,0.00037176698,0.00013134694],"about_ca_topic_score_codex":0.000060582137,"about_ca_topic_score_gemma":0.00018970718,"teacher_disagreement_score":0.6545641,"about_ca_system_score_codex":0.00016043367,"about_ca_system_score_gemma":0.000016873251,"threshold_uncertainty_score":0.42868465},"labels":[],"label_agreement":null},{"id":"W2253314556","doi":"","title":"A guidance-based motion-planning methodology for the docking of autonomous vehicles: Research Articles","year":2005,"lang":"en","type":"article","venue":"Journal of Robotic Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Novelty; Computer science; Guidance system; Control engineering; Simulation; Engineering; Motion planning; Artificial intelligence; Robot; Aerospace engineering","score_opus":0.1456785485247627,"score_gpt":0.3568812249252244,"score_spread":0.2112026764004617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2253314556","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":[0.17909855,0.009266031,0.80941707,0.0009863249,0.00070199225,0.00032430253,0.000001849296,0.00008340426,0.000120476936],"genre_scores_gemma":[0.9808859,0.00002397124,0.018644985,0.000017221217,0.00035041547,0.000019352969,2.6793083e-7,0.000024699208,0.000033135315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981948,0.0002685989,0.000846021,0.0001001224,0.00025046218,0.00033997616],"domain_scores_gemma":[0.9965784,0.0024946188,0.00028660797,0.00025460665,0.00033345056,0.00005233998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050974013,0.0001162728,0.00041565933,0.00029445894,0.00015675722,0.000024444602,0.0004049759,0.00016739128,0.000006366898],"category_scores_gemma":[0.00043906205,0.00008357731,0.00013834701,0.00026910604,0.00013990018,0.000108063934,0.000022240918,0.00044743155,0.0000049435043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035836827,0.000023681709,0.0021201426,0.00012233328,0.00012478387,0.0000060801412,0.00051035616,0.977616,0.0051348098,0.0013721396,0.00047933558,0.012454484],"study_design_scores_gemma":[0.0007851677,0.00019867533,0.0030703794,0.0003291902,0.00006778715,0.00016437226,0.0013965671,0.97943884,0.0116954055,0.00045066688,0.002287446,0.00011550752],"about_ca_topic_score_codex":0.00001496303,"about_ca_topic_score_gemma":0.000004010482,"teacher_disagreement_score":0.8017874,"about_ca_system_score_codex":0.00017799162,"about_ca_system_score_gemma":0.00009313466,"threshold_uncertainty_score":0.34081838},"labels":[],"label_agreement":null},{"id":"W2338506351","doi":"10.1049/iet-its.2014.0257","title":"Automated cyclist data collection under high density conditions","year":2016,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","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":"University of British Columbia","funders":"","keywords":"Data collection; Computer science; Transport engineering; Engineering; Statistics; Mathematics","score_opus":0.02465353091680207,"score_gpt":0.24363957525352814,"score_spread":0.21898604433672608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338506351","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":[0.6249593,0.00024901258,0.36208928,0.0003292253,0.00227567,0.00053711794,0.0006095876,0.0076953224,0.0012555317],"genre_scores_gemma":[0.9984536,0.00019755532,0.00008121588,0.000019008457,0.00006742648,0.000038485352,0.0003678928,0.000041555115,0.0007332546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871606,0.000028638757,0.00047285383,0.0003296219,0.00014957103,0.0003032336],"domain_scores_gemma":[0.99898183,0.00006565291,0.00005061282,0.00076594175,0.000052736705,0.00008324684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021418039,0.00020650541,0.0002862759,0.00012022624,0.00015500096,0.000012804046,0.0003831389,0.00028595573,0.00015472625],"category_scores_gemma":[0.00000636711,0.00016603175,0.000054281765,0.0002335621,0.000111563815,0.00020258322,0.00001820473,0.00015628847,0.00037485364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004694904,0.0015634588,0.16251954,0.002240027,0.0066596386,0.000936131,0.0023294727,0.22736302,0.16793264,0.19651642,0.2157645,0.015705656],"study_design_scores_gemma":[0.0031347137,0.0003254536,0.3328214,0.0014135787,0.00077263184,0.00077743514,0.001175989,0.42483863,0.110227644,0.0036715493,0.117262065,0.003578891],"about_ca_topic_score_codex":0.0002997743,"about_ca_topic_score_gemma":0.00049737643,"teacher_disagreement_score":0.37349433,"about_ca_system_score_codex":0.00018420996,"about_ca_system_score_gemma":0.00003649007,"threshold_uncertainty_score":0.67705786},"labels":[],"label_agreement":null},{"id":"W2339444104","doi":"10.1109/percom.2016.7456505","title":"SafeCam: Analyzing intersection-related driver behaviors using multi-sensor smartphones","year":2016,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Intersection (aeronautics); Computer science; Phone; Latency (audio); Real-time computing; Inertial measurement unit; Driving simulator; Simulation; Computer security; Computer vision; Engineering; Transport engineering; Telecommunications","score_opus":0.01354543281191072,"score_gpt":0.23031854113646566,"score_spread":0.21677310832455493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339444104","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":[0.91558814,0.00004547479,0.08175715,0.000059708567,0.00042782148,0.00008190666,0.0000035512492,0.0013741347,0.000662111],"genre_scores_gemma":[0.99501777,0.000018962237,0.0038971675,0.000010044096,0.000018401397,0.000004384643,0.0000012965552,0.000033647204,0.0009983303],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9992196,0.000015698852,0.00025461614,0.00019006366,0.00005228434,0.0002677143],"domain_scores_gemma":[0.9996401,0.000032881428,0.000029931585,0.00021930438,0.000026564569,0.000051238952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000079584366,0.00015982868,0.00016579531,0.0001770344,0.00009726425,0.000008227687,0.00011421513,0.00022765694,0.0005861149],"category_scores_gemma":[0.00001211796,0.000118518714,0.00008057494,0.0001907814,0.00009333432,0.000170319,0.000038433624,0.00015706556,0.00024811825],"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":[0.000015687783,0.000073706746,0.18307523,0.000017087883,0.00026228305,0.000044109973,0.00044557784,0.005027222,0.764407,0.00084114634,0.00013966073,0.045651324],"study_design_scores_gemma":[0.004932451,0.0001315103,0.37618518,0.0003118595,0.00035056385,0.00028154472,0.00079808326,0.25552812,0.35652858,0.0005774854,0.0021348589,0.002239769],"about_ca_topic_score_codex":0.00005007548,"about_ca_topic_score_gemma":0.00007840352,"teacher_disagreement_score":0.40787837,"about_ca_system_score_codex":0.00015722039,"about_ca_system_score_gemma":0.000009375972,"threshold_uncertainty_score":0.6417547},"labels":[],"label_agreement":null},{"id":"W2371362393","doi":"10.1139/tcsme-2012-0007","title":"REAL-TIME MARK-ON-WINDSHIELD WARNING SYSTEM FOR INTELLIGENT VEHICLES","year":2012,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Windshield; Intersection (aeronautics); Computer science; Computer vision; Obstacle; Point (geometry); Line (geometry); Sight; Coordinate system; Construct (python library); Artificial intelligence; Simulation; Engineering; Aerospace engineering; Mathematics; Geography; Physics","score_opus":0.009346769465313425,"score_gpt":0.19435162976516154,"score_spread":0.1850048602998481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2371362393","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":[0.077677704,0.00020604572,0.9159941,0.0006138152,0.0018573033,0.0014522119,0.0005511669,0.0012177401,0.00042989533],"genre_scores_gemma":[0.98990273,0.000016616823,0.009669369,0.00002155901,0.0000800217,0.00015058536,0.0000065600047,0.000059962913,0.00009259819],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903214,0.0000057908815,0.00025367446,0.00012010712,0.00008320981,0.00050506793],"domain_scores_gemma":[0.9992864,0.00019589305,0.000029186871,0.00026627808,0.000029109815,0.0001931283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032783847,0.00016898755,0.00021928022,0.000060717157,0.00025158134,0.000008642399,0.00026210252,0.0003098792,0.000015099646],"category_scores_gemma":[0.000020260863,0.00015863102,0.00053759024,0.00016551628,0.000026699643,0.00007261249,0.0000048108222,0.00028194863,0.0000054282013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053801148,0.00006726659,0.000029970835,0.0016160663,0.001352823,4.3258942e-7,0.0014601359,0.8407005,0.07442018,0.064247355,0.0015204735,0.014531012],"study_design_scores_gemma":[0.0004883071,0.00009752622,0.000075097494,0.00019338929,0.00022019858,0.000011746545,0.0004450086,0.8017313,0.18822446,0.00012418433,0.007941645,0.00044714898],"about_ca_topic_score_codex":0.0003997043,"about_ca_topic_score_gemma":0.00040247946,"teacher_disagreement_score":0.912225,"about_ca_system_score_codex":0.0005674981,"about_ca_system_score_gemma":0.000052683106,"threshold_uncertainty_score":0.64687854},"labels":[],"label_agreement":null},{"id":"W2417591941","doi":"10.1109/icra.2016.7487506","title":"Learning time series models for pedestrian motion prediction","year":2016,"lang":"en","type":"article","venue":"","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":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Motion (physics); Robot; Pedestrian; Series (stratigraphy); Wheelchair; Mobile robot; Machine learning; Time series; Object (grammar); Computer vision; Engineering","score_opus":0.007082218286883824,"score_gpt":0.17575543183597317,"score_spread":0.16867321354908935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2417591941","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":[0.07452353,0.000020174002,0.91355217,0.0003805752,0.00008607071,0.00014040912,0.000009801577,0.00245435,0.008832914],"genre_scores_gemma":[0.99237674,0.000036003963,0.0018358111,0.0000040639356,0.000041356514,0.000027871753,0.0000063337525,0.000015588934,0.005656224],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966633,0.000005239141,0.00009417221,0.00008261394,0.000027730437,0.00012388191],"domain_scores_gemma":[0.9998591,0.000023191566,0.0000103420725,0.000074588126,0.000014824763,0.00001795224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007622752,0.00006132381,0.00006505618,0.000036516696,0.00006354746,0.000004615535,0.000042266496,0.000113297094,0.00007744739],"category_scores_gemma":[0.000013438198,0.000045400902,0.000025731219,0.000032355383,0.000020541063,0.00027436326,0.00000803516,0.00005151696,0.000070837],"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":[0.00009559655,0.000031245574,0.0022526528,0.00008631219,0.0001265296,0.0000018395849,0.0003151672,0.27479705,0.06786541,0.051123697,0.0031996781,0.6001048],"study_design_scores_gemma":[0.0006804797,0.000182875,0.0010072053,0.000020336727,0.000016154223,0.000011460843,0.00004743524,0.9403449,0.025392076,0.02400835,0.00810027,0.00018841801],"about_ca_topic_score_codex":9.428758e-7,"about_ca_topic_score_gemma":0.0000017990575,"teacher_disagreement_score":0.91785324,"about_ca_system_score_codex":0.0000353644,"about_ca_system_score_gemma":0.0000042750885,"threshold_uncertainty_score":0.1851395},"labels":[],"label_agreement":null},{"id":"W2462253356","doi":"10.1061/9780784479896.030","title":"A Driver Warning System for the Android System","year":2016,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Ministry of Transportation of Ontario","funders":"","keywords":"Android (operating system); Computer science; Kalman filter; Real-time computing; Frame rate; Mobile device; Warning system; Computer vision; Artificial intelligence; Operating system","score_opus":0.005421271055533074,"score_gpt":0.17169681370231,"score_spread":0.1662755426467769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2462253356","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":[0.08046488,0.00040028695,0.8979768,0.00040984145,0.0005641709,0.00045955615,0.000010619153,0.0046007745,0.01511303],"genre_scores_gemma":[0.9985735,0.000008307632,0.00042491802,0.000007530358,0.00005227194,0.000078187186,3.0039652e-7,0.000017771381,0.00083725],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995595,0.0000069168505,0.00012303444,0.00009218853,0.000039632054,0.00017870955],"domain_scores_gemma":[0.99957144,0.00016585561,0.000015887626,0.000210553,0.000016348822,0.000019908352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015057196,0.0000798818,0.00010299222,0.000025443962,0.000113307535,0.000006654125,0.0001518586,0.00009465475,0.000012665143],"category_scores_gemma":[0.000007401244,0.000038101596,0.000045864563,0.000043878706,0.000029533827,0.000048339505,0.000020270678,0.0000531594,0.00012015762],"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":[0.000059880025,0.000016597454,0.005435725,0.001255083,0.0006497707,0.000026039446,0.0006313287,0.004965892,0.035957456,0.7004755,0.010378163,0.24014856],"study_design_scores_gemma":[0.00397836,0.00016803171,0.007405049,0.0008681642,0.00021461048,0.00035365744,0.00498763,0.7047939,0.1113474,0.0002855639,0.1644837,0.0011139679],"about_ca_topic_score_codex":0.000002649724,"about_ca_topic_score_gemma":0.0000039842166,"teacher_disagreement_score":0.9181086,"about_ca_system_score_codex":0.00010558406,"about_ca_system_score_gemma":0.000005739331,"threshold_uncertainty_score":0.1553738},"labels":[],"label_agreement":null},{"id":"W2474392009","doi":"10.1155/2017/3080859","title":"A Framework for Estimating Long Term Driver Behavior","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Division of Electrical, Communications and Cyber Systems; National Science Foundation","keywords":"Term (time); Trajectory; State (computer science); Markov chain; Hidden Markov model; Advanced driver assistance systems; Estimation; Hybrid system","score_opus":0.012469858809608329,"score_gpt":0.28581139111908577,"score_spread":0.27334153230947744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2474392009","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":[0.5876856,0.000050963215,0.41169903,0.000038293125,0.0003951371,0.000078003446,0.0000045579413,0.00003866232,0.000009760292],"genre_scores_gemma":[0.71924967,0.000021857022,0.2806182,0.000004661948,0.00007630865,0.000008867405,0.0000033052215,0.000012934001,0.0000041957264],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994674,0.0000019557272,0.00029034744,0.000056981455,0.000070164526,0.00011317428],"domain_scores_gemma":[0.9994739,0.00003414613,0.0002566468,0.0001352521,0.00006710656,0.00003292604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007897134,0.00007856243,0.00014975118,0.00004803312,0.00015437568,0.000016020409,0.00015968335,0.0001049919,0.000007716043],"category_scores_gemma":[0.000033704928,0.00007732965,0.00008420725,0.000018765497,0.000034903383,0.000428018,0.0000010339763,0.00021859324,9.852665e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014377608,0.0000788837,0.11105597,0.00019498581,0.00012226355,0.00013991966,0.0016631605,0.24618849,0.012613113,0.0042316727,0.000015507247,0.62355226],"study_design_scores_gemma":[0.0006400987,0.000078970785,0.98855907,0.00014699684,0.00008769306,0.000011917713,0.000046240526,0.0013131964,0.0044817408,0.0044810344,0.000045621266,0.000107398184],"about_ca_topic_score_codex":2.7346417e-7,"about_ca_topic_score_gemma":0.000008690352,"teacher_disagreement_score":0.8775031,"about_ca_system_score_codex":0.000028516173,"about_ca_system_score_gemma":0.000011509846,"threshold_uncertainty_score":0.31534114},"labels":[],"label_agreement":null},{"id":"W2504521433","doi":"10.2495/safe-v6-n2-383-393","title":"Wide-area based traffic situation detection at an ungated level crossing","year":2016,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Overtaking; Computer science; Real-time computing; Probabilistic logic; Range (aeronautics); Situated; Event (particle physics); Artificial intelligence; Transport engineering; Simulation; Data mining; Engineering","score_opus":0.009792397426954693,"score_gpt":0.20739321853456621,"score_spread":0.1976008211076115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2504521433","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":[0.7385836,0.00012688668,0.26030427,0.0002984482,0.00046265047,0.000033350163,0.000023910174,0.00014284567,0.000023993514],"genre_scores_gemma":[0.99907136,0.00012529759,0.0006598789,0.00002350827,0.00008439669,0.0000010650571,0.000006085456,0.000019595815,0.000008831833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991416,0.000014638892,0.00039028414,0.00010363143,0.0001997236,0.00015016829],"domain_scores_gemma":[0.9994748,0.00012589527,0.00009376857,0.00007850291,0.0001465942,0.000080386395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028904589,0.00013616518,0.00016105776,0.0002245842,0.00008892844,0.000039508916,0.00016834946,0.00014441238,0.000033931006],"category_scores_gemma":[0.000087324166,0.00011492295,0.00006250008,0.000077287135,0.000051958545,0.00048448963,0.000023024775,0.00020799611,0.000002974742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049458956,0.00006891861,0.0015618823,0.000042256896,0.0002967774,0.00009366813,0.0011715674,0.66007644,0.19100158,0.00040919514,0.000021103562,0.14476204],"study_design_scores_gemma":[0.0022633115,0.00014370124,0.03320077,0.00027830363,0.000035167242,0.00038120456,0.00004883394,0.8919061,0.06681719,0.00029597737,0.0042579216,0.0003715489],"about_ca_topic_score_codex":0.0000017882179,"about_ca_topic_score_gemma":0.00003159804,"teacher_disagreement_score":0.2604877,"about_ca_system_score_codex":0.00029443076,"about_ca_system_score_gemma":0.000024858278,"threshold_uncertainty_score":0.46864218},"labels":[],"label_agreement":null},{"id":"W2509939652","doi":"10.1109/ivs.2016.7535444","title":"Manoeuvre segmentation using smartphone sensors","year":2016,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Principal component analysis; Artificial intelligence; Computer science; Support vector machine; Classifier (UML); Pattern recognition (psychology); Sliding window protocol; Segmentation; Dimensionality reduction; Mobile phone; Feature extraction; Computer vision; Window (computing)","score_opus":0.010112746120936933,"score_gpt":0.20491617810393,"score_spread":0.19480343198299307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509939652","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":[0.9118794,0.000022858454,0.081724904,0.00014333485,0.00012158885,0.00004487827,0.0000019118656,0.000818391,0.0052427053],"genre_scores_gemma":[0.99414724,0.00002032987,0.0049274946,0.000022791195,0.000021732223,0.0000023139553,6.7939277e-7,0.000012878862,0.00084456516],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996725,0.000005079006,0.00008956497,0.00007221244,0.000035424782,0.00012524442],"domain_scores_gemma":[0.9998448,0.0000149355465,0.000008508407,0.00010505414,0.0000071362983,0.000019562469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003797711,0.00006262187,0.000058792186,0.00004100259,0.000032322325,0.0000029426167,0.00004288204,0.000074420066,0.0003618456],"category_scores_gemma":[0.0000027819965,0.000043668104,0.00001792998,0.000055829652,0.000025274607,0.00008209104,0.000010582396,0.000036812533,0.00022360274],"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":[0.000008726087,0.000014419493,0.010513992,0.000018605795,0.000063288266,0.00001299586,0.0001344056,0.003989487,0.8429641,0.0036045457,0.0010339576,0.13764149],"study_design_scores_gemma":[0.0011578753,0.000035001507,0.019334042,0.000039457973,0.000025951489,0.000058411388,0.00021492619,0.048835132,0.9229103,0.0020106386,0.0048701107,0.0005082108],"about_ca_topic_score_codex":0.0000032298965,"about_ca_topic_score_gemma":0.000004598441,"teacher_disagreement_score":0.13713329,"about_ca_system_score_codex":0.000057728863,"about_ca_system_score_gemma":0.0000037067962,"threshold_uncertainty_score":0.39619553},"labels":[],"label_agreement":null},{"id":"W2524923808","doi":"10.1109/aim.2016.7577010","title":"Geometric path tracking algorithm for autonomous driving in pedestrian environment","year":2016,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nutrasource","funders":"National Research Foundation Singapore; National University of Singapore","keywords":"Path (computing); Kinematics; Position (finance); Tracking (education); Computer science; Pedestrian; Point (geometry); Control theory (sociology); Computer vision; Track (disk drive); Controller (irrigation); Algorithm; Orientation (vector space); Motion planning; Property (philosophy); Artificial intelligence; Control (management); Mathematics; Engineering; Robot","score_opus":0.008823423497191342,"score_gpt":0.19535522098482275,"score_spread":0.1865317974876314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2524923808","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":[0.1330499,0.00017936273,0.8642292,0.00019115442,0.000118062206,0.0002754724,0.000009754633,0.00064319756,0.0013038861],"genre_scores_gemma":[0.9782788,0.00015452491,0.020980287,0.000014060048,0.00003984715,0.000069535396,0.0000020927946,0.00003141278,0.00042943764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910027,0.0000080254895,0.00025485115,0.0002048137,0.000061748884,0.00037030835],"domain_scores_gemma":[0.99958104,0.00014394564,0.000024038909,0.00019934315,0.0000048316465,0.000046781934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021155043,0.00014212873,0.00017586829,0.0002736699,0.00004605558,0.000009037332,0.00015126004,0.00017120526,0.00018053163],"category_scores_gemma":[0.0000237434,0.00011097214,0.00005508947,0.00016249999,0.000033429224,0.0001274619,0.000030865805,0.000106089814,0.00006871865],"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":[0.0000010476912,0.000022377857,0.0037445182,0.000007062017,0.00001332319,0.0000060412845,0.00003239264,0.00053300493,0.0008721993,0.0002944573,0.000056993587,0.9944166],"study_design_scores_gemma":[0.00842982,0.00053584564,0.32946467,0.00021274552,0.000070439804,0.000071043614,0.00038219066,0.50311416,0.05657031,0.0071034282,0.09153397,0.002511363],"about_ca_topic_score_codex":0.0000074752465,"about_ca_topic_score_gemma":0.0000131659845,"teacher_disagreement_score":0.9919052,"about_ca_system_score_codex":0.00024159254,"about_ca_system_score_gemma":0.000012254965,"threshold_uncertainty_score":0.45253128},"labels":[],"label_agreement":null},{"id":"W2547966251","doi":"10.1109/ccece.2016.7726750","title":"INS/GPS localization for reliable cooperative driving","year":2016,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Else Kröner-Fresenius-Stiftung; Sharif University of Technology","keywords":"Global Positioning System; Robustness (evolution); Computer science; Real-time computing; Inertial navigation system; Multipath propagation; Inertial frame of reference; Computer network; Telecommunications","score_opus":0.00645948226983629,"score_gpt":0.2016231726612223,"score_spread":0.195163690391386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2547966251","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":[0.026173217,0.000033630648,0.9650683,0.00029256876,0.00010114799,0.00013749908,0.0000026810064,0.00089530274,0.0072956495],"genre_scores_gemma":[0.99579173,0.00003944513,0.0021656787,0.000057397647,0.000022639288,0.00003370418,0.0000023882644,0.000015970327,0.0018710485],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996527,0.000002927283,0.00009925635,0.00008639288,0.000024275303,0.00013449225],"domain_scores_gemma":[0.99979365,0.00004255071,0.0000082763045,0.00010540384,0.000031194057,0.00001889768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000059428367,0.000063402,0.0000734776,0.00003136561,0.000062359424,0.000004803365,0.000060729013,0.00009638876,0.000121442536],"category_scores_gemma":[0.000029168183,0.000043006658,0.000016739063,0.00006515045,0.000027362506,0.00010899966,0.000012346392,0.000033045057,0.000072136645],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035948127,0.000066030836,0.018007873,0.00009077085,0.0001797002,0.000003840908,0.0004398263,0.07008348,0.06869427,0.60715306,0.059719976,0.17552525],"study_design_scores_gemma":[0.0013878427,0.00013827987,0.0014976135,0.00008190181,0.000017789907,0.000005431414,0.00010210412,0.42748338,0.31624556,0.0075608892,0.2449844,0.0004948121],"about_ca_topic_score_codex":0.0000014176222,"about_ca_topic_score_gemma":0.000019990115,"teacher_disagreement_score":0.9696185,"about_ca_system_score_codex":0.000042879,"about_ca_system_score_gemma":0.000008709214,"threshold_uncertainty_score":0.17537606},"labels":[],"label_agreement":null},{"id":"W2552387360","doi":"","title":"A STUDY OF PRIVACY REQUIREMENTS FOR SMART TOYS","year":2016,"lang":"en","type":"article","venue":"Pacific Asia Conference on Information Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Internet privacy; Computer security; Information privacy; Privacy software","score_opus":0.036895325880237854,"score_gpt":0.25429914178519725,"score_spread":0.21740381590495939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552387360","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":[0.6232507,0.000022828308,0.20079221,0.00020141703,0.0013833044,0.0037166371,0.00014064477,0.0011863301,0.16930592],"genre_scores_gemma":[0.9994486,0.000008159066,0.00005864303,0.000002589461,0.0000132142995,0.00027346823,0.000007983632,0.000008159159,0.00017919234],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9990122,0.000019911557,0.0005559715,0.000082577535,0.00015799385,0.00017136753],"domain_scores_gemma":[0.9993126,0.000045253542,0.00015352647,0.00032123018,0.00013265476,0.00003476432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002614959,0.0001242824,0.0002132151,0.00014956098,0.000054092838,0.000023327791,0.00018233835,0.00010586809,0.000016167201],"category_scores_gemma":[0.000033665103,0.00009044801,0.000030310448,0.00009171358,0.000028072503,0.0004867562,0.000018279117,0.000056560177,0.00013936029],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005858174,0.00070725713,0.035234045,0.0017408652,0.0010083142,0.0000039220354,0.050512824,0.0035330032,0.0074216705,0.5070167,0.009703607,0.38253194],"study_design_scores_gemma":[0.036853828,0.009684508,0.04302973,0.0034623046,0.00027395412,0.000080104284,0.33387375,0.22452055,0.029537974,0.0039748503,0.31022748,0.004480949],"about_ca_topic_score_codex":0.000004015805,"about_ca_topic_score_gemma":0.0000011797272,"teacher_disagreement_score":0.50304186,"about_ca_system_score_codex":0.00006750886,"about_ca_system_score_gemma":0.000025673291,"threshold_uncertainty_score":0.36883628},"labels":[],"label_agreement":null},{"id":"W2556398113","doi":"10.1109/tvt.2016.2631604","title":"Recent Trends in Driver Safety Monitoring Systems: State of the Art and Challenges","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":157,"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":"Advanced driver assistance systems; Transport engineering; Context (archaeology); Engineering; Intelligent transportation system; Perception; Risk analysis (engineering); Computer science; Computer security; Systems engineering; Business","score_opus":0.012478919667857021,"score_gpt":0.20497506118963288,"score_spread":0.19249614152177585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2556398113","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":[0.9455607,0.0067090667,0.03913374,0.0059751314,0.0008523328,0.00028850182,0.00003441818,0.0010411813,0.00040491487],"genre_scores_gemma":[0.97685534,0.022844601,0.00008443337,0.0000028608827,0.0000056876333,0.00004700334,1.3310658e-7,0.000022265569,0.0001376921],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99913627,0.00003389508,0.00028488727,0.00021077691,0.00008986556,0.00024430777],"domain_scores_gemma":[0.9994466,0.00004112704,0.00004176628,0.00042076956,0.00002652631,0.0000232611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012609687,0.00015747112,0.00023936442,0.00048233455,0.00006374274,0.0000025166905,0.00020412746,0.000312409,0.0000106855],"category_scores_gemma":[0.0000033882368,0.00010725121,0.000047596273,0.00043661107,0.00022761643,0.00006162535,0.0000041899566,0.00035321075,0.000011118439],"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":[0.000018215489,0.000045963516,0.0004301717,0.00003072302,0.00007491368,0.000007714947,0.000083264895,0.015969211,0.011776566,0.00046261578,0.0000061708947,0.9710945],"study_design_scores_gemma":[0.003564618,0.00040043957,0.03960809,0.0010362423,0.00011590276,0.00019794253,0.0005274192,0.009210097,0.877793,0.0018968543,0.064660646,0.0009887269],"about_ca_topic_score_codex":0.0000030432282,"about_ca_topic_score_gemma":0.000060575912,"teacher_disagreement_score":0.97010577,"about_ca_system_score_codex":0.00014225452,"about_ca_system_score_gemma":0.000008998288,"threshold_uncertainty_score":0.43735775},"labels":[],"label_agreement":null},{"id":"W2557595357","doi":"","title":"2006 IEEE 64th Vehicular Technology Conference (VTC-2006 Fall) Montreal, QC, Canada","year":2006,"lang":"en","type":"article","venue":"Bristol Research (University of Bristol)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.011572205943415428,"score_gpt":0.20008607937871115,"score_spread":0.18851387343529571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2557595357","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":[0.9642349,0.001764746,0.006136553,0.00195058,0.00014555093,0.000425021,0.0002664308,0.0007041944,0.024372015],"genre_scores_gemma":[0.9951187,0.00020246959,0.0006811402,0.000010609791,0.000030762843,0.0000021862859,0.000039137554,0.000033774348,0.0038812198],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976922,0.000094508716,0.00026831232,0.00046072085,0.0006158664,0.0008683401],"domain_scores_gemma":[0.99851584,0.00009112575,0.00009046955,0.0007164757,0.0004378221,0.00014828949],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000391999,0.00024858164,0.0004786229,0.000633781,0.0004900909,0.000018111326,0.0011372332,0.0004903725,0.000088140216],"category_scores_gemma":[0.000038579037,0.00034135117,0.00009547856,0.0010644613,0.0010111753,0.00018018812,0.00020944791,0.001034587,0.00008107674],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022910554,0.00053172366,0.022551734,0.00049521064,0.0004509056,0.00386288,0.00034710692,0.018867869,0.016851034,0.013808167,0.8643636,0.057640627],"study_design_scores_gemma":[0.005144426,0.0005525672,0.087406814,0.00031546605,0.0001847175,0.00026635826,0.0072989874,0.105877124,0.013187806,0.013209118,0.76401633,0.0025402969],"about_ca_topic_score_codex":0.7296022,"about_ca_topic_score_gemma":0.883704,"teacher_disagreement_score":0.1541018,"about_ca_system_score_codex":0.00093431375,"about_ca_system_score_gemma":0.0008008295,"threshold_uncertainty_score":0.99990386},"labels":[],"label_agreement":null},{"id":"W2583389971","doi":"10.1587/transfun.e100.a.491","title":"Driver Behavior Assessment in Case of Critical Driving Situations","year":2017,"lang":"en","type":"article","venue":"IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences","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":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; CarSim; Sensor fusion; Inference; Fuzzy logic; Data mining; Simulation; Machine learning; Artificial intelligence","score_opus":0.025154724315853475,"score_gpt":0.32708063921844144,"score_spread":0.30192591490258797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2583389971","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":[0.8495773,0.0003970401,0.14828418,0.00084871415,0.00005939795,0.00014909921,0.000012723264,0.000047162284,0.00062437536],"genre_scores_gemma":[0.9760657,0.0006911165,0.023189506,0.000008947246,0.0000020761122,0.000033652796,9.169871e-7,0.000004718366,0.0000033678693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934953,0.00003431957,0.00025358496,0.0001268425,0.00007758735,0.0001581091],"domain_scores_gemma":[0.9990137,0.00019731701,0.00006340317,0.0006652453,0.000032090815,0.000028207118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025010356,0.00008369468,0.00014550364,0.00015604895,0.0007352814,0.00004471368,0.00059106894,0.000056642286,0.000011642489],"category_scores_gemma":[0.0000028793736,0.00008854609,0.00003756984,0.00012167972,0.00095733645,0.0002638333,0.00003464735,0.00026514396,5.7324695e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007955297,0.0027275165,0.019005464,0.00010334038,0.0001371475,0.000025132671,0.0020332972,0.023159254,0.0079862,0.21829236,0.000011434447,0.7265109],"study_design_scores_gemma":[0.00067779096,0.0008692886,0.059229016,0.00014053138,0.00009860102,0.00020948864,0.0009227001,0.9270222,0.0066246474,0.003691846,0.00014037975,0.0003734712],"about_ca_topic_score_codex":0.00007189132,"about_ca_topic_score_gemma":0.001022839,"teacher_disagreement_score":0.903863,"about_ca_system_score_codex":0.000050915263,"about_ca_system_score_gemma":0.000055913104,"threshold_uncertainty_score":0.5655264},"labels":[],"label_agreement":null},{"id":"W2588681948","doi":"","title":"Deriving Bearing Measurements from Video Images using Haar-like Features for Vehicle-to-Vehicle Navigation","year":2014,"lang":"en","type":"article","venue":"Proceedings of the 27th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2014)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bearing (navigation); Global Positioning System; Differential GPS; Computer vision; Computer science; Artificial intelligence; Track (disk drive); Differential (mechanical device); Remote sensing; Engineering; Geography; Telecommunications","score_opus":0.017712737359218123,"score_gpt":0.255572179689019,"score_spread":0.23785944232980089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588681948","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":[0.99377596,0.00012448154,0.0025256013,0.0009442802,0.0012320888,0.00065904594,0.000048655576,0.00011460148,0.0005752656],"genre_scores_gemma":[0.9874153,0.000018390381,0.012345916,0.000043676882,0.00007824991,0.000018470158,0.000008081608,0.00003543824,0.000036455],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976304,0.000033095963,0.000994904,0.00030495072,0.0008163948,0.0002202196],"domain_scores_gemma":[0.9976511,0.00019025689,0.0010157947,0.0003905311,0.000706268,0.000046028672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013114321,0.00024347936,0.00038490567,0.00012569827,0.00023646357,0.000030156856,0.0017721516,0.00023711166,0.0000031073243],"category_scores_gemma":[0.0011249343,0.0001670591,0.0003366022,0.0003802629,0.00035319795,0.00034817992,0.0006716405,0.0003754974,0.0000012309148],"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":[0.000069094414,0.000056035933,0.017581671,0.0001987688,0.00009274203,2.2848347e-8,0.00013690794,0.019315507,0.95417356,0.0016608363,0.000114222006,0.00660062],"study_design_scores_gemma":[0.0004588687,0.000043199114,0.06720924,0.0030513355,0.0000811398,0.0000029048304,0.00003226904,0.013795406,0.9054953,0.009165701,0.00049125514,0.00017342165],"about_ca_topic_score_codex":0.000100815116,"about_ca_topic_score_gemma":0.000005368665,"teacher_disagreement_score":0.049627565,"about_ca_system_score_codex":0.00015647644,"about_ca_system_score_gemma":0.00002866162,"threshold_uncertainty_score":0.68124723},"labels":[],"label_agreement":null},{"id":"W2590925981","doi":"10.4271/2017-01-0043","title":"Real-Time Robust Lane Marking Detection and Tracking for Degraded Lane Markings","year":2017,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","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":"University of Waterloo","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Tracking (education)","score_opus":0.013652341891656991,"score_gpt":0.22664935231073113,"score_spread":0.21299701041907415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2590925981","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":[0.92525554,0.00043112203,0.000048411708,0.0030597788,0.0004664075,0.0018163691,0.00009811942,0.010914729,0.057909507],"genre_scores_gemma":[0.98945695,0.0008141296,0.008440461,0.0001651643,0.00018546126,0.00031169588,0.000031240645,0.00018860864,0.0004063017],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9967821,0.00005966446,0.00081085454,0.0009791967,0.00036467728,0.0010034678],"domain_scores_gemma":[0.99748564,0.00045712158,0.00025262198,0.0014858342,0.00006737898,0.0002513775],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007977703,0.0007358514,0.0009260549,0.00021932954,0.0012211845,0.000218424,0.001025847,0.0013815446,0.000111027526],"category_scores_gemma":[0.00059506745,0.00070669816,0.00030314646,0.00020659572,0.0009583685,0.0005786545,0.0003346568,0.0012948867,0.00004553443],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002610349,0.000067187895,0.00029468816,0.000105882595,0.000071669434,0.000036404577,0.000017987322,0.00004035955,0.9561398,0.0039150757,0.00041672823,0.038633205],"study_design_scores_gemma":[0.0009919496,0.0005309284,0.9718672,0.00022014855,0.00011801568,0.00022442744,0.000036190475,0.000009779995,0.0011846235,0.0028516676,0.021126984,0.0008380816],"about_ca_topic_score_codex":0.000049291924,"about_ca_topic_score_gemma":0.028435476,"teacher_disagreement_score":0.9715725,"about_ca_system_score_codex":0.00027712758,"about_ca_system_score_gemma":0.000031580752,"threshold_uncertainty_score":0.9999149},"labels":[],"label_agreement":null},{"id":"W2592158934","doi":"10.1109/fit.2016.063","title":"Analysis of Positioning Uncertainty in Vehicular Environment","year":2016,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Global Positioning System; Computer science; Component (thermodynamics); Wireless; Range (aeronautics); Real-time computing; Position (finance); Automotive industry; Collision avoidance; Hybrid positioning system; Collision; Positioning system; Telecommunications; Engineering; Computer security; Aerospace engineering","score_opus":0.003804733725727216,"score_gpt":0.17632168951395372,"score_spread":0.1725169557882265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592158934","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":[0.9711147,0.000051205512,0.026734639,0.000119622826,0.000008094997,0.000027848953,0.0000034573864,0.00009737393,0.0018430463],"genre_scores_gemma":[0.9995869,0.00004712098,0.0002908818,0.000008484161,0.0000016966495,0.0000041160392,0.0000020377622,0.0000036064646,0.000055151224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9996758,0.00000656296,0.00012633683,0.00006692867,0.00003735807,0.00008699325],"domain_scores_gemma":[0.99982643,0.00002398745,0.00001057039,0.000125203,0.0000024244725,0.000011382834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006919333,0.000044046912,0.00011592512,0.00015668652,0.000008331951,6.9458673e-7,0.00005223923,0.000060080685,0.00034167504],"category_scores_gemma":[0.0000026014025,0.000031348056,0.000042524953,0.00016759298,0.000029954424,0.000028017332,0.000012497893,0.000034475706,0.00001725544],"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":[0.000004808736,0.00002407966,0.07774366,0.0000051840007,0.00047305855,0.000005782504,0.00009299367,0.8523477,0.03716818,0.0059888884,0.00001258863,0.026133107],"study_design_scores_gemma":[0.00058492005,0.000036257024,0.5584875,0.000027488928,0.00025958367,0.0000012605457,0.000093807925,0.40609092,0.032282144,0.0010715908,0.0008008401,0.0002637156],"about_ca_topic_score_codex":0.000015977543,"about_ca_topic_score_gemma":0.000028594062,"teacher_disagreement_score":0.48074383,"about_ca_system_score_codex":0.00006198178,"about_ca_system_score_gemma":0.0000019224817,"threshold_uncertainty_score":0.3741102},"labels":[],"label_agreement":null},{"id":"W2600344422","doi":"10.15353/vsnl.v1i1.61","title":"Straightening Curved Traffic Lanes in Video","year":2015,"lang":"en","type":"article","venue":"Vision Letters","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Interpolation (computer graphics); Tracking (education); Computer science; Simple (philosophy); Computer vision; Intelligent transportation system; Artificial intelligence; Computer graphics (images); Motion (physics); Engineering; Transport engineering","score_opus":0.01185351853370384,"score_gpt":0.21909711194443965,"score_spread":0.2072435934107358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600344422","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":[0.9953909,0.00014357334,0.0013681116,0.0015949861,0.00017813554,0.000058513047,0.0000011025758,0.00061761204,0.00064706546],"genre_scores_gemma":[0.9988713,0.000006996148,0.00077822834,0.00026914122,0.000029773279,0.0000047863914,0.0000043327595,0.00001587177,0.000019541934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945396,0.00001794039,0.00014741541,0.00011591362,0.000077028926,0.00018771992],"domain_scores_gemma":[0.99977475,0.000025165704,0.000012602843,0.00013662642,0.0000056708436,0.000045188117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000165818,0.000091600086,0.000115063965,0.00012694235,0.000018986619,0.000010044462,0.00011856269,0.00009209035,0.000022741133],"category_scores_gemma":[0.00001247767,0.00008787874,0.000023544051,0.00014946096,0.000031794425,0.00010093312,0.000016231756,0.00020366423,0.00012467953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004293285,0.000059257138,0.006968433,0.000046100333,0.00004090211,0.00027908833,0.0021128904,0.740436,0.05096948,0.00046884624,0.03921159,0.15936445],"study_design_scores_gemma":[0.0052835885,0.0002486594,0.09724924,0.00023175105,0.000027678489,0.000083536244,0.00080228894,0.7873573,0.011366062,0.0006721667,0.095045954,0.0016318237],"about_ca_topic_score_codex":0.0000058397345,"about_ca_topic_score_gemma":0.000016196464,"teacher_disagreement_score":0.15773262,"about_ca_system_score_codex":0.00004874196,"about_ca_system_score_gemma":0.000006191436,"threshold_uncertainty_score":0.3583591},"labels":[],"label_agreement":null},{"id":"W2601645953","doi":"10.15353/vsnl.v2i1.101","title":"Spatial Detection of Vehicles in Images using Convolutional Neural Networks and Stereo Matching","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","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":true,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Matching (statistics); Computer science; Pixel; Computer vision; Pattern recognition (psychology); Artificial neural network; RADIUS; Mathematics; Statistics","score_opus":0.00561190050870166,"score_gpt":0.22326729665161882,"score_spread":0.21765539614291715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2601645953","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":[0.60152674,0.0010912991,0.39710093,0.00006269872,0.00017151139,0.000028371249,0.00000185241,0.000012301836,0.000004322437],"genre_scores_gemma":[0.999288,0.000039153103,0.0005878465,0.000007363657,0.00006719343,3.4956858e-7,3.5313687e-7,0.000008106987,0.0000015892124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926335,0.00004649707,0.00042157483,0.000058682497,0.000119800396,0.00009007282],"domain_scores_gemma":[0.9995359,0.00015597735,0.00016495811,0.00002998458,0.000081664875,0.000031515447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029493013,0.00007928957,0.00018702149,0.00019106427,0.00004326935,0.000020973761,0.000042014522,0.000042461535,0.0000012051455],"category_scores_gemma":[0.000011195423,0.000058361587,0.000026678334,0.000057639103,0.00007426747,0.00026864358,0.00001960778,0.00012168488,1.4180375e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006718575,0.000019570718,0.037305344,0.0000776133,0.00003748376,0.000013744222,0.0001413684,0.8164802,0.0453981,0.00032678232,0.000013971211,0.10011862],"study_design_scores_gemma":[0.0006070866,0.00003075216,0.11803141,0.00023793356,0.000006970773,0.00039469803,0.00007677079,0.8793692,0.0002440343,0.000925219,0.000012291111,0.00006364445],"about_ca_topic_score_codex":0.000022401675,"about_ca_topic_score_gemma":0.000001654216,"teacher_disagreement_score":0.39776132,"about_ca_system_score_codex":0.000043753775,"about_ca_system_score_gemma":0.00001256738,"threshold_uncertainty_score":0.23799165},"labels":[],"label_agreement":null},{"id":"W2605428776","doi":"10.1145/3054977.3054979","title":"SafeWatch","year":2017,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Science Foundation","keywords":"Smartwatch; Alertness; Computer science; Distraction; Heading (navigation); Real-time computing; Human–computer interaction; Artificial intelligence; Simulation; Wearable computer; Embedded system; Engineering","score_opus":0.006936083519687994,"score_gpt":0.20432304354810799,"score_spread":0.19738696002842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605428776","genre_codex":"other","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":[0.26387718,0.00003821198,0.0061657946,0.00071377127,0.0001938064,0.00003046464,6.880794e-7,0.0012282454,0.72775185],"genre_scores_gemma":[0.99721193,0.000016801383,0.0007692834,0.0000196483,0.000017414213,0.0000019343067,2.3976733e-7,0.000005557973,0.0019572119],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99982804,6.760067e-7,0.00003730069,0.000037931466,0.000016131211,0.00007989972],"domain_scores_gemma":[0.99966687,0.0000030767446,0.0000053321364,0.00030837205,0.00000302242,0.0000133179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000026857326,0.000033126184,0.000039525505,0.000008883478,0.00012292256,0.0000124174085,0.00017198498,0.00005357847,0.0002008176],"category_scores_gemma":[0.0000054952993,0.00002969852,0.000012298636,0.000005092341,0.000034197234,0.000060379156,0.000023650226,0.00006118049,0.00037337834],"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":[0.000006367393,0.000025372161,0.04551443,0.00003198193,0.00010455574,0.000045604753,0.000161663,0.001373895,0.009302169,0.26125512,0.031056274,0.65112257],"study_design_scores_gemma":[0.0007402941,0.00003295409,0.45210937,0.000011945933,0.000014293824,0.000027469561,0.000059314916,0.0724437,0.106351405,0.027865782,0.3397336,0.0006098725],"about_ca_topic_score_codex":0.000006124525,"about_ca_topic_score_gemma":0.000015760432,"teacher_disagreement_score":0.7333347,"about_ca_system_score_codex":0.000006863262,"about_ca_system_score_gemma":0.0000017444473,"threshold_uncertainty_score":0.47991475},"labels":[],"label_agreement":null},{"id":"W2606791716","doi":"","title":"Modern Techniques to Attain Smart Vehicular Schemes Using Embedded Systems","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Vendor; SAFER; Automation; Software; Toll; Transport engineering; Control (management); Computer security; Engineering; Liability; Advanced driver assistance systems; Risk analysis (engineering); Computer science; Business; Finance","score_opus":0.007474063458821558,"score_gpt":0.22200976884692275,"score_spread":0.21453570538810118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606791716","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":[0.44652793,0.0021628384,0.5497753,0.00032426452,0.00013317926,0.00015753786,0.000002706657,0.00067736174,0.00023887078],"genre_scores_gemma":[0.9975445,0.00071746233,0.0011363117,0.000038965627,0.00017430974,0.000015008083,5.1732576e-7,0.00005875859,0.00031413382],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974563,0.000053316155,0.00032230522,0.00018625068,0.00016106201,0.0018207651],"domain_scores_gemma":[0.9995033,0.00003352189,0.00005742515,0.00026533494,0.00005908183,0.000081337625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011657764,0.00021075226,0.00025069635,0.00020189164,0.00016263133,0.000029929353,0.0003219704,0.0002314796,0.000007729663],"category_scores_gemma":[0.000040209234,0.00015942933,0.000089572655,0.00016426407,0.000040961208,0.00018982636,0.000044931527,0.0009956771,0.00003556264],"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":[0.00007864028,0.00006619366,0.002356537,0.000055567398,0.00087236415,0.000051590894,0.00021760393,0.015389801,0.48240364,0.117662966,0.00044873002,0.38039637],"study_design_scores_gemma":[0.0033949167,0.0012875681,0.00037029243,0.0013480375,0.00034456715,0.00883135,0.0019050427,0.3211027,0.23749146,0.3419697,0.07799601,0.0039583645],"about_ca_topic_score_codex":0.0000101545065,"about_ca_topic_score_gemma":0.000030978877,"teacher_disagreement_score":0.5510166,"about_ca_system_score_codex":0.0014273572,"about_ca_system_score_gemma":0.00037120644,"threshold_uncertainty_score":0.6501339},"labels":[],"label_agreement":null},{"id":"W2609273318","doi":"","title":"Hierarchical Architecture for Connected Vehicles Collision Avoidance","year":2014,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","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":"Université du Québec en Outaouais","funders":"","keywords":"Collision avoidance; Architecture; Computer science; Collision; Computer network; Distributed computing; Computer security; Geography; Archaeology","score_opus":0.007761324649936531,"score_gpt":0.2062389780950536,"score_spread":0.19847765344511706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609273318","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":[0.28957814,0.0008981256,0.6932492,0.006299346,0.00026333422,0.0006811774,0.00014473626,0.0015353903,0.0073504983],"genre_scores_gemma":[0.92846787,0.00027743485,0.06921922,0.0000826232,0.00004235839,0.00020781318,0.00047177545,0.00008840805,0.0011425036],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968907,0.0013283108,0.00049896544,0.0006371898,0.00020914206,0.0004356481],"domain_scores_gemma":[0.99529845,0.0020580124,0.00019197076,0.0016371298,0.0006740394,0.00014041805],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0024558203,0.0003882508,0.0004871499,0.00018925604,0.00032780413,0.00012262656,0.0011348731,0.00078195636,0.000029166396],"category_scores_gemma":[0.0010561892,0.00041754008,0.00022508194,0.00021111387,0.00029894782,0.000047146426,0.00060202,0.0013200109,0.000025838877],"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":[0.00016505958,0.0007841314,0.0015587328,0.0025414964,0.0006651213,0.0000141801265,0.012063942,0.0794463,0.029621612,0.416134,0.011746832,0.4452586],"study_design_scores_gemma":[0.0014907883,0.0000016218085,0.0038367843,0.0021956165,0.00009391119,0.000020667369,0.00004464061,0.6638363,0.16365662,0.07068,0.09290104,0.0012419823],"about_ca_topic_score_codex":0.00004557308,"about_ca_topic_score_gemma":0.00046875526,"teacher_disagreement_score":0.63888973,"about_ca_system_score_codex":0.00011361434,"about_ca_system_score_gemma":0.000113915725,"threshold_uncertainty_score":0.9998276},"labels":[],"label_agreement":null},{"id":"W2612090565","doi":"","title":"Towards Affordable Self-driving Cars.","year":2016,"lang":"en","type":"article","venue":"British Machine Vision Conference","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Self driving; Business; Automotive engineering; Computer science; Transport engineering; Engineering","score_opus":0.005288394893174995,"score_gpt":0.2080674052543622,"score_spread":0.2027790103611872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612090565","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":[0.73968804,0.0018897733,0.19047533,0.0012947639,0.0008258807,0.00038060138,0.000108152,0.0076346183,0.057702854],"genre_scores_gemma":[0.9949906,0.0012603886,0.0030147429,0.00003993872,0.000040439183,0.000017126977,0.000004885271,0.000034361085,0.00059753685],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99896306,0.000021209995,0.0002446712,0.0002677608,0.00014109825,0.00036221257],"domain_scores_gemma":[0.999479,0.000041908374,0.000030656836,0.0002976589,0.000055196782,0.00009558004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014989289,0.00016630476,0.00022220299,0.000069029,0.00011458107,0.000061802275,0.0003306244,0.00017790098,0.00086200784],"category_scores_gemma":[0.000040035186,0.0001551015,0.00005776779,0.0001320986,0.00006927344,0.00024120258,0.00012019742,0.00019323455,0.0002037993],"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":[0.0000022705387,0.000030352905,0.002256499,0.00002066847,0.000026647678,0.00005481151,0.000033638218,0.000038903552,0.005027195,0.0031740174,0.002184467,0.98715055],"study_design_scores_gemma":[0.0053120903,0.00061373034,0.3062762,0.0020668132,0.000108352375,0.001511392,0.00008475349,0.2404146,0.04543909,0.023151036,0.37187192,0.003150009],"about_ca_topic_score_codex":0.00014744319,"about_ca_topic_score_gemma":0.00034430504,"teacher_disagreement_score":0.9840005,"about_ca_system_score_codex":0.000068595284,"about_ca_system_score_gemma":0.000044609762,"threshold_uncertainty_score":0.9438382},"labels":[],"label_agreement":null},{"id":"W2729352406","doi":"10.1109/itsc.2017.8317915","title":"Imitation learning for vision-based lane keeping assistance","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Volvo (Canada)","funders":"","keywords":"Computer science; Convolutional neural network; Robustness (evolution); Artificial intelligence; Imitation; Computer vision; Gaze; Grayscale; Smoothness; Task (project management); Engineering; Pixel; Mathematics","score_opus":0.016119714636226558,"score_gpt":0.26754426432992406,"score_spread":0.2514245496936975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2729352406","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":[0.018643592,0.0003909041,0.95671666,0.00053244666,0.00062981265,0.00040575446,0.00001333134,0.002349164,0.020318318],"genre_scores_gemma":[0.97648114,0.000037499503,0.022307308,0.000034431698,0.00007006848,0.000111724774,0.00012410134,0.00004550054,0.0007882122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923384,0.000013128473,0.0002105613,0.00026037547,0.00006848503,0.00021361715],"domain_scores_gemma":[0.999242,0.00014797908,0.00010472804,0.00042658742,0.000049592985,0.000029081075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002456169,0.00018711235,0.0002533668,0.00009059398,0.00022032352,0.000055447726,0.00029134177,0.000519152,0.000024143335],"category_scores_gemma":[0.00012195831,0.00020223403,0.00010486789,0.000019694207,0.000042474927,0.00005019702,0.00007345084,0.00068579294,0.00002612096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024214032,0.000014913193,0.00087505573,0.0006475062,0.0000880325,0.0000044556173,0.00006853305,0.89874864,0.0005962564,0.0024561333,0.0012101708,0.09526609],"study_design_scores_gemma":[0.00030588615,0.000027436732,0.0048846444,0.0001874073,0.000026307998,5.310013e-7,0.000016655418,0.97532177,0.0017585935,0.0047334344,0.012396356,0.0003409894],"about_ca_topic_score_codex":0.000009243014,"about_ca_topic_score_gemma":0.000028189192,"teacher_disagreement_score":0.9578376,"about_ca_system_score_codex":0.00009799168,"about_ca_system_score_gemma":0.000054458025,"threshold_uncertainty_score":0.82468647},"labels":[],"label_agreement":null},{"id":"W2740401942","doi":"10.5220/0006468404250430","title":"Autonomous Trail Following","year":2017,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"York University","funders":"","keywords":"Heading (navigation); Artificial intelligence; Computer science; Computer vision; Robot; Task (project management); Key (lock); Standard deviation; Geography; Engineering; Mathematics; Geodesy; Computer security; Statistics","score_opus":0.010627977951760579,"score_gpt":0.2238949961918044,"score_spread":0.21326701824004382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2740401942","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":[0.5916181,0.00009396169,0.006925209,0.0003950132,0.0004918317,0.00006146999,0.0000011212564,0.0020900068,0.3983233],"genre_scores_gemma":[0.9967781,0.000007370604,0.0013546019,0.000032147906,0.000028441968,0.0000062319605,6.421521e-7,0.000015464064,0.0017770423],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996079,0.000001973302,0.0000895377,0.00008866144,0.00003533832,0.00017655315],"domain_scores_gemma":[0.9995003,0.000008058704,0.0000133488375,0.00044383085,0.0000033235438,0.00003117408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000068099536,0.00007874686,0.00010024793,0.000024972656,0.00026446866,0.00003622521,0.00030675955,0.000108440916,0.00012173383],"category_scores_gemma":[0.000013104403,0.00007584571,0.00006330541,0.000012492697,0.000039127637,0.00014970364,0.000035491183,0.00012419595,0.00025013424],"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":[0.000007696254,0.00005273542,0.025704958,0.00004367381,0.00041227555,0.00025468165,0.00053231244,0.0029436804,0.013059464,0.093418844,0.0075256,0.85604405],"study_design_scores_gemma":[0.002815365,0.00011566469,0.4113396,0.00005365978,0.00011216064,0.000073768264,0.0002665779,0.21757235,0.08954024,0.021898482,0.25420493,0.0020071932],"about_ca_topic_score_codex":0.000010725282,"about_ca_topic_score_gemma":0.000025740173,"teacher_disagreement_score":0.85403687,"about_ca_system_score_codex":0.000023978786,"about_ca_system_score_gemma":0.0000074645604,"threshold_uncertainty_score":0.3215053},"labels":[],"label_agreement":null},{"id":"W2751602042","doi":"10.1007/978-3-319-67534-3_13","title":"SwifTree: Interactive Extraction of 3D Trees Supporting Gaming and Crowdsourcing","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Segmentation; Crowdsourcing; Robustness (evolution); Tree (set theory); Artificial intelligence; Software; Data mining; Machine learning; Pattern recognition (psychology); World Wide Web","score_opus":0.009299112628253458,"score_gpt":0.24591953462002372,"score_spread":0.23662042199177027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751602042","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":[0.017432762,0.00040961514,0.9763958,0.00005409815,0.0005389239,0.00013749844,0.000003551806,0.00017532008,0.004852449],"genre_scores_gemma":[0.97474676,0.000047192898,0.024981717,0.000018174747,0.00009790765,0.0000018106646,0.0000015315527,0.00002432244,0.00008059678],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884707,0.000005617263,0.00032225702,0.00039672115,0.00015950407,0.00026883488],"domain_scores_gemma":[0.9990375,0.00022960124,0.0002453846,0.00039971905,0.000049872186,0.000037915906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003448739,0.0002411149,0.00034113973,0.00037390398,0.00016189837,0.000056619087,0.00042353597,0.00030093954,0.000013514252],"category_scores_gemma":[0.00007740853,0.00024261087,0.000041370156,0.000047009187,0.0005089569,0.0003056746,0.00021073669,0.0007261304,0.0000025381485],"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":[0.000005118166,0.000004244025,0.00045644603,0.000057772788,0.00001721736,0.000032009484,0.00082889403,0.039775163,0.0031024313,0.0002632545,0.0000010880043,0.9554564],"study_design_scores_gemma":[0.00027670842,0.000110511195,0.005209247,0.0011243697,0.0000313142,0.00017739978,0.000002937116,0.9441541,0.028460555,0.019386532,0.00040121767,0.00066510955],"about_ca_topic_score_codex":0.00001866669,"about_ca_topic_score_gemma":0.00015113976,"teacher_disagreement_score":0.957314,"about_ca_system_score_codex":0.00011197586,"about_ca_system_score_gemma":0.00005127359,"threshold_uncertainty_score":0.9893384},"labels":[],"label_agreement":null},{"id":"W2757046826","doi":"10.1007/s12239-018-0016-1","title":"Belief and fuzzy theories for driving behavior assessment in case of accident scenarios","year":2017,"lang":"en","type":"article","venue":"International Journal of Automotive Technology","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; National Highway Traffic Safety Administration","keywords":"Accident (philosophy); Fuzzy logic; Computer science; Psychology; Engineering; Transport engineering; Artificial intelligence; Epistemology","score_opus":0.007964073541839705,"score_gpt":0.30725569603801484,"score_spread":0.29929162249617514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757046826","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":[0.99149156,0.00015031235,0.005845086,0.0016334807,0.0004523553,0.00013995665,0.000010865003,0.00006428027,0.00021210017],"genre_scores_gemma":[0.994274,0.00010655486,0.0055356626,0.000008231078,0.000034537265,0.000017058992,6.881261e-7,0.000012920005,0.000010343081],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992119,0.000010287579,0.00043913256,0.0001002205,0.0000984459,0.0001400293],"domain_scores_gemma":[0.9990353,0.00008406864,0.00035707984,0.00017648506,0.00032429898,0.000022781584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003330368,0.00010891237,0.000261845,0.000538243,0.000075413955,0.000023999039,0.0005303622,0.00020345468,0.000008840343],"category_scores_gemma":[0.0002478544,0.000103744926,0.000058580823,0.000043504002,0.00029503458,0.0002225479,0.00015413149,0.0003951417,4.2470438e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052071715,0.00022983355,0.74258053,0.000022792407,0.00042341926,0.0026446732,0.0004870688,0.00092874165,0.0058108834,0.09971024,0.00003697358,0.14707279],"study_design_scores_gemma":[0.0031917142,0.0006409441,0.852847,0.00030392336,0.00010089183,0.008715067,0.0015689065,0.004109834,0.03968188,0.08820559,0.0003145649,0.00031969245],"about_ca_topic_score_codex":0.000013214882,"about_ca_topic_score_gemma":0.00015493967,"teacher_disagreement_score":0.14675309,"about_ca_system_score_codex":0.00014238361,"about_ca_system_score_gemma":0.00004844348,"threshold_uncertainty_score":0.42305952},"labels":[],"label_agreement":null},{"id":"W2765774826","doi":"10.1155/2017/7090549","title":"A Hybrid Vision-Map Method for Urban Road Detection","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Electronic Components and Systems for European Leadership; Ministerio de Economía y Competitividad; European Commission; Comunidad de Madrid","keywords":"Artificial intelligence; Computer vision; Computer science; Robustness (evolution); Global Positioning System; Machine vision; Digital mapping; Set (abstract data type); Deep learning; Stereopsis; Geography; Cartography","score_opus":0.0057638346584147114,"score_gpt":0.2659287288464097,"score_spread":0.260164894187995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765774826","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":[0.34911558,0.00016210452,0.64977443,0.00013966871,0.0005847332,0.00010693443,0.000009424552,0.00006649134,0.00004062246],"genre_scores_gemma":[0.9329773,0.000055773027,0.066814184,0.000008359204,0.000096528885,0.0000070242113,0.000004405891,0.000017156764,0.000019238762],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99939996,0.000006153764,0.00032640045,0.00007336473,0.00008063038,0.0001134619],"domain_scores_gemma":[0.9994229,0.000032312713,0.00026273483,0.00014761709,0.0000971579,0.000037271056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002024339,0.00008701267,0.00017294982,0.00008187511,0.00016751868,0.000014857484,0.00014366859,0.00006947599,0.0000051692823],"category_scores_gemma":[0.000025813042,0.0000841492,0.00011289959,0.000019354491,0.000021814705,0.00053716806,9.15119e-7,0.00018005467,0.0000016324933],"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":[0.00026218282,0.000029531362,0.0002295363,0.000082225124,0.00009169948,0.000023123599,0.00036768295,0.099192716,0.09628911,0.00036916323,0.0000896004,0.80297345],"study_design_scores_gemma":[0.005660907,0.0011654764,0.44719407,0.00025793805,0.0003106169,0.00008653843,0.00032306046,0.038459484,0.44705623,0.024640724,0.03422734,0.00061759236],"about_ca_topic_score_codex":0.0000019154795,"about_ca_topic_score_gemma":0.000027658189,"teacher_disagreement_score":0.8023558,"about_ca_system_score_codex":0.00004125196,"about_ca_system_score_gemma":0.0000126430605,"threshold_uncertainty_score":0.34315047},"labels":[],"label_agreement":null},{"id":"W2765836461","doi":"10.1155/2017/6057830","title":"Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Geolocation; Anomaly detection; Identification (biology); Computer science; Global Positioning System; Artificial neural network; Anomaly (physics); Accelerometer; Real-time computing; Artificial intelligence; Data mining; Algorithm; Telecommunications","score_opus":0.007606384660166706,"score_gpt":0.22529071304826695,"score_spread":0.21768432838810026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765836461","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":[0.77041173,0.00014365395,0.22892138,0.000049893606,0.00034562853,0.00008249776,0.000002112625,0.00003675021,0.000006359145],"genre_scores_gemma":[0.9982282,0.00013103198,0.0015577131,0.0000058896926,0.000049806862,0.000005341078,0.0000052052123,0.000012327224,0.000004520354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9995169,0.000004288148,0.00028095723,0.00006326299,0.0000487497,0.00008582617],"domain_scores_gemma":[0.99956447,0.000022853677,0.00021479739,0.0000944347,0.00007647761,0.0000269616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011476439,0.00006735838,0.00011856786,0.000056252422,0.000187578,0.000023631128,0.000083282845,0.00007772941,0.0000011292973],"category_scores_gemma":[0.0000124627895,0.00006963098,0.00004551846,0.000024278552,0.00003587349,0.0005802458,9.305246e-7,0.00013531896,1.16772355e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017885648,0.00001515177,0.009803771,0.00005044789,0.00005728172,0.000006559506,0.0005665549,0.6988119,0.06837813,0.00030294215,0.0000031161585,0.22182532],"study_design_scores_gemma":[0.0011277951,0.00009772461,0.8015187,0.000020645974,0.000051926283,0.000008760956,0.000110550216,0.18836236,0.007811536,0.00071517116,0.00008739715,0.00008746074],"about_ca_topic_score_codex":0.0000010336365,"about_ca_topic_score_gemma":0.000038438775,"teacher_disagreement_score":0.7917149,"about_ca_system_score_codex":0.000018923183,"about_ca_system_score_gemma":0.0000036828262,"threshold_uncertainty_score":0.2839469},"labels":[],"label_agreement":null},{"id":"W2768911616","doi":"10.1155/2017/9620831","title":"A Novel Rear-End Collision Detection Algorithm Based on GNSS Fusion and ANFIS","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Algorithm; GNSS applications; Computer science; Artificial intelligence; Global Positioning System; Telecommunications","score_opus":0.006271278433127777,"score_gpt":0.22040713788108995,"score_spread":0.21413585944796218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768911616","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":[0.75480014,0.0000562957,0.2444966,0.00011641578,0.00030260274,0.000078146935,0.000012774419,0.000054365035,0.00008265099],"genre_scores_gemma":[0.9793907,0.00017607774,0.020342698,0.000014987822,0.000045856865,0.0000022659544,0.0000049955675,0.00001535133,0.000007067197],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993932,0.0000054639913,0.00027069764,0.00008950756,0.00014061414,0.00010049862],"domain_scores_gemma":[0.9994939,0.000029232051,0.00021540865,0.0001437741,0.000071339295,0.00004636084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014994605,0.000099959514,0.00015458858,0.00011986243,0.00019885522,0.000017943003,0.000087346954,0.000120479366,0.000006505297],"category_scores_gemma":[0.00001863393,0.00009368975,0.000048817958,0.000048282145,0.000042027743,0.00036897842,0.0000013260577,0.0002492199,9.239577e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025031244,0.000056380064,0.00055668544,0.00003473568,0.000022100892,0.000035519006,0.00023294789,0.2517848,0.14055717,0.000053021537,0.0000057974603,0.6064105],"study_design_scores_gemma":[0.0031760924,0.000621824,0.7744634,0.00020496165,0.000061659106,0.00003570854,0.00013851788,0.11499696,0.10443718,0.00048726378,0.0011485491,0.000227854],"about_ca_topic_score_codex":0.000005836391,"about_ca_topic_score_gemma":0.000049712988,"teacher_disagreement_score":0.77390677,"about_ca_system_score_codex":0.00004691181,"about_ca_system_score_gemma":0.000013335915,"threshold_uncertainty_score":0.3820557},"labels":[],"label_agreement":null},{"id":"W2770086227","doi":"10.1155/2017/9203251","title":"PaTAVTT: A Hardware-in-the-Loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Higher Education Discipline Innovation Project; Ministry of Transport of the People's Republic of China; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Trajectory; Adaptability; Hardware-in-the-loop simulation; Tracking (education); Simulation; Loop (graph theory); Process (computing); Transmission (telecommunications); Engineering; Vehicle dynamics; Computer science; Control engineering; Real-time computing; Automotive engineering","score_opus":0.02310765528199139,"score_gpt":0.2567387863299689,"score_spread":0.2336311310479775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770086227","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":[0.9854027,0.00021474093,0.013456059,0.00013651696,0.0003043058,0.00023441893,0.00001419384,0.000081284656,0.00015576363],"genre_scores_gemma":[0.99100435,0.00003578372,0.008789843,0.000023299146,0.000090109825,0.000016227494,0.0000062593826,0.00002473854,0.000009418243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989766,0.000007005607,0.0005509798,0.00010377793,0.000132532,0.00022914479],"domain_scores_gemma":[0.9991612,0.00016184074,0.00033194467,0.00020775315,0.000100702746,0.000036602607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004089868,0.0001345873,0.0002478661,0.000107133215,0.00024471953,0.000036038724,0.00035363564,0.0001156396,0.0000033337972],"category_scores_gemma":[0.000097138036,0.00011607872,0.000113780894,0.00007483776,0.00005121027,0.00078080076,0.0000014263373,0.00037027418,0.0000011040231],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032252213,0.00012117712,0.017121222,0.00024378342,0.000093617695,0.0001996076,0.005986677,0.46759003,0.04158852,0.00059715693,0.000027229808,0.46610844],"study_design_scores_gemma":[0.003320673,0.0003001655,0.96791863,0.00024046382,0.00008791148,0.00005271507,0.0010648873,0.011403205,0.0116240755,0.0026995668,0.0009979976,0.00028972633],"about_ca_topic_score_codex":0.0000044339026,"about_ca_topic_score_gemma":0.00013396013,"teacher_disagreement_score":0.9507974,"about_ca_system_score_codex":0.00007409209,"about_ca_system_score_gemma":0.00004139825,"threshold_uncertainty_score":0.4733553},"labels":[],"label_agreement":null},{"id":"W2771583656","doi":"10.1109/iccvw.2017.33","title":"Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior","year":2017,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":403,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Schema crosswalk; Pedestrian; Baseline (sea); Computer science; Benchmark (surveying); Minimum bounding box; Bounding overwatch; Point (geometry); Artificial intelligence; Machine learning; Pedestrian detection; Pedestrian crossing; Information retrieval; Human–computer interaction; Transport engineering; Image (mathematics); Engineering","score_opus":0.02574090554235806,"score_gpt":0.308778951740207,"score_spread":0.2830380461978489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771583656","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":[0.98884743,0.00010073358,0.0056673717,0.00053266436,0.0001801619,0.00052165193,0.0028378386,0.0004105914,0.0009015448],"genre_scores_gemma":[0.9955004,0.000016357517,0.0036477267,0.00011971881,0.000060783357,0.0001043615,0.00018610683,0.000023572562,0.0003410052],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992895,0.000004930353,0.00017078921,0.00022207288,0.0000460655,0.000266625],"domain_scores_gemma":[0.9991523,0.000056200617,0.000053860345,0.00062395475,0.000022311066,0.000091362526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026577205,0.00014032176,0.00016825608,0.00004343475,0.0005148471,0.00016943263,0.0003111143,0.00014485743,0.0000666949],"category_scores_gemma":[0.00018280043,0.00013158089,0.000029473405,0.000017003978,0.00007589121,0.00019778064,0.00013524029,0.00012416043,0.000040359977],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002480682,0.00018980405,0.664253,0.0004118083,0.00015905268,0.00015428208,0.00041471276,0.002008686,0.0071814544,0.0027430162,0.08863082,0.23360533],"study_design_scores_gemma":[0.0020671734,0.00012507876,0.78616387,0.000059034362,0.00008590028,0.000041679672,0.000107675434,0.023010636,0.006909359,0.000708431,0.17991391,0.00080726325],"about_ca_topic_score_codex":0.00004736347,"about_ca_topic_score_gemma":0.0009163178,"teacher_disagreement_score":0.23279805,"about_ca_system_score_codex":0.000021881719,"about_ca_system_score_gemma":0.000008985487,"threshold_uncertainty_score":0.5365713},"labels":[],"label_agreement":null},{"id":"W2774168074","doi":"10.1109/smc.2017.8123204","title":"Analysis of driving data for autonomous vehicle applications","year":2017,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Federal Highway Administration","keywords":"Computer science; Advanced driver assistance systems; MATLAB; Host (biology); Headway; Simulation; Real-time computing; Artificial intelligence","score_opus":0.02517368166128237,"score_gpt":0.2805864606498375,"score_spread":0.25541277898855513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774168074","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":[0.15212704,0.00011662255,0.82059246,0.00040603138,0.000073135605,0.0004689756,0.000291854,0.00093264563,0.024991224],"genre_scores_gemma":[0.9936809,0.000014123398,0.0059619043,0.000007331341,0.000015384036,0.000046665467,0.000071506816,0.0000099669305,0.00019221942],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953645,0.0000019651843,0.00015919257,0.00015133555,0.00003170327,0.000119344644],"domain_scores_gemma":[0.99806976,0.000046579473,0.00005306435,0.0017874707,0.00002094784,0.000022170101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013276952,0.000060946702,0.00017545337,0.00009411186,0.00018107441,0.000015896201,0.0008578539,0.00007903782,0.000039411105],"category_scores_gemma":[0.000025587871,0.00006171715,0.000053173397,0.00009146029,0.00006672759,0.00014031751,0.00015073021,0.000054703083,0.000007711752],"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":[0.000009218226,0.00015448857,0.13150509,0.00014448265,0.0057670088,0.0000014098842,0.0001504279,0.05281957,0.013841198,0.18667573,0.0022050359,0.60672635],"study_design_scores_gemma":[0.00011793944,0.0000061415544,0.074072465,0.0000017399768,0.0003631728,2.0655207e-7,0.000017119954,0.90197533,0.0022097805,0.0004470393,0.020693012,0.00009601964],"about_ca_topic_score_codex":0.00001664875,"about_ca_topic_score_gemma":0.00021285404,"teacher_disagreement_score":0.8491558,"about_ca_system_score_codex":0.000014610487,"about_ca_system_score_gemma":0.00001031253,"threshold_uncertainty_score":0.25167525},"labels":[],"label_agreement":null},{"id":"W2774270279","doi":"10.1109/jsen.2017.2780089","title":"Driving Maneuver Classification: A Comparison of Feature Extraction Methods","year":2017,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":50,"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; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Feature extraction; Preprocessor; Computer science; Pattern recognition (psychology); Artificial intelligence; Principal component analysis; Classifier (UML); Statistical classification; Data pre-processing; Feature (linguistics); Data mining","score_opus":0.0452357521931929,"score_gpt":0.37482613573157264,"score_spread":0.3295903835383797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774270279","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":[0.92164934,0.00024731993,0.06915371,0.00066531094,0.0016173464,0.00006400314,0.0000014756081,0.00017644667,0.006425027],"genre_scores_gemma":[0.9748748,0.000079428995,0.024485366,0.0000047355707,0.000188341,0.0000010497595,3.8103607e-7,0.000017462235,0.00034842573],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99932885,0.000052984033,0.0002572576,0.00009086817,0.000103306884,0.00016675492],"domain_scores_gemma":[0.9992259,0.000057183763,0.0002585084,0.00034518412,0.000057581332,0.00005565083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036210124,0.000106337146,0.00021566206,0.00009032052,0.00039162676,0.00005078296,0.00025993166,0.00023373055,0.00004952147],"category_scores_gemma":[0.000058539972,0.000101617305,0.000081246595,0.000037324793,0.00009288135,0.00022005962,0.000011916105,0.0008102462,0.000015223325],"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":[0.000043097443,0.00009799413,0.044002514,0.00006528742,0.00029973048,0.00006208175,0.0011130723,0.02937987,0.59440273,0.00075318216,0.016651781,0.31312865],"study_design_scores_gemma":[0.000610882,0.00006582653,0.39594027,0.00010176676,0.00009361984,0.00095966895,0.00058909203,0.28508332,0.28729725,0.0009759468,0.02790695,0.00037539736],"about_ca_topic_score_codex":0.0000013451927,"about_ca_topic_score_gemma":0.000005326858,"teacher_disagreement_score":0.35193774,"about_ca_system_score_codex":0.00006035164,"about_ca_system_score_gemma":0.000013539742,"threshold_uncertainty_score":0.41438335},"labels":[],"label_agreement":null},{"id":"W2776336566","doi":"10.15353/vsnl.v3i1.181","title":"Efficient Deep Network Architecture for Vision-Based Vehicle Detection Keyvan Kasiri,","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ontario Centres of Excellence","keywords":"Deep learning; Artificial intelligence; Computer science; Software deployment; Artificial neural network; Architecture; Object detection; Deep neural networks; Process (computing); Network architecture; Machine learning; Pattern recognition (psychology); Computer security","score_opus":0.004742313726864791,"score_gpt":0.2403935391249207,"score_spread":0.2356512253980559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2776336566","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":[0.24338314,0.00084721943,0.75416756,0.00053726666,0.00079725735,0.00013379732,0.0000029110256,0.00006280557,0.00006802715],"genre_scores_gemma":[0.99454147,0.0000034985098,0.0051196613,0.00004745697,0.00025857,0.0000034294465,0.0000017834691,0.000018295455,0.000005843988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999144,0.000030803167,0.0003876713,0.00009356314,0.00017958831,0.00016438536],"domain_scores_gemma":[0.9991366,0.00020562486,0.0002867772,0.00012930194,0.0001705274,0.0000711611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048171144,0.0001224928,0.00022561976,0.00012381216,0.0005747374,0.00019949563,0.00016242401,0.00006584756,0.0000017148413],"category_scores_gemma":[0.000049712173,0.00010148151,0.00009056729,0.000044115764,0.00009213006,0.00008633586,0.000020485266,0.00021485196,0.000001652037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004550725,0.000015599764,0.0005424539,0.000049521288,0.000023396853,0.000004272489,0.000046562796,0.9421707,0.00042915897,0.00020123551,0.00022594944,0.05624567],"study_design_scores_gemma":[0.0008543732,0.00010188372,0.018201021,0.00016557069,0.000016726974,0.00009903181,0.000034791214,0.9765328,0.00009673748,0.0014976264,0.002292573,0.00010682181],"about_ca_topic_score_codex":0.000002735663,"about_ca_topic_score_gemma":0.0000015764125,"teacher_disagreement_score":0.7511583,"about_ca_system_score_codex":0.000049613533,"about_ca_system_score_gemma":0.00003521575,"threshold_uncertainty_score":0.44204736},"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":"W2783437277","doi":"10.1145/3161179","title":"SafeDrive","year":2018,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Mitacs; National Science Foundation","keywords":"Gesture; Computer science; Wearable computer; Distracted driving; Artificial intelligence; Smartwatch; Real-time computing; Computer vision; Simulation; Human–computer interaction; Poison control; Embedded system","score_opus":0.006253644753485771,"score_gpt":0.22049040170588244,"score_spread":0.21423675695239666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783437277","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":[0.9824055,0.00038432318,0.000011751426,0.0006252027,0.00021483557,0.00031184647,0.000005912882,0.001564322,0.014476318],"genre_scores_gemma":[0.9986763,0.00039738652,0.00054125045,0.0000333509,0.000033409535,0.00011650722,1.4003601e-7,0.00002429523,0.00017736253],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991504,0.0000027011426,0.00021384445,0.00024853737,0.00010704969,0.00027748302],"domain_scores_gemma":[0.99922585,0.00008153352,0.000101044796,0.00043548984,0.00013963712,0.000016459795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001030448,0.00020014025,0.0002467611,0.00016107225,0.00017696976,0.000022952356,0.0012088722,0.00024844013,0.00001588428],"category_scores_gemma":[0.00039785233,0.00014195834,0.0000619313,0.00029311897,0.0007348316,0.00019808984,0.0008064572,0.00048339117,0.000026864498],"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":[0.00037252347,0.00021985141,0.010042084,0.00028853634,0.00045389545,0.0000027831368,0.0021701253,0.0001051784,0.6038951,0.022711022,0.013688146,0.3460507],"study_design_scores_gemma":[0.00015930917,0.00053779426,0.0013478022,0.00018318508,0.000018139284,0.000020145073,0.0029981516,0.0004126884,0.9584233,0.030657658,0.005066117,0.0001756848],"about_ca_topic_score_codex":0.000005480053,"about_ca_topic_score_gemma":0.0000030760466,"teacher_disagreement_score":0.3545282,"about_ca_system_score_codex":0.00006157156,"about_ca_system_score_gemma":0.0000076597835,"threshold_uncertainty_score":0.5788893},"labels":[],"label_agreement":null},{"id":"W2786007213","doi":"10.1109/vtcfall.2017.8288317","title":"Fog Assisted Driver Behavior Monitoring for Intelligent Transportation System","year":2017,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cloud computing; Intelligent transportation system; Computer science; Population; Urbanization; Computer security; Convergence (economics); Real-time computing; Transport engineering; Engineering","score_opus":0.025901420358096946,"score_gpt":0.2613042094917512,"score_spread":0.23540278913365426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786007213","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":[0.89374095,0.000043187152,0.1020179,0.000028615743,0.0007365903,0.00033486407,0.000013368145,0.0011434199,0.0019410999],"genre_scores_gemma":[0.996319,0.000008662909,0.0031998258,0.0000011539883,0.000051592426,0.00014410751,0.000006990864,0.000019280349,0.0002493785],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9995496,0.0000020402015,0.00015475236,0.000105078696,0.00004407081,0.00014444014],"domain_scores_gemma":[0.99962604,0.000012244078,0.00003378562,0.00027346524,0.00002539684,0.000029089002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000059354687,0.00008770805,0.00011067043,0.00003250075,0.00021683806,0.000023792545,0.00017641994,0.00013456034,0.000010049601],"category_scores_gemma":[0.0000039258825,0.000086187174,0.000056766294,0.000014144265,0.00002594675,0.00011092236,0.0000046791556,0.00008060064,0.000019442652],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000506259,0.00011793994,0.3518798,0.00066906353,0.00028652183,0.00006188597,0.0008480693,0.0033042703,0.030584894,0.04201511,0.0003880392,0.56979376],"study_design_scores_gemma":[0.00041994703,0.00003642993,0.8565422,0.000060398404,0.00009794405,0.000004766229,0.00042399002,0.010085502,0.13028139,0.00005371046,0.0017314142,0.00026224984],"about_ca_topic_score_codex":0.000015644824,"about_ca_topic_score_gemma":0.00002874352,"teacher_disagreement_score":0.56953156,"about_ca_system_score_codex":0.00007244331,"about_ca_system_score_gemma":0.00000493335,"threshold_uncertainty_score":0.35146108},"labels":[],"label_agreement":null},{"id":"W2786973885","doi":"10.1155/2018/9702730","title":"Investigations on Driver Unique Identification from Smartphone’s GPS Data Alone","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Global Positioning System; Telematics; Identification (biology); Computer science; Random forest; Classifier (UML); Data mining; Artificial intelligence; Machine learning; Telecommunications","score_opus":0.01546164424212296,"score_gpt":0.24260021528101794,"score_spread":0.22713857103889498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786973885","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":[0.90416867,0.00007896715,0.09463004,0.00027252387,0.0005024541,0.00007293244,0.00008558314,0.00010999621,0.00007886701],"genre_scores_gemma":[0.98442405,0.00013574443,0.01492249,0.00005385031,0.0001429014,0.0000021336928,0.0002904432,0.000017176448,0.000011216647],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9991902,0.000011990797,0.00044870604,0.000120439465,0.00013280356,0.00009583147],"domain_scores_gemma":[0.99929065,0.000042233987,0.00018643201,0.00030956307,0.000121831414,0.00004927853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013433126,0.00009327147,0.00013984946,0.00011682193,0.00006839108,0.000008783173,0.00024912425,0.00009880241,0.000036437657],"category_scores_gemma":[0.000024828332,0.000094162926,0.00003139811,0.00015428565,0.00008522103,0.00061460334,0.00000244455,0.00023653709,0.00003061764],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041784561,0.00026968747,0.01629959,0.000065552595,0.000513709,0.00007713387,0.0063672476,0.30442557,0.557544,0.008403414,0.0031622325,0.10245399],"study_design_scores_gemma":[0.001014828,0.00015763502,0.8370727,0.000108515305,0.00010254393,0.000005712496,0.00019575085,0.0040901676,0.13584994,0.015533379,0.005653705,0.00021517732],"about_ca_topic_score_codex":0.000006447761,"about_ca_topic_score_gemma":0.00016088616,"teacher_disagreement_score":0.82077307,"about_ca_system_score_codex":0.00004618664,"about_ca_system_score_gemma":0.000030748823,"threshold_uncertainty_score":0.38398525},"labels":[],"label_agreement":null},{"id":"W2789803700","doi":"10.1155/2018/5804536","title":"Design Method of ADAS for Urban Electric Vehicle Based on Virtual Prototyping","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Virtual prototyping; Advanced driver assistance systems; Process (computing); Automotive engineering; Rapid prototyping; Cost reduction; Engineering; Computer science; Engineering design process; Simulation; Systems engineering; Embedded system","score_opus":0.010585170503399565,"score_gpt":0.25450840220341076,"score_spread":0.24392323170001118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789803700","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":[0.16242866,0.000052702617,0.8369684,0.00003860156,0.00010778913,0.00031931532,0.000003372969,0.00005156026,0.000029581142],"genre_scores_gemma":[0.8684405,0.000010853966,0.13142928,0.000021857897,0.000055478828,0.000017783188,0.0000021554563,0.000018793071,0.000003293559],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999245,0.0000211444,0.00041247177,0.00007590894,0.000111309935,0.00013416624],"domain_scores_gemma":[0.99938387,0.0001439681,0.00019341976,0.00008176928,0.00016689792,0.00003006747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032314082,0.00009367328,0.000199508,0.00017863952,0.00004234812,0.0000022711429,0.0000943091,0.000092683265,0.000007531201],"category_scores_gemma":[0.00002484645,0.00008926296,0.00007847497,0.00020286086,0.00002225083,0.0001521315,3.2183237e-7,0.00016162133,7.8341196e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011198218,0.000052786214,0.00016244574,0.000053805434,0.000045340366,0.0000031794546,0.00045672306,0.7202748,0.19547027,0.000938588,0.000024063833,0.08139819],"study_design_scores_gemma":[0.0020517597,0.003170963,0.014487613,0.00011965473,0.000068712085,0.0000036060057,0.00007625958,0.22386983,0.75382644,0.0016481349,0.00052158686,0.00015541885],"about_ca_topic_score_codex":3.133712e-7,"about_ca_topic_score_gemma":0.0000018597972,"teacher_disagreement_score":0.70601183,"about_ca_system_score_codex":0.000044617405,"about_ca_system_score_gemma":0.00004745722,"threshold_uncertainty_score":0.3640038},"labels":[],"label_agreement":null},{"id":"W2790799255","doi":"10.1007/s11760-018-1264-2","title":"Real-time road surface and semantic lane estimation using deep features","year":2018,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Deconvolution; Artificial intelligence; Road surface; Computer vision; Task (project management); Pattern recognition (psychology); Artificial neural network; Algorithm; Engineering","score_opus":0.007022154002578906,"score_gpt":0.24213218911012965,"score_spread":0.23511003510755074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790799255","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":[0.9199639,0.00157791,0.076632075,0.000054712524,0.000022797663,0.00008323351,0.0000017339117,0.0004572501,0.0012063931],"genre_scores_gemma":[0.9676333,0.000055911136,0.032140706,0.000019614119,0.00005470784,0.000001095276,0.0000037738141,0.00002309118,0.00006776776],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993952,0.000016689151,0.0001398054,0.00017742955,0.00006274985,0.00020813948],"domain_scores_gemma":[0.99977225,0.000024761679,0.000036351623,0.000079746154,0.000041476076,0.000045411914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016075165,0.0001385967,0.00015717193,0.000052757154,0.0002484838,0.000090711386,0.000053734664,0.00012163619,0.000023813669],"category_scores_gemma":[0.00001439599,0.00013260356,0.0000128734755,0.000111856556,0.00018522739,0.00038576184,0.000034430013,0.00014017297,0.000012856579],"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":[0.000025151126,0.000012118575,0.0005427685,0.00031156398,0.000030371968,0.000023316114,0.0010461315,0.0015712943,0.71431214,0.000033439766,0.00009574806,0.28199598],"study_design_scores_gemma":[0.00020646877,0.00003850696,0.0034744754,0.00012595476,0.000041944757,0.00011571625,0.00007850157,0.93851095,0.05616017,0.0010084582,0.000022733355,0.0002160874],"about_ca_topic_score_codex":0.00003797671,"about_ca_topic_score_gemma":0.000007565162,"teacher_disagreement_score":0.9369397,"about_ca_system_score_codex":0.000019344743,"about_ca_system_score_gemma":0.000016618924,"threshold_uncertainty_score":0.5407416},"labels":[],"label_agreement":null},{"id":"W2790965127","doi":"10.1155/2018/2586520","title":"A Rapid Prototyping Environment for Cooperative Advanced Driver Assistance Systems","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Technische Universität Berlin; Deutsche Forschungsgemeinschaft","keywords":"Advanced driver assistance systems; Automation; Scalability; Computer science; Process (computing); Vehicle dynamics; Systems engineering; Automotive industry; Engineering; Automotive engineering; Artificial intelligence","score_opus":0.007081815091806632,"score_gpt":0.21525470830764162,"score_spread":0.208172893215835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790965127","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":[0.60854137,0.0019667086,0.3876486,0.00007510385,0.0006267921,0.00090117287,0.000027223,0.00011239753,0.00010064872],"genre_scores_gemma":[0.97747445,0.0005244602,0.021733165,0.000013419165,0.00010717143,0.00008358661,0.000009063978,0.000024427613,0.000030230603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991468,0.000010808315,0.0004598037,0.00010734166,0.00011199223,0.00016322237],"domain_scores_gemma":[0.999495,0.000034335964,0.00019633364,0.000095912896,0.00013552452,0.000042863972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001343709,0.00012529809,0.00022917247,0.00007319206,0.000087621156,0.0000072326566,0.00009524787,0.000087577224,0.000015331903],"category_scores_gemma":[0.0000086692235,0.00011739964,0.000070447524,0.000079291705,0.00006899965,0.00037494497,8.379299e-7,0.00015941725,0.0000040914747],"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":[0.00097175094,0.00007944636,0.00030264608,0.00023364667,0.0002345341,0.00001893584,0.0028172263,0.7511173,0.061884224,0.0023775287,0.000090244415,0.17987256],"study_design_scores_gemma":[0.014926533,0.0056030387,0.16984758,0.0013541039,0.0003920686,0.00007615405,0.0046904306,0.032369066,0.19704276,0.0019510315,0.5701205,0.0016267074],"about_ca_topic_score_codex":2.65448e-7,"about_ca_topic_score_gemma":0.0000066565035,"teacher_disagreement_score":0.7187482,"about_ca_system_score_codex":0.000103201965,"about_ca_system_score_gemma":0.000021900702,"threshold_uncertainty_score":0.47874185},"labels":[],"label_agreement":null},{"id":"W2791182737","doi":"10.1155/2018/2493401","title":"Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Task (project management); Point (geometry); Mathematical optimization; Jump; Optimization problem; Order (exchange); Operations research; Engineering; Algorithm; Systems engineering; Mathematics","score_opus":0.009617242398345442,"score_gpt":0.2282634823875989,"score_spread":0.21864623998925345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791182737","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":[0.16898444,0.00010422717,0.8303537,0.000050824936,0.00018721102,0.0001716641,0.00001038564,0.00009845703,0.000039133243],"genre_scores_gemma":[0.7698116,0.000048829534,0.2299272,0.000023294211,0.000081803846,0.000012836352,0.00007173465,0.000018847917,0.000003881697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924946,0.000008464255,0.00043030555,0.00008979945,0.00009907779,0.00012289405],"domain_scores_gemma":[0.9993704,0.00005291551,0.00016249211,0.000091845366,0.00028276502,0.00003955871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015678703,0.00010048757,0.00015037917,0.000121060184,0.00006372701,0.0000067450283,0.00010238534,0.000070520866,0.000006742942],"category_scores_gemma":[0.000028245255,0.00009999977,0.00007322914,0.00009080991,0.000049405044,0.0002485745,5.221072e-7,0.00011732502,0.000001027844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016224988,0.00004067479,0.00045133076,0.000044119894,0.000027547023,0.0000011965386,0.00021019999,0.98519444,0.0031654423,0.0001628455,0.000027401713,0.01051252],"study_design_scores_gemma":[0.0011603758,0.00032147032,0.0045594536,0.00004325343,0.000050784867,0.0000049866526,0.0001455884,0.8899906,0.102375045,0.00051726936,0.000670824,0.00016031627],"about_ca_topic_score_codex":7.802126e-7,"about_ca_topic_score_gemma":0.0000075160083,"teacher_disagreement_score":0.60082716,"about_ca_system_score_codex":0.00006398296,"about_ca_system_score_gemma":0.00003392475,"threshold_uncertainty_score":0.40778723},"labels":[],"label_agreement":null},{"id":"W2791780736","doi":"10.1109/itsc.2017.8317731","title":"Trajectory prediction of traffic agents at urban intersections through learned interactions","year":2017,"lang":"en","type":"article","venue":"","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":"U.S. Department of Transportation","keywords":"Intersection (aeronautics); Computer science; Trajectory; Task (project management); Artificial intelligence; Dynamic Bayesian network; Bayesian network; Machine learning; Object (grammar); Artificial neural network; Tracking (education); Transport engineering; Engineering","score_opus":0.034470821403833805,"score_gpt":0.2623047870176779,"score_spread":0.22783396561384409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791780736","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":[0.9458458,0.000032073305,0.004025217,0.0001292341,0.0008533394,0.00009875387,0.000020005054,0.0008080502,0.048187546],"genre_scores_gemma":[0.9968008,0.00004836668,0.00024064333,0.000009425836,0.000042270425,0.000014803685,0.000008121831,0.00001650338,0.002819108],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995053,0.000008991989,0.00018619653,0.00012046719,0.000049495848,0.00012959189],"domain_scores_gemma":[0.9994943,0.000017401024,0.00005333599,0.00039358903,0.000017624347,0.000023737382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000444651,0.000092228714,0.00011934523,0.00005886806,0.00030020092,0.000010134222,0.00017793196,0.000107980886,0.0005197611],"category_scores_gemma":[0.000018087616,0.00009379997,0.00007581168,0.000036069374,0.000118719894,0.00026971434,0.000042989144,0.00020579477,0.000072341565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003785999,0.0013883016,0.08292966,0.00054138555,0.0033560647,0.000047279384,0.028229697,0.34179986,0.12934464,0.012133655,0.25158724,0.1482636],"study_design_scores_gemma":[0.0023087491,0.0003496533,0.37709087,0.00013985792,0.00021260075,0.00011330445,0.0021420992,0.42114502,0.07020705,0.0004888299,0.12508816,0.00071380194],"about_ca_topic_score_codex":0.000025234729,"about_ca_topic_score_gemma":0.00033756226,"teacher_disagreement_score":0.29416123,"about_ca_system_score_codex":0.00010889232,"about_ca_system_score_gemma":0.0000070202605,"threshold_uncertainty_score":0.569102},"labels":[],"label_agreement":null},{"id":"W2795522097","doi":"10.1007/978-3-319-89656-4_44","title":"Decision Assist for Self-driving Cars","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Visibility; Reinforcement learning; Motion planning; Self driving; State (computer science); Artificial intelligence; Path (computing); Real-time computing; Machine learning; Algorithm; Transport engineering; Robot; Engineering","score_opus":0.008075547271338426,"score_gpt":0.2206067179341335,"score_spread":0.2125311706627951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795522097","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015420299,0.00024408314,0.9937361,0.00005653694,0.0016037212,0.00025588158,0.0000056217154,0.00066896726,0.0018870643],"genre_scores_gemma":[0.49186587,0.000038533606,0.5072331,0.000167496,0.0004831239,0.000012674489,0.0000044707103,0.00006623037,0.00012850287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998534,0.000003278854,0.00028112507,0.0005545335,0.00022263055,0.00040441038],"domain_scores_gemma":[0.9989029,0.00034994297,0.000059519796,0.00054622866,0.000081414546,0.00005995037],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003600012,0.00029815477,0.000314933,0.00036555293,0.00017849835,0.00006433808,0.00085325754,0.0005044922,0.00003181275],"category_scores_gemma":[0.000043126398,0.00028619447,0.00008642671,0.00015361777,0.00036620325,0.000112398,0.00022622493,0.00047659857,0.000038705108],"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":[0.000003834052,0.000007644012,0.0001252922,0.000040287512,0.000022363314,0.000014093822,0.0002915742,0.051487762,0.00009375412,0.0020524843,0.00012929825,0.94573164],"study_design_scores_gemma":[0.00021490011,0.00011203214,0.00033162074,0.00030274707,0.000016303438,0.00003328639,1.16351366e-7,0.84100074,0.0019173699,0.14267355,0.012879053,0.0005182538],"about_ca_topic_score_codex":0.0000011139426,"about_ca_topic_score_gemma":0.00007362705,"teacher_disagreement_score":0.9452134,"about_ca_system_score_codex":0.00029260517,"about_ca_system_score_gemma":0.000098647586,"threshold_uncertainty_score":0.999959},"labels":[],"label_agreement":null},{"id":"W2796327494","doi":"10.4271/2018-01-0531","title":"Driver Response Time to Cyclist Path Intrusions","year":2018,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Autonomous Vehicle Technology and Safety","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":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Computer science; Path (computing); Response time; Computer security; Computer network; Computer graphics (images)","score_opus":0.00605297555469167,"score_gpt":0.22000401530587446,"score_spread":0.2139510397511828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796327494","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":[0.87164026,0.00015422683,0.000009710176,0.009136984,0.0003928931,0.001222554,0.00014805462,0.014639922,0.1026554],"genre_scores_gemma":[0.9896564,0.00009403922,0.0047674053,0.0024909368,0.00025036154,0.00028529603,0.000029368568,0.0002143136,0.0022118664],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9955761,0.00018939476,0.0010697077,0.0011930646,0.00063513464,0.0013366072],"domain_scores_gemma":[0.9964604,0.00058224075,0.000109888744,0.0021212215,0.00012860294,0.0005976534],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009326198,0.0009024108,0.0009786007,0.00040973848,0.0006668481,0.00007833824,0.0015580439,0.0014983125,0.0035991275],"category_scores_gemma":[0.0010216516,0.00083429524,0.00038828497,0.0012485716,0.0018009532,0.00037919087,0.00076980086,0.0017096319,0.0067749964],"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":[0.0008710284,0.00017791642,0.00006160259,0.000016209899,0.000056376048,0.00008139545,0.000055642213,0.000041820313,0.96071106,0.005342785,0.025648938,0.0069352468],"study_design_scores_gemma":[0.00045783643,0.0014159984,0.7182924,0.00012858116,0.00005058283,0.00009771448,0.000037417594,9.4263834e-7,0.0006972224,0.0013730053,0.27663368,0.0008146027],"about_ca_topic_score_codex":0.000010723057,"about_ca_topic_score_gemma":0.010184449,"teacher_disagreement_score":0.9600138,"about_ca_system_score_codex":0.000521943,"about_ca_system_score_gemma":0.00009839634,"threshold_uncertainty_score":0.99979794},"labels":[],"label_agreement":null},{"id":"W2805439952","doi":"10.1109/tits.2019.2901817","title":"Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice","year":2019,"lang":"en","type":"preprint","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":48,"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; Perception; Computer science; Human–computer interaction; Demographics; Transport engineering; Data science; Engineering; Psychology; Sociology","score_opus":0.03325595422192322,"score_gpt":0.2619121690180029,"score_spread":0.22865621479607967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805439952","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":[0.20670228,0.000997702,0.78834885,0.000027398763,0.0013058423,0.0010310075,0.00082965643,0.00048014542,0.0002771252],"genre_scores_gemma":[0.99779665,0.0012820848,0.00031143226,0.000017287623,0.000015268406,0.00018526636,0.00012446873,0.00009879357,0.00016874229],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763685,0.0003988221,0.00086822326,0.0005159008,0.00030817158,0.00027200454],"domain_scores_gemma":[0.9971526,0.0015847256,0.00034509538,0.00059720397,0.00023431034,0.00008606332],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011967544,0.0004952931,0.00074318453,0.00038886978,0.000076254546,0.000054957673,0.00027892756,0.00064140855,0.000032459742],"category_scores_gemma":[0.000014060661,0.00046460386,0.00013441675,0.00021437039,0.00016617065,0.00022786112,0.0000010695234,0.0011128564,0.000027031261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012268242,0.00018720415,0.000941096,0.0012504486,0.0011247237,0.000017989065,0.0030675035,0.9825237,0.0001951684,0.00055348256,0.000021199497,0.008890709],"study_design_scores_gemma":[0.01025136,0.005880784,0.166803,0.021347452,0.0091032535,0.00094335235,0.04936965,0.3969989,0.31586948,0.0014413259,0.00878601,0.013205428],"about_ca_topic_score_codex":0.00093378767,"about_ca_topic_score_gemma":0.00059731567,"teacher_disagreement_score":0.79109436,"about_ca_system_score_codex":0.00013953217,"about_ca_system_score_gemma":0.000148972,"threshold_uncertainty_score":0.9997806},"labels":[],"label_agreement":null},{"id":"W2806799977","doi":"10.1109/icoa.2018.8370584","title":"Route planning for electric vehicle efficiency using the Bellman-Ford algorithm on an embedded GPU","year":2018,"lang":"en","type":"article","venue":"2018 4th International Conference on Optimization and Applications (ICOA)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Dijkstra's algorithm; Traverse; Computer science; Algorithm; Plan (archaeology); Dynamic programming; Process (computing); Enhanced Data Rates for GSM Evolution; Computational complexity theory; Efficient energy use; Energy (signal processing); Work (physics); Shortest path problem; Artificial intelligence; Engineering","score_opus":0.03783492986209856,"score_gpt":0.3043775762055602,"score_spread":0.26654264634346164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806799977","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":[0.019186916,0.000024589572,0.97282326,0.000356078,0.00015214126,0.00051423605,0.000042104417,0.0002818128,0.006618837],"genre_scores_gemma":[0.98181397,0.000052366267,0.016971577,0.00028675902,0.00025462182,0.00022695384,0.000103749444,0.000028138063,0.00026184804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991655,0.000016354212,0.00022145794,0.0002633386,0.000140439,0.00019291669],"domain_scores_gemma":[0.99939066,0.00004997066,0.000078770165,0.00025153707,0.00017772891,0.00005133149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014736787,0.0001525704,0.00010863572,0.00015530841,0.0004357643,0.00008240187,0.00031747035,0.000119493154,0.00007739282],"category_scores_gemma":[0.000012518741,0.00013289749,0.000028842955,0.0002098606,0.000111673085,0.0001435847,0.000027228638,0.00015810582,0.000018959397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006919652,0.00021067481,0.00012426442,0.000013291209,0.00009076947,5.138034e-7,0.00062666595,0.60302526,0.0048608207,0.298224,0.00041801037,0.092336565],"study_design_scores_gemma":[0.0003094817,0.00014452865,0.00007884951,0.000015497084,0.000012372547,0.0000047923013,0.00014922438,0.9929607,0.0025837922,0.0012095758,0.002373912,0.00015726946],"about_ca_topic_score_codex":0.0000048104034,"about_ca_topic_score_gemma":0.0000026793266,"teacher_disagreement_score":0.96262705,"about_ca_system_score_codex":0.00007646468,"about_ca_system_score_gemma":0.00003720072,"threshold_uncertainty_score":0.5419402},"labels":[],"label_agreement":null},{"id":"W2806907466","doi":"10.1155/2018/4129582","title":"Advances in Traffic Safety Methodologies and Technologies","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transport engineering; Computer science; Engineering; Risk analysis (engineering); Business","score_opus":0.010050933757387135,"score_gpt":0.260758552330311,"score_spread":0.25070761857292384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806907466","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":[0.9572315,0.011628151,0.0301362,0.00018148648,0.0002534526,0.000077498684,0.000003539871,0.0003049539,0.0001832158],"genre_scores_gemma":[0.94222736,0.017578993,0.0401531,0.0000056643094,0.000019931142,0.0000022903698,0.000001355432,0.000009617185,0.0000016841025],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99929196,0.000014113641,0.00040387214,0.00008299137,0.000070842936,0.00013620348],"domain_scores_gemma":[0.9996639,0.000085858024,0.00010931016,0.0000722774,0.000053201235,0.000015450732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002285572,0.00009945103,0.00022282901,0.00020683774,0.000054533513,0.0000027904941,0.000101111335,0.0001292405,0.000004112142],"category_scores_gemma":[0.000043647968,0.00008811638,0.000032785127,0.00021035592,0.00016629482,0.0006144038,0.0000025874776,0.00025731043,7.293236e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015831465,0.000010500224,0.0013081786,0.000049836435,0.000012115389,0.000019313635,0.0012061455,0.14181867,0.0032277163,0.0008254848,0.00000261868,0.8513611],"study_design_scores_gemma":[0.0071618524,0.002378257,0.69390094,0.0007437956,0.00016433866,0.00023534869,0.022587221,0.004726058,0.11197704,0.08256143,0.072252974,0.0013107178],"about_ca_topic_score_codex":1.8130248e-7,"about_ca_topic_score_gemma":0.0000849837,"teacher_disagreement_score":0.8500504,"about_ca_system_score_codex":0.000033667202,"about_ca_system_score_gemma":0.000010572195,"threshold_uncertainty_score":0.35932818},"labels":[],"label_agreement":null},{"id":"W2807069029","doi":"10.1109/plans.2018.8373527","title":"Driver behavior assessment based on loosely coupled GPS/INS integration in harsh environment","year":2018,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Global Positioning System; Computer science; Operating system","score_opus":0.008451139500559923,"score_gpt":0.22559329504283726,"score_spread":0.21714215554227734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807069029","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":[0.9093663,0.0000050285853,0.08320483,0.00016295709,0.00012090263,0.00026918887,0.000004177492,0.00042712336,0.0064395056],"genre_scores_gemma":[0.9955019,0.000008621468,0.004036636,0.00012513649,0.000021203552,0.00007356719,0.000016898697,0.000017995622,0.00019807684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935436,0.000013570535,0.00018008989,0.0001683695,0.00010216414,0.00018143846],"domain_scores_gemma":[0.9996839,0.000020473568,0.00001773703,0.00023928167,0.0000073432147,0.00003123792],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011877158,0.0001282644,0.00011877544,0.00010969154,0.000042879226,0.000008837673,0.00010574858,0.0001540771,0.0011775062],"category_scores_gemma":[0.0000030505491,0.00011933558,0.00002909629,0.00007288022,0.000071901886,0.00006584327,0.000016814629,0.0002208993,0.0002809398],"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":[0.00017762046,0.0033151587,0.24998575,0.00005547959,0.00012480814,0.00018966701,0.0014091637,0.21089344,0.19910266,0.036354568,0.0034289449,0.29496273],"study_design_scores_gemma":[0.0005034389,0.00016606889,0.24658445,0.000013214429,0.000010587434,7.246989e-7,0.000043740314,0.7376312,0.014068537,0.00006887764,0.00074950774,0.00015963937],"about_ca_topic_score_codex":0.000017613625,"about_ca_topic_score_gemma":0.000118605865,"teacher_disagreement_score":0.52673775,"about_ca_system_score_codex":0.0002751329,"about_ca_system_score_gemma":0.000014413291,"threshold_uncertainty_score":0.99973553},"labels":[],"label_agreement":null},{"id":"W2807847293","doi":"10.1155/2018/1547276","title":"Occluded Street Objects Perception Algorithm of Intelligent Vehicles Based on 3D Projection Model","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer vision; Visibility; Computer science; Perception; Artificial intelligence; Projection (relational algebra); Affine transformation; Perspective (graphical); Transformation (genetics); Front (military); Algorithm; Engineering; Mathematics","score_opus":0.009096236534809975,"score_gpt":0.23734850333569354,"score_spread":0.22825226680088356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807847293","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":[0.6326696,0.000019181654,0.36689302,0.000017406428,0.00014520594,0.00009200136,0.000010830439,0.00006535772,0.000087324544],"genre_scores_gemma":[0.93382853,0.00007535082,0.06597558,0.000018008799,0.00006267156,0.0000031038244,0.000014330787,0.000017458964,0.0000049854707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991234,0.000012931403,0.0004680877,0.000091645044,0.00018546869,0.00011845299],"domain_scores_gemma":[0.99944633,0.000021021593,0.00020001551,0.00009993543,0.00019887646,0.000033817796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001452603,0.00011599363,0.0001887487,0.00022602794,0.000042601972,0.000003038985,0.00008081097,0.00012167861,0.000012528507],"category_scores_gemma":[0.000008119307,0.000110374735,0.00008156842,0.00016154711,0.000057021414,0.00023020669,7.5109875e-7,0.00021748665,0.0000022262532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019501137,0.00006888561,0.00039376324,0.000036707086,0.000023850316,0.0000032803243,0.0008730952,0.8298502,0.031561695,0.00004873202,0.00000823863,0.13693656],"study_design_scores_gemma":[0.0008796202,0.00096089445,0.043928556,0.00014509767,0.00005526825,0.0000032971943,0.00083160866,0.8587799,0.093725614,0.00050256035,0.000038540227,0.00014905809],"about_ca_topic_score_codex":0.0000024779174,"about_ca_topic_score_gemma":0.000052331685,"teacher_disagreement_score":0.30115885,"about_ca_system_score_codex":0.00009315981,"about_ca_system_score_gemma":0.000042061685,"threshold_uncertainty_score":0.4500951},"labels":[],"label_agreement":null},{"id":"W2883700531","doi":"10.1155/2018/6060924","title":"Urban Motion Planning Framework Based on N-Bézier Curves Considering Comfort and Safety","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Electronic Components and Systems for European Leadership","keywords":"Automotive industry; Advanced driver assistance systems; Motion planning; Bézier curve; Planner; Simulation; Parametric statistics; Trajectory; Process (computing); Computer science; Work (physics); Engineering; Transport engineering; Robot; Artificial intelligence","score_opus":0.007614992136325769,"score_gpt":0.2334681808941124,"score_spread":0.22585318875778662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883700531","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":[0.68786013,0.00067996,0.31038442,0.00021040793,0.00037473073,0.00009720671,0.0000072506587,0.00012790639,0.00025800936],"genre_scores_gemma":[0.9839173,0.0001801981,0.01564088,0.00014744767,0.000085409796,0.000001495327,0.0000074093277,0.000016750388,0.0000031025659],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9993186,0.000009851822,0.00035404053,0.00008042604,0.00011614779,0.00012090608],"domain_scores_gemma":[0.99960434,0.000082314255,0.00012459738,0.00008179279,0.00006126154,0.000045699257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015197671,0.00010470491,0.00017655046,0.00010922563,0.00007663235,0.000005106849,0.000051117262,0.00010705647,0.000018592176],"category_scores_gemma":[0.000024327492,0.000102376674,0.00004082728,0.00010171012,0.000066834844,0.00021647451,7.620495e-7,0.00031753074,0.0000011845854],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054174056,0.00005199623,0.04295592,0.00029233156,0.000095544914,0.000072663104,0.0021770925,0.91237915,0.0031341403,0.0018084726,0.00024135037,0.036249585],"study_design_scores_gemma":[0.00232031,0.0008592097,0.9374303,0.003262263,0.00013531868,0.000041453895,0.0005089187,0.02985668,0.016357925,0.0037595509,0.0049836207,0.0004844463],"about_ca_topic_score_codex":3.3942797e-7,"about_ca_topic_score_gemma":0.000002536255,"teacher_disagreement_score":0.8944744,"about_ca_system_score_codex":0.000035436886,"about_ca_system_score_gemma":0.00001349277,"threshold_uncertainty_score":0.41747996},"labels":[],"label_agreement":null},{"id":"W2886750067","doi":"10.1155/2018/8372108","title":"Research on a Conflict Early Warning System Based on the Active Safety Concept","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"CarSim; Warning system; Reliability (semiconductor); Active safety; Early warning system; Lane departure warning system; Computer science; Traffic conflict; Simulation; Engineering; Transport engineering; Automotive engineering; Computer security; Real-time computing; Vehicle dynamics; Traffic congestion; Floating car data; Telecommunications","score_opus":0.0199470914903474,"score_gpt":0.28315806167382673,"score_spread":0.26321097018347933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886750067","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":[0.98100495,0.00004311184,0.015713802,0.000405533,0.00031601536,0.00020024879,0.000015673093,0.00012463288,0.0021760038],"genre_scores_gemma":[0.99933887,0.000015112613,0.00043715994,0.00004285942,0.00012043808,0.000006305323,0.00000386563,0.000020237067,0.000015184602],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99895966,0.00007560047,0.00035109933,0.0000978774,0.00031754246,0.00019822863],"domain_scores_gemma":[0.99903816,0.00036242715,0.0001341091,0.00015187486,0.00026852477,0.0000448884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005363282,0.00010548403,0.00017176528,0.00017508726,0.0002043515,0.000008929864,0.00017071019,0.00010978966,0.000027597425],"category_scores_gemma":[0.000026153908,0.00007599097,0.0000706324,0.00026383932,0.00014019926,0.000147998,9.80376e-7,0.000772595,0.000019143754],"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":[0.0020779104,0.000055980676,0.00028162537,0.00004766017,0.00011863213,0.000069756774,0.007323035,0.9358357,0.006681408,0.018755833,0.000109838555,0.028642582],"study_design_scores_gemma":[0.0065356097,0.0071136886,0.6676715,0.002205422,0.00012392017,0.000028172197,0.019808447,0.053220503,0.22401409,0.0007164372,0.017857019,0.00070517475],"about_ca_topic_score_codex":0.000002560032,"about_ca_topic_score_gemma":0.000008251381,"teacher_disagreement_score":0.8826152,"about_ca_system_score_codex":0.00017769185,"about_ca_system_score_gemma":0.00004157269,"threshold_uncertainty_score":0.33565837},"labels":[],"label_agreement":null},{"id":"W2890572967","doi":"10.1049/iet-its.2018.5256","title":"Dynamic integration and online evaluation of vision‐based lane detection algorithms","year":2018,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","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":"","keywords":"Computer science; Computer vision; Artificial intelligence; Algorithm","score_opus":0.01900037167858269,"score_gpt":0.271742487796949,"score_spread":0.2527421161183663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890572967","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":[0.6057753,0.00045977946,0.39248157,0.0000150889855,0.0004874767,0.00032105632,0.00003520653,0.00025323566,0.0001712663],"genre_scores_gemma":[0.9991598,0.000065541135,0.0005533618,0.0000045191914,0.00004133111,0.000026523023,0.00010619098,0.000021313517,0.00002143037],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900407,0.00003760917,0.00041997663,0.0001714568,0.00023503954,0.00013182552],"domain_scores_gemma":[0.99949217,0.000022283677,0.00006287075,0.00019533109,0.00019237955,0.000034970315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055170746,0.00013825021,0.00020058449,0.00016260985,0.000048825663,0.0000056771687,0.00008056281,0.00019751799,0.000035613382],"category_scores_gemma":[0.00000825733,0.00012922962,0.000042845037,0.0001790274,0.000095615855,0.0000778168,0.0000028277964,0.00014043468,0.000013137665],"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":[0.00013099451,0.00028339744,0.0028754647,0.00055013987,0.00024967451,0.000005866402,0.0017981216,0.09460438,0.09198434,0.0004859012,0.00003268417,0.806999],"study_design_scores_gemma":[0.00023314623,0.00018621741,0.008757558,0.000113726695,0.00007263409,0.000008508261,0.00022896925,0.9511056,0.03876588,0.000086470616,0.0003140775,0.00012721893],"about_ca_topic_score_codex":0.00007423334,"about_ca_topic_score_gemma":0.000834534,"teacher_disagreement_score":0.8565012,"about_ca_system_score_codex":0.00010072938,"about_ca_system_score_gemma":0.000021854023,"threshold_uncertainty_score":0.52698314},"labels":[],"label_agreement":null},{"id":"W2890965859","doi":"10.3390/s18092952","title":"An Autonomous Vehicle Navigation System Based on Inertial and Visual Sensors","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","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":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Heilongjiang Province; China Scholarship Council","keywords":"Inertial measurement unit; Inertial navigation system; Gyroscope; Navigation system; Computer science; Reliability (semiconductor); Wind triangle; Computer vision; Artificial intelligence; Inertial frame of reference; Engineering; Simulation; Robot; Mobile robot; Aerospace engineering","score_opus":0.004866560607590487,"score_gpt":0.22063298531361566,"score_spread":0.21576642470602517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890965859","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":[0.99539334,0.000011563402,0.0004243089,0.0000695605,0.0002442721,0.00014158404,0.0000076013466,0.0017260539,0.0019817166],"genre_scores_gemma":[0.99935067,9.3064085e-7,0.0003226303,0.000039260445,0.00019398837,0.00000647627,0.000013206357,0.000041414656,0.000031431828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912363,0.000049346043,0.0002013731,0.00025015758,0.00010644742,0.00026904163],"domain_scores_gemma":[0.9995739,0.000037102418,0.00003179661,0.00023027066,0.000035651934,0.00009126357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014567745,0.0001771672,0.0001713341,0.00011183376,0.00015539539,0.000022435082,0.00008078508,0.0002337572,0.00001603848],"category_scores_gemma":[0.000009758812,0.00017935084,0.000029516075,0.0001431351,0.0001540953,0.00007240594,0.000011972665,0.00020710938,0.00014285809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078612997,0.0004368577,0.015102666,0.00069295545,0.0002620632,0.0005199302,0.0044712117,0.6129168,0.23253421,0.008601377,0.000513827,0.12316193],"study_design_scores_gemma":[0.0003593707,0.00029578374,0.0067194295,0.000035065736,0.000012808164,0.000022477254,0.00023244307,0.94906,0.042712986,0.0000131111965,0.0003287229,0.00020779402],"about_ca_topic_score_codex":0.000022083253,"about_ca_topic_score_gemma":0.0000063543066,"teacher_disagreement_score":0.33614317,"about_ca_system_score_codex":0.00010429388,"about_ca_system_score_gemma":0.000015159773,"threshold_uncertainty_score":0.7313715},"labels":[],"label_agreement":null},{"id":"W2891395548","doi":"10.1177/0361198118795004","title":"In-Vehicle Passing Collision Warning System for Two-Lane Highways Considering Driver Characteristics","year":2018,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Collision; Acceleration; Simulation; Computer science; Global Positioning System; Real-time computing; Position (finance); Computer security; Telecommunications","score_opus":0.0496854290855081,"score_gpt":0.33488322778253005,"score_spread":0.28519779869702194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891395548","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":[0.9841926,0.000091328126,0.012787687,0.0008962659,0.0008852699,0.0008035284,0.000057723464,0.00015258395,0.00013299446],"genre_scores_gemma":[0.99617165,0.00015354926,0.00311697,0.000017402428,0.00030677737,0.0000665469,0.000011479034,0.000066214816,0.00008941462],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9959963,0.00037547507,0.0012902645,0.00029747302,0.0011766355,0.0008638301],"domain_scores_gemma":[0.99652654,0.0009033429,0.0002506591,0.0003436332,0.0017782528,0.00019760063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003424737,0.00022194487,0.00047015474,0.0009063115,0.00066021894,0.000084144995,0.0006509637,0.00024974166,0.00005247672],"category_scores_gemma":[0.00011885728,0.00018736298,0.0001991802,0.0012302139,0.00060427614,0.0005413209,0.000007031064,0.0017834244,0.000021565864],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0060305125,0.00054213795,0.62610966,0.003704612,0.0007746976,0.0010705332,0.018758023,0.027716251,0.23122886,0.028896987,0.012872003,0.042295735],"study_design_scores_gemma":[0.004833812,0.0009172259,0.88519377,0.0016062198,0.000074582604,0.0000044777576,0.0033431642,0.033930786,0.05298099,0.0018854891,0.014727323,0.0005021804],"about_ca_topic_score_codex":0.0008293733,"about_ca_topic_score_gemma":0.008290338,"teacher_disagreement_score":0.2590841,"about_ca_system_score_codex":0.0004728508,"about_ca_system_score_gemma":0.0002711311,"threshold_uncertainty_score":0.7748191},"labels":[],"label_agreement":null},{"id":"W2891742881","doi":"10.1109/tte.2018.2870819","title":"A Cognitive Advanced Driver Assistance Systems Architecture for Autonomous-Capable Electrified Vehicles","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Transportation Electrification","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Canada Excellence Research Chairs, Government of Canada; Eurostars; Canada Research Chairs","keywords":"Architecture; Cognitive architecture; Acceleration; Computer science; Advanced driver assistance systems; Control (management); Energy management; Battery (electricity); Energy (signal processing); Automotive engineering; Cognition; Simulation; Engineering; Artificial intelligence; Power (physics)","score_opus":0.008988577682531607,"score_gpt":0.22578201516222493,"score_spread":0.21679343747969332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891742881","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":[0.18659718,0.00016018095,0.8103213,0.000096028314,0.00032869104,0.00096882036,0.00017871159,0.0010998393,0.0002492855],"genre_scores_gemma":[0.99686724,0.00011422519,0.0014682047,0.00005759334,0.00006282315,0.0008658368,0.00012483938,0.00006968107,0.00036953454],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998496,0.000033836528,0.00045733398,0.0004244061,0.00016477765,0.00042363335],"domain_scores_gemma":[0.99916303,0.00015571594,0.00010624324,0.00025341712,0.00024848612,0.0000731067],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016121031,0.00027843748,0.00026175828,0.00028340347,0.00040776926,0.000028881905,0.00016032331,0.00033784992,0.000023677203],"category_scores_gemma":[0.0000052368528,0.00031361694,0.00012150838,0.0005610062,0.00013863614,0.00022304148,1.2715426e-8,0.00041708612,0.000049402064],"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":[0.00092207006,0.00023767122,0.000024137857,0.00015497599,0.00028576504,0.0000017588893,0.001302646,0.11517684,0.73399436,0.003085,0.00015823914,0.14465651],"study_design_scores_gemma":[0.0014359272,0.00045187463,0.0020499434,0.00006397936,0.00014448694,0.0000039736165,0.00013705879,0.032210346,0.9605572,0.00053412846,0.0019731494,0.00043790968],"about_ca_topic_score_codex":0.000010610781,"about_ca_topic_score_gemma":0.0001765423,"teacher_disagreement_score":0.8102701,"about_ca_system_score_codex":0.00017109467,"about_ca_system_score_gemma":0.00007390469,"threshold_uncertainty_score":0.9999316},"labels":[],"label_agreement":null},{"id":"W2895850288","doi":"10.1109/ivs.2018.8500501","title":"Modeling and Predicting Vehicle Motion Activities by Using And-Or Graph","year":2018,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Computer science; Motion (physics); Hidden Markov model; Graph; Property (philosophy); Data modeling; Artificial intelligence; Markov process; Markov chain; Markov model; Machine learning; Theoretical computer science; Mathematics; Database","score_opus":0.012840395803813411,"score_gpt":0.21563591365061593,"score_spread":0.20279551784680253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895850288","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":[0.8721925,0.00016260355,0.12650755,0.000022161232,0.00004195873,0.000044043478,0.000002324389,0.000534758,0.00049211475],"genre_scores_gemma":[0.99804,0.00006304046,0.0017897206,0.000019299974,0.000034238587,0.0000020533612,7.7454825e-7,0.000014061792,0.000036819496],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996109,0.0000061081078,0.00009312249,0.00011105598,0.000034416807,0.00014438604],"domain_scores_gemma":[0.9998712,0.000015360909,0.000008384029,0.00006998488,0.0000075327566,0.000027540662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000074521064,0.0000794653,0.00008264177,0.000048475686,0.0001456205,0.000015450316,0.000030823758,0.00010277954,0.000014646402],"category_scores_gemma":[0.0000057555444,0.000071487164,0.000008666009,0.000059097296,0.00008427059,0.00019287078,0.000030147316,0.00009999712,8.376979e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012748032,0.000080598715,0.06144733,0.00034429843,0.00033867272,0.000010289304,0.005126425,0.08404738,0.4512203,0.0038555628,0.00036587054,0.3930358],"study_design_scores_gemma":[0.00013175077,0.000023126657,0.00016678397,0.000011679301,0.000008336042,0.000012237719,0.00032908388,0.9859383,0.012930376,0.0003402061,0.000020626649,0.00008749782],"about_ca_topic_score_codex":0.000041268937,"about_ca_topic_score_gemma":0.000027942933,"teacher_disagreement_score":0.90189093,"about_ca_system_score_codex":0.00001532389,"about_ca_system_score_gemma":0.0000033936099,"threshold_uncertainty_score":0.29151618},"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":"W2898966489","doi":"10.1007/1345_2018_45","title":"Assessment of GNSS and Map Integration for Lane-Level Applications in the Scope of Intelligent Transportation Location Based Services (ITLBS)","year":2018,"lang":"en","type":"book-chapter","venue":"International Association of Geodesy symposia","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"GNSS applications; Computer science; Sensor fusion; Kalman filter; Map matching; Context (archaeology); Global Positioning System; Intelligent transportation system; Identification (biology); Real-time computing; Computer vision; Artificial intelligence; Geography; Engineering; Transport engineering; Telecommunications","score_opus":0.010318812016666014,"score_gpt":0.2482527520804263,"score_spread":0.23793394006376029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898966489","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":[0.08851522,0.002106281,0.8030183,0.0031243458,0.0013526494,0.00764114,0.007303309,0.00036404314,0.086574756],"genre_scores_gemma":[0.99368274,0.00020321397,0.002656139,0.000028728331,0.000048077218,0.0001276401,0.0017455172,0.000024852563,0.0014831108],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9987059,0.000017052196,0.0007124725,0.0001651486,0.00031327218,0.00008613985],"domain_scores_gemma":[0.99835604,0.00026867486,0.000661346,0.00015839473,0.00054251065,0.000013051242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047049322,0.0001653408,0.00027729044,0.0002582236,0.000028105951,0.00000981307,0.00026702616,0.00031822396,0.00004053941],"category_scores_gemma":[0.000012199226,0.00015409706,0.00008428463,0.000062453044,0.000056545116,0.00009270968,0.000008030998,0.00016101041,0.0000017065827],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010704104,0.00031923706,0.018646233,0.0023261912,0.001024933,5.1434205e-7,0.0029174858,0.02119054,0.00745717,0.9296191,0.0005030766,0.015888495],"study_design_scores_gemma":[0.004924859,0.0010523783,0.45885432,0.0048690075,0.0012614705,0.000003264775,0.0017845486,0.25933778,0.06475662,0.12467424,0.07655365,0.001927865],"about_ca_topic_score_codex":0.000044442426,"about_ca_topic_score_gemma":0.00060415885,"teacher_disagreement_score":0.9051675,"about_ca_system_score_codex":0.00025850278,"about_ca_system_score_gemma":0.00006159196,"threshold_uncertainty_score":0.62838954},"labels":[],"label_agreement":null},{"id":"W2899442714","doi":"10.1109/icra.2019.8794029","title":"Building a Winning Self-Driving Car in Six Months","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","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 Toronto","funders":"","keywords":"Simple (philosophy); Architecture; Competition (biology); Computer science; Set (abstract data type); ZEUS (particle detector); Autonomy; Self driving; Artificial intelligence; Real-time computing; Operations research; Simulation; Engineering; Transport engineering; Geography; Programming language; Political science","score_opus":0.00601490189063548,"score_gpt":0.21455828524298928,"score_spread":0.2085433833523538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899442714","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":[0.94218975,0.0006712754,0.029961547,0.000104238636,0.0007624867,0.00043409946,0.0000035037203,0.004247225,0.021625862],"genre_scores_gemma":[0.97414386,0.00010465062,0.025420915,0.000024777575,0.00006405234,0.000043683394,0.0000050332646,0.00007425716,0.000118797376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986102,0.000020975294,0.000395186,0.00041603934,0.00010542043,0.00045218458],"domain_scores_gemma":[0.9992151,0.000072069946,0.00005309596,0.00059927336,0.000016859469,0.000043600525],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002493518,0.00033774946,0.00047677002,0.0003153256,0.000035891186,0.000028990602,0.0004551779,0.00091424864,0.000050068244],"category_scores_gemma":[0.00002043,0.00037680182,0.00010388993,0.00014714514,0.000022069566,0.000081472754,0.0005871373,0.0016757521,0.000082104416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024318854,0.000022652514,0.041169446,0.0003483303,0.00014584212,0.00005586487,0.0006001832,0.94483304,0.001407557,0.004378821,0.00023952387,0.0067963176],"study_design_scores_gemma":[0.0003375773,0.0000135772025,0.016607773,0.00043686392,0.000032724056,0.000012147375,0.00010655734,0.973474,0.0030260757,0.0019076801,0.0032243351,0.0008206558],"about_ca_topic_score_codex":0.0000962865,"about_ca_topic_score_gemma":0.00009982737,"teacher_disagreement_score":0.03195407,"about_ca_system_score_codex":0.0003527746,"about_ca_system_score_gemma":0.00005578061,"threshold_uncertainty_score":0.9998684},"labels":[],"label_agreement":null},{"id":"W2901436583","doi":"10.1155/2018/5020648","title":"Identifying Driver Behavior in Preturning Maneuvers Using In-Vehicle CANbus Signals","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Categorization; Computer science; Consistency (knowledge bases); Brake; Acceleration; Steering wheel; Automotive engineering; Cluster (spacecraft); Simulation; Artificial intelligence; Engineering","score_opus":0.013508096370273627,"score_gpt":0.260578231652349,"score_spread":0.2470701352820754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901436583","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":[0.9943123,0.00015707208,0.0050858897,0.000011135949,0.00027055922,0.000092618095,0.0000019110496,0.0000373423,0.000031167532],"genre_scores_gemma":[0.9945719,0.000053694486,0.005302748,0.000008762126,0.000037815757,0.000002579117,0.0000018857695,0.000017209197,0.000003356855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991175,0.000013253552,0.0004900938,0.00008483725,0.000117142,0.00017716036],"domain_scores_gemma":[0.99968916,0.000020448353,0.00013801079,0.00006224219,0.000058001893,0.000032148702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015643811,0.00009470329,0.00018121583,0.00030125797,0.00003329577,0.0000063756825,0.00009258242,0.000102089754,0.000026277006],"category_scores_gemma":[0.000006480546,0.00010469586,0.00004721292,0.00026138537,0.000042745924,0.0005411106,0.0000013822266,0.00032263398,0.0000016111717],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008420113,0.00003633176,0.1304492,0.00003459816,0.000016773703,0.00024969835,0.004073137,0.5538007,0.30080163,0.000040196748,0.0000017401375,0.010411755],"study_design_scores_gemma":[0.0011123294,0.000092455164,0.9468479,0.0001935653,0.000036022026,0.000021808852,0.0010877457,0.0056579025,0.044415362,0.00034149573,0.00003622155,0.000157203],"about_ca_topic_score_codex":0.000015262924,"about_ca_topic_score_gemma":0.0005287702,"teacher_disagreement_score":0.8163987,"about_ca_system_score_codex":0.00016266265,"about_ca_system_score_gemma":0.000025699472,"threshold_uncertainty_score":0.4269373},"labels":[],"label_agreement":null},{"id":"W2903070283","doi":"","title":"Towards cognitive automotive environment modelling: reasoning based on vector representations.","year":2018,"lang":"en","type":"article","venue":"The European Symposium on Artificial Neural Networks","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Automotive industry; Cognition; Artificial intelligence; Cognitive science; Engineering; Psychology; Neuroscience","score_opus":0.017398290382706492,"score_gpt":0.22187418495784197,"score_spread":0.2044758945751355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903070283","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":[0.33142823,0.000087256274,0.44960138,0.0038819723,0.0019056858,0.0011986397,0.000041560947,0.0030364008,0.20881888],"genre_scores_gemma":[0.9979917,0.000021606354,0.00014416467,0.00069857517,0.0009134096,0.00001686088,0.00002231136,0.00009198388,0.00009941027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982184,0.00041013255,0.00032888286,0.00039982144,0.00020810598,0.00043464737],"domain_scores_gemma":[0.9991127,0.00019759226,0.000078778314,0.00048409266,0.000040702384,0.0000860951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048381995,0.00028849213,0.00018822828,0.00007833444,0.00047154477,0.000054245083,0.00032406172,0.00009470961,0.00007312637],"category_scores_gemma":[0.00002610872,0.0002327023,0.00010061635,0.00019357596,0.00035048465,0.000065134416,0.00007019824,0.00062289275,0.0005323837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016372174,0.000053926222,0.000022791784,0.0000019457464,0.00003018822,0.000026747588,0.00032348366,0.9828181,0.00027145157,0.0009329161,0.00030903617,0.0150456885],"study_design_scores_gemma":[0.0002066204,0.00031756898,0.0011334276,0.000042590655,0.000035902212,0.0000036443842,0.0000631595,0.9935078,0.0039823838,0.00011302264,0.0003462412,0.0002476577],"about_ca_topic_score_codex":0.000005403776,"about_ca_topic_score_gemma":0.000002215183,"teacher_disagreement_score":0.66656345,"about_ca_system_score_codex":0.00007296971,"about_ca_system_score_gemma":0.000008405331,"threshold_uncertainty_score":0.9489324},"labels":[],"label_agreement":null},{"id":"W2903311511","doi":"","title":"Partition-wise Recurrent Neural Networks for Point-based AIS Trajectory Classification.","year":2017,"lang":"en","type":"article","venue":"The European Symposium on Artificial Neural Networks","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Dalhousie University","funders":"","keywords":"Partition (number theory); Computer science; Trajectory; Artificial neural network; Recurrent neural network; Artificial intelligence; Pattern recognition (psychology); Mathematics; Combinatorics","score_opus":0.029185364418745058,"score_gpt":0.24359607627328295,"score_spread":0.2144107118545379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903311511","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":[0.57798624,0.0011906122,0.30558184,0.03632543,0.020046351,0.0056077978,0.00018871733,0.008082909,0.044990093],"genre_scores_gemma":[0.9972041,0.000053053864,0.000057257665,0.00060236,0.0017123369,0.00008968514,0.000065049804,0.0001339958,0.00008216314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775255,0.00031676373,0.0005873642,0.00047277287,0.00016541299,0.00070513424],"domain_scores_gemma":[0.9979059,0.00028146876,0.00024715372,0.0013555094,0.000065561864,0.00014441594],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.000816134,0.00040441082,0.00031124416,0.0000744333,0.0015191358,0.0002492867,0.0010433267,0.00017362708,0.000029087378],"category_scores_gemma":[0.00005453628,0.000332808,0.00025817048,0.00011407057,0.00040615705,0.00019273593,0.000085589185,0.00083415967,0.000069927264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020304014,0.000073263465,0.000182356,0.000012685113,0.000035408797,0.000013886613,0.00005695781,0.91375387,0.0007030438,0.0016337928,0.0053423755,0.07798933],"study_design_scores_gemma":[0.00039519966,0.00017390246,0.0073216,0.000029955554,0.0000550273,0.0000049309133,0.000021169022,0.9887026,0.000361704,0.00012309282,0.0024327938,0.00037803195],"about_ca_topic_score_codex":0.0000034747209,"about_ca_topic_score_gemma":0.000032230488,"teacher_disagreement_score":0.41921782,"about_ca_system_score_codex":0.00007032299,"about_ca_system_score_gemma":0.0000112834305,"threshold_uncertainty_score":0.9999124},"labels":[],"label_agreement":null},{"id":"W2904140126","doi":"10.1609/aaai.v33i01.3301978","title":"Crash to Not Crash: Learn to Identify Dangerous Vehicles Using a Simulator","year":2019,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Computer science; Crash; Classifier (UML); Driving simulator; Collision; Generator (circuit theory); Artificial intelligence; Simulation; Machine learning; Computer security","score_opus":0.0607103320586291,"score_gpt":0.30693622427405637,"score_spread":0.24622589221542726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904140126","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":[0.99193406,0.000009201681,0.003732287,0.00082556135,0.0004389342,0.00061187526,0.000009249541,0.0003132726,0.0021255733],"genre_scores_gemma":[0.99823946,0.00000720331,0.0010600536,0.000307168,0.000059178827,0.000017877433,2.992527e-7,0.00004153668,0.00026721388],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99827236,0.000008059749,0.0005039359,0.0004102864,0.00031462385,0.0004907642],"domain_scores_gemma":[0.9991084,0.000065517306,0.00010836992,0.00030651662,0.0002752602,0.00013593295],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00033577782,0.00026612057,0.00034225636,0.00020772518,0.00012970272,0.000095720265,0.0010065449,0.00019731074,0.00014720765],"category_scores_gemma":[0.00023064253,0.00023521364,0.00010359521,0.0006009845,0.00010749787,0.00017929699,0.00027728328,0.00042209873,0.0010176632],"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":[0.000086796106,0.000056278885,0.0020720216,0.000094944815,0.00003622422,7.8375126e-7,0.0014045519,0.03580067,0.8770632,0.04478036,0.00009258707,0.038511567],"study_design_scores_gemma":[0.000031755604,0.00015412853,0.001627439,0.00024297115,0.00002411371,0.0000030879012,0.00076459727,0.10612856,0.88450027,0.005757319,0.00040531714,0.0003604635],"about_ca_topic_score_codex":0.000028501217,"about_ca_topic_score_gemma":0.000014055107,"teacher_disagreement_score":0.07032789,"about_ca_system_score_codex":0.00011736085,"about_ca_system_score_gemma":0.00004224098,"threshold_uncertainty_score":0.99976015},"labels":[],"label_agreement":null},{"id":"W2904932247","doi":"10.1049/iet-its.2018.5351","title":"Hardware and software architecture of intelligent vehicles and road verification in typical traffic scenarios","year":2018,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":34,"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; China Postdoctoral Science Foundation","keywords":"Software; Intelligent transportation system; Embedded system; Architecture; Robustness (evolution); Engineering; Computer science; Transport engineering; Operating system","score_opus":0.011309880792679652,"score_gpt":0.211970443329904,"score_spread":0.20066056253722436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904932247","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":[0.9606397,0.0033782565,0.034863662,0.000046635574,0.00025195445,0.0004127323,0.000032887066,0.0003068435,0.00006730243],"genre_scores_gemma":[0.9987943,0.000670524,0.00035939596,0.000008810876,0.00005240453,0.000030269835,0.000021700975,0.000031527343,0.000031063304],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99875075,0.000029805884,0.0005520965,0.0002895974,0.00012554793,0.0002521894],"domain_scores_gemma":[0.9995292,0.000037556136,0.000053081978,0.0002541552,0.000047016954,0.00007902294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002241136,0.00021453763,0.0003613137,0.00018372477,0.00004380143,0.0000100602465,0.00015368266,0.00029797084,0.000018456223],"category_scores_gemma":[0.000011594681,0.0002048641,0.000047577945,0.00017853513,0.0002679824,0.00005900376,0.000011557762,0.00029719577,0.000012474744],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010849814,0.0004756923,0.17341118,0.005083021,0.00053744944,0.00010331135,0.03775945,0.18667248,0.007791074,0.0034486025,0.00017634207,0.5834564],"study_design_scores_gemma":[0.0028133325,0.0020994707,0.6323833,0.0039446373,0.00037018975,0.00056870846,0.008830576,0.22896153,0.06886422,0.0014112158,0.046305515,0.0034473212],"about_ca_topic_score_codex":0.00005273003,"about_ca_topic_score_gemma":0.00033483343,"teacher_disagreement_score":0.5800091,"about_ca_system_score_codex":0.000046559588,"about_ca_system_score_gemma":0.000019893607,"threshold_uncertainty_score":0.83541155},"labels":[],"label_agreement":null},{"id":"W2908482268","doi":"10.1155/2018/2195760","title":"Optimal Control of Overtaking Maneuver for Intelligent Vehicles","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"European Social Fund; Emberi Eroforrások Minisztériuma; Magyarország Kormánya; Magyar Tudományos Akadémia; Budapesti Műszaki és Gazdaságtudományi Egyetem","keywords":"Overtaking; Trajectory; Control theory (sociology); Acceleration; Computer science; Task (project management); Computation; Function (biology); Optimal control; Cluster analysis; Tracking (education); Trajectory optimization; Control (management); Motion (physics); Control engineering; Engineering; Mathematical optimization; Artificial intelligence; Mathematics; Algorithm","score_opus":0.007299307817449911,"score_gpt":0.23411904458148844,"score_spread":0.22681973676403852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908482268","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":[0.66350555,0.00014981245,0.33599675,0.000025956786,0.00019108473,0.000072726434,0.000008775713,0.00002490956,0.000024404711],"genre_scores_gemma":[0.9832401,0.000063881474,0.016583027,0.00001487803,0.00007405887,0.000002751389,0.0000026551152,0.000013291318,0.0000053332706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993525,0.000004096648,0.0004125356,0.000049719107,0.00007884969,0.00010226636],"domain_scores_gemma":[0.99954194,0.000046961093,0.0001753916,0.000057025845,0.00015496001,0.000023706965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000111047644,0.00007166893,0.00017937017,0.00008074446,0.000026431624,0.0000019870854,0.000078547746,0.00007036524,0.000012382402],"category_scores_gemma":[0.00000983271,0.00006766118,0.00008832388,0.000058004778,0.000048048623,0.00016697674,4.567468e-7,0.00010293861,7.954382e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009856387,0.00005024139,0.0028796154,0.00012098893,0.00018864307,0.000008438472,0.0017298564,0.839812,0.09742028,0.0025905157,0.00003426406,0.054179523],"study_design_scores_gemma":[0.0068582166,0.0024662414,0.22751908,0.000352325,0.00036789203,0.000034499662,0.0015854136,0.03713053,0.70975494,0.004500576,0.008946259,0.00048399012],"about_ca_topic_score_codex":4.887498e-7,"about_ca_topic_score_gemma":0.000009740369,"teacher_disagreement_score":0.80268145,"about_ca_system_score_codex":0.000025668414,"about_ca_system_score_gemma":0.000013591569,"threshold_uncertainty_score":0.2759143},"labels":[],"label_agreement":null},{"id":"W2908826808","doi":"10.18178/joig.5.2.64-67","title":"Enhanced HoG Technique for Pedestrian Detection Based on Quarter Cell","year":2017,"lang":"en","type":"article","venue":"Journal of Image and Graphics","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Pedestrian; Pedestrian detection; Computer science; Artificial intelligence; Computer vision; Geography; Transport engineering; Engineering; Archaeology","score_opus":0.006118394497805383,"score_gpt":0.2229481942352577,"score_spread":0.2168297997374523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908826808","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":[0.18626757,0.00007365389,0.8120951,0.00034440585,0.00017042886,0.00015384525,0.000004894089,0.000049737195,0.00084036],"genre_scores_gemma":[0.9969822,0.00011183846,0.0027771357,0.000038601564,0.00006194093,0.0000075489957,3.146788e-7,0.000011373023,0.00000904561],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99960387,0.000007884444,0.00017234431,0.000058069123,0.000052961434,0.00010487191],"domain_scores_gemma":[0.99956083,0.000042380827,0.00013691536,0.0001642825,0.00005919347,0.00003639713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024506208,0.00008174421,0.00013182743,0.0001383259,0.00019236548,0.0000366952,0.000119039156,0.00014148175,0.0000024107762],"category_scores_gemma":[0.000035055735,0.000071102426,0.00008264096,0.000028701996,0.000063920685,0.00014987755,0.0000054898583,0.00028243085,6.546153e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019301093,0.00008255571,0.00046236103,0.00019338015,0.000047047935,0.000026646232,0.000094123585,0.00016090549,0.9751707,0.00021542862,0.00042097282,0.02293289],"study_design_scores_gemma":[0.0013180079,0.00079226244,0.006758971,0.00006111228,0.000043815537,0.00002885251,0.000037120994,0.01320647,0.9732164,0.0033312126,0.0010310921,0.00017466824],"about_ca_topic_score_codex":0.0000012194193,"about_ca_topic_score_gemma":0.0000075007515,"teacher_disagreement_score":0.8107146,"about_ca_system_score_codex":0.000011184871,"about_ca_system_score_gemma":0.000012175665,"threshold_uncertainty_score":0.28994727},"labels":[],"label_agreement":null},{"id":"W2909761845","doi":"10.1109/iros.2018.8594388","title":"Deep Multi-Sensor Lane Detection","year":2018,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":103,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Field (mathematics); Image (mathematics); Artificial neural network; Lidar; Remote sensing; Geography; Mathematics","score_opus":0.008670715388683263,"score_gpt":0.20639091186321593,"score_spread":0.19772019647453265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909761845","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":[0.2903892,0.000043773554,0.685932,0.000045141445,0.00027678258,0.00006050999,4.6993708e-7,0.0024549006,0.020797227],"genre_scores_gemma":[0.99112326,0.000006850998,0.008184463,0.000034425255,0.00006418561,0.0000036313372,5.860417e-7,0.000011078608,0.0005714912],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971753,0.0000035821708,0.00006742127,0.00006871155,0.000022652775,0.00012011923],"domain_scores_gemma":[0.99983746,0.00000743007,0.0000051783895,0.000117937925,0.00001269329,0.00001928968],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000033056323,0.00005512647,0.000052356605,0.000036109217,0.00004835334,0.0000033791302,0.00005241528,0.000105018684,0.0002449282],"category_scores_gemma":[0.0000065066415,0.000050824805,0.000015181573,0.000065158354,0.000043296877,0.000033147455,0.000010156681,0.00008641692,0.0009875163],"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":[0.000028577097,0.00007011387,0.0045361016,0.000037517886,0.00013348686,0.000019157187,0.0005438744,0.0025156497,0.1896204,0.003560367,0.00065281714,0.7982819],"study_design_scores_gemma":[0.0003371701,0.00006555405,0.016076278,0.0000023163825,0.0000073343417,0.00002534975,0.00007035979,0.68648785,0.2829315,0.00024039477,0.01356639,0.000189473],"about_ca_topic_score_codex":0.000008898291,"about_ca_topic_score_gemma":0.00028657593,"teacher_disagreement_score":0.7980925,"about_ca_system_score_codex":0.000020025855,"about_ca_system_score_gemma":0.0000013760229,"threshold_uncertainty_score":0.9997903},"labels":[],"label_agreement":null},{"id":"W2910009568","doi":"10.1109/epec.2018.8598294","title":"Modeling and Simulation of an Autonomous-capable Electrified Vehicle: A Review","year":2018,"lang":"en","type":"review","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"McMaster University","funders":"","keywords":"Computer science; Automotive industry; Distraction; Field (mathematics); Modeling and simulation; Vehicle dynamics; Control engineering; Simulation; Systems engineering; Automotive engineering; Engineering; Aerospace engineering","score_opus":0.03051178683246113,"score_gpt":0.29570040837644707,"score_spread":0.2651886215439859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910009568","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":[0.000043921467,0.9894446,0.0078903185,0.0000039944207,0.000044238084,0.00061066786,0.000007855199,0.0006425145,0.001311884],"genre_scores_gemma":[0.00084979093,0.99794173,0.0009258345,0.000019700332,0.000041422765,0.000037672824,0.000041501055,0.00006513577,0.0000772376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985918,0.00005039068,0.0007351764,0.00029569556,0.000080207,0.00024673017],"domain_scores_gemma":[0.99926674,0.000058163543,0.000100694466,0.00046731462,0.00005076352,0.00005629071],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036858226,0.00032383867,0.0013668323,0.00014933622,0.00004901504,0.000008686997,0.00022363458,0.00053644914,0.000061863015],"category_scores_gemma":[0.000029708222,0.0002831682,0.00014193275,0.00027087826,0.00004936318,0.00013604015,0.000036844,0.00035833305,0.00002925895],"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":[8.5820324e-7,0.000010666797,6.1374706e-8,0.036155324,0.00008320583,9.4824986e-7,0.000007919223,0.018203348,8.933251e-7,0.00019988102,0.000036331883,0.9453006],"study_design_scores_gemma":[0.00005201316,0.000038704584,4.9571426e-8,0.008723247,0.00031409974,0.000009554887,9.962002e-7,0.74963194,0.000004902339,0.000106466934,0.24087965,0.0002383904],"about_ca_topic_score_codex":0.0000075460357,"about_ca_topic_score_gemma":0.0000058098785,"teacher_disagreement_score":0.94506216,"about_ca_system_score_codex":0.00006629472,"about_ca_system_score_gemma":0.000076864315,"threshold_uncertainty_score":0.99996203},"labels":[],"label_agreement":null},{"id":"W2911324515","doi":"10.1007/978-3-030-30281-8_20","title":"WiseMove: A Framework to Investigate Safe Deep Reinforcement Learning for Autonomous Driving","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Modular design; Scalability; Context (archaeology); Software; Motion planning; Architecture; Deep learning","score_opus":0.00858529657000148,"score_gpt":0.2190842659755918,"score_spread":0.21049896940559032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911324515","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":[0.000696612,0.00022774316,0.99339277,0.00038582867,0.0011625546,0.0007546589,0.0000011333462,0.0005721449,0.0028065478],"genre_scores_gemma":[0.7105548,0.00003237037,0.28705812,0.0010771136,0.00028696546,0.00003423835,0.000008520724,0.00009913581,0.0008487464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977605,0.000008014936,0.0004626722,0.0007695269,0.0002903146,0.0007089966],"domain_scores_gemma":[0.99855155,0.00042968782,0.000112372174,0.00067749404,0.00007457353,0.0001543242],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004490153,0.00047577792,0.00051649695,0.0005543705,0.00021747955,0.000102175116,0.0010129277,0.000672167,0.000038435872],"category_scores_gemma":[0.00017881447,0.00049870805,0.00010914964,0.00027034793,0.00030365557,0.0001453215,0.0004308526,0.0013731569,0.000108940745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035459552,0.000001815311,0.00006600522,0.000051795967,0.000014741088,0.0000060502134,0.0004937096,0.8070879,0.00011032817,0.0062903813,0.000007156948,0.18586658],"study_design_scores_gemma":[0.00014610704,0.00016232468,0.00010677575,0.00051165535,0.0000121989915,0.000011833379,3.558414e-7,0.9457524,0.0012968236,0.04390089,0.007476886,0.0006217865],"about_ca_topic_score_codex":0.0000035037444,"about_ca_topic_score_gemma":0.000028237704,"teacher_disagreement_score":0.7098582,"about_ca_system_score_codex":0.00050694286,"about_ca_system_score_gemma":0.00022311404,"threshold_uncertainty_score":0.99974644},"labels":[],"label_agreement":null},{"id":"W2917159929","doi":"10.1109/glocom.2018.8647971","title":"Data-driven Robust Scoring Approach for Driver Profiling Applications","year":2018,"lang":"en","type":"article","venue":"","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":"Queen's University","funders":"","keywords":"Computer science; Profiling (computer programming); Decision tree; Machine learning; Support vector machine; Data mining; Artificial intelligence; Preprocessor; Regression; Statistics","score_opus":0.045279305842745886,"score_gpt":0.25572091918341905,"score_spread":0.21044161334067316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917159929","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":[0.0034120786,0.00003721394,0.9688703,0.00003113928,0.000053788117,0.0005257837,0.00004634617,0.0012783057,0.02574509],"genre_scores_gemma":[0.5647588,0.000006858882,0.43447003,0.000019585757,0.00018401918,0.00020348966,0.00014106903,0.000022954382,0.00019322267],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993703,0.0000030241467,0.00013788648,0.00023910223,0.000040228453,0.00020950154],"domain_scores_gemma":[0.99930716,0.000020821268,0.000015692513,0.0005871702,0.000037956423,0.000031198073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010051791,0.00009352826,0.000101908685,0.000048715694,0.00014568209,0.000011911116,0.00041540057,0.00011024603,0.000023148119],"category_scores_gemma":[0.000007393149,0.000091128306,0.000020743788,0.00011639007,0.00008477562,0.0001432574,0.000107636595,0.00010817005,0.00005570698],"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":[0.000038616217,0.00026974894,0.005849557,0.00062556745,0.000578014,0.0000016608064,0.00038488483,0.25601435,0.022557557,0.5025374,0.014003602,0.19713901],"study_design_scores_gemma":[0.00015432035,0.000014036542,0.00013348798,0.0000027752076,0.000016777643,0.0000028070885,0.000062629704,0.97323954,0.0066338372,0.00029753742,0.01929863,0.00014364677],"about_ca_topic_score_codex":0.0000015952673,"about_ca_topic_score_gemma":0.000007849755,"teacher_disagreement_score":0.7172252,"about_ca_system_score_codex":0.000027782185,"about_ca_system_score_gemma":0.000012272178,"threshold_uncertainty_score":0.37161043},"labels":[],"label_agreement":null},{"id":"W2919388183","doi":"10.1109/iros40897.2019.8967715","title":"A behavior driven approach for sampling rare event situations for autonomous vehicles","year":2019,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Event (particle physics); Rare events; Sampling (signal processing); Bounded function; Data collection; Data acquisition; Data modeling","score_opus":0.02011147717966617,"score_gpt":0.2512080196841782,"score_spread":0.23109654250451206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919388183","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":[0.31150955,0.00007721867,0.6844969,0.00009674648,0.00012815354,0.0016301265,0.00006552834,0.000953322,0.0010424692],"genre_scores_gemma":[0.87098837,0.0000056038966,0.12708159,0.000029919005,0.000037837013,0.0010544267,0.000113497175,0.00003948466,0.000649268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999219,0.00000419347,0.00022464068,0.00021579288,0.000049357808,0.00028703242],"domain_scores_gemma":[0.9995491,0.0000911172,0.00002794238,0.00025095994,0.00004065911,0.00004024975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010431553,0.000142195,0.00019197693,0.00007811845,0.000112556496,0.00001559295,0.00017254529,0.00019497963,0.000044663877],"category_scores_gemma":[0.000011703446,0.00014291913,0.00012533866,0.000071663184,0.000021572261,0.00008967024,0.000026679338,0.00011326653,0.000030155954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007312742,0.00045483888,0.007948385,0.0006162494,0.0003377058,0.0000011447594,0.001066272,0.67312545,0.02802108,0.09343786,0.0022373544,0.19268054],"study_design_scores_gemma":[0.0010659202,0.00011300589,0.0038296455,0.000010536349,0.00008035714,0.000006502205,0.00032726847,0.9804693,0.0049831145,0.0016565272,0.007084174,0.00037368355],"about_ca_topic_score_codex":0.0000029558562,"about_ca_topic_score_gemma":0.0000062498048,"teacher_disagreement_score":0.5594788,"about_ca_system_score_codex":0.000086036736,"about_ca_system_score_gemma":0.000027651367,"threshold_uncertainty_score":0.5828073},"labels":[],"label_agreement":null},{"id":"W2923088732","doi":"10.1126/scirobotics.aaw4106","title":"Parallel testing of vehicle intelligence via virtual-real interaction","year":2019,"lang":"en","type":"review","venue":"Science Robotics","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":220,"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; Human–computer interaction","score_opus":0.06713276179965347,"score_gpt":0.31899956884112823,"score_spread":0.2518668070414748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923088732","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":[0.00005885774,0.8278624,0.16451089,0.000011606639,0.0017537831,0.0005652952,0.000010547263,0.00067194167,0.0045546996],"genre_scores_gemma":[0.005694701,0.9819569,0.012189334,0.0000039083593,0.000036761885,0.00000898405,0.0000050728713,0.000038062073,0.00006626702],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847627,0.000021142901,0.0005826739,0.0003211675,0.0002230525,0.0003756959],"domain_scores_gemma":[0.99891084,0.00023432968,0.00021061357,0.0004984044,0.00008448566,0.000061335864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043239546,0.00026006092,0.00075221807,0.00029916316,0.00009314543,0.000022501523,0.0008237482,0.00028443267,0.000010327826],"category_scores_gemma":[0.0001230296,0.00023661052,0.00012199027,0.0012028822,0.00045438515,0.00022839083,0.00016749922,0.0005918962,0.00022187878],"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":[3.7954456e-7,0.00000982763,0.000009827711,0.0011105508,0.000012945546,0.0000015216463,0.000025811123,0.1626023,0.000026760941,0.0013982534,0.0000047601634,0.834797],"study_design_scores_gemma":[0.00008050889,0.00025101364,0.00003694845,0.0073848264,0.00034640997,0.00010133475,0.00010548578,0.9311044,0.0003065218,0.0007579025,0.058481026,0.0010436085],"about_ca_topic_score_codex":0.000013040489,"about_ca_topic_score_gemma":0.000003132086,"teacher_disagreement_score":0.83375347,"about_ca_system_score_codex":0.00023950756,"about_ca_system_score_gemma":0.00024313784,"threshold_uncertainty_score":0.9648697},"labels":[],"label_agreement":null},{"id":"W2926054342","doi":"10.3390/s19071618","title":"Steering Angle Assisted Vehicular Navigation Using Portable Devices in GNSS-Denied Environments","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Heading (navigation); Inertial measurement unit; Accelerometer; Steering wheel; GNSS applications; Gyroscope; Extended Kalman filter; Kalman filter; Computer science; Acceleration; Simulation; Road surface; Engineering; Automotive engineering; Global Positioning System; Artificial intelligence; Aerospace engineering","score_opus":0.008920192243685754,"score_gpt":0.20435048675113449,"score_spread":0.19543029450744873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2926054342","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":[0.9980945,0.00015547489,0.00019269841,0.000012133275,0.00013392228,0.00016030709,0.00000265252,0.00022682069,0.0010215021],"genre_scores_gemma":[0.9989871,0.000009427605,0.00075859553,0.000011257768,0.000013189853,0.000003409399,0.000014419491,0.000029733801,0.00017290904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992493,0.000017530674,0.00020980717,0.00017279183,0.00009961262,0.00025096472],"domain_scores_gemma":[0.9997036,0.00001568694,0.000037185167,0.00021047029,0.000004159601,0.000028921067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010924994,0.00012765171,0.00016782641,0.00009088206,0.000035327103,0.000010307431,0.00009061052,0.00017741988,0.00005122443],"category_scores_gemma":[0.0000037023601,0.00014441997,0.000032758093,0.00016004396,0.000020667567,0.00012263627,0.000025159909,0.00020529774,0.00013018766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007334464,0.000024962164,0.097622044,0.000061858234,0.00004444577,0.000051828254,0.00020607274,0.7729359,0.12582687,0.00010162884,0.000003624611,0.003113417],"study_design_scores_gemma":[0.0006255339,0.00002192001,0.2292346,0.0001137617,0.000020599215,0.000031133368,0.000346435,0.72468,0.04215546,0.00007037818,0.0023250305,0.00037516636],"about_ca_topic_score_codex":0.000041525833,"about_ca_topic_score_gemma":0.00001880408,"teacher_disagreement_score":0.13161255,"about_ca_system_score_codex":0.00014981924,"about_ca_system_score_gemma":0.000006824002,"threshold_uncertainty_score":0.58892757},"labels":[],"label_agreement":null},{"id":"W2937291170","doi":"10.1109/itsc.2019.8916912","title":"Multi-Objective Autonomous Braking System using Naturalistic Dataset","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Brake; Reinforcement learning; Pedestrian; Computer science; Perception; Braking system; Braking distance; Virtual reality; Simulation; Automotive engineering; Artificial intelligence; Engineering; Transport engineering; Psychology","score_opus":0.024108266860362687,"score_gpt":0.25920647354815307,"score_spread":0.23509820668779038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937291170","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":[0.23586416,0.0039756633,0.700845,0.00007232268,0.012351016,0.0034618978,0.011527456,0.01810572,0.013796786],"genre_scores_gemma":[0.97892714,0.000014851612,0.019256847,0.000035042533,0.00010259741,0.000026801483,0.0014169269,0.00010218439,0.00011758322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822104,0.000045896566,0.0004909836,0.0006196675,0.00013730674,0.0004851023],"domain_scores_gemma":[0.9985602,0.00007596448,0.00013710295,0.0011129202,0.00004776847,0.000066067485],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002110121,0.00051702105,0.0006601818,0.0002450745,0.000113852446,0.00006591665,0.00065206806,0.0011550091,0.00004704115],"category_scores_gemma":[0.000023584698,0.0005247315,0.00012782436,0.000117041236,0.00007278854,0.00011313314,0.0008250662,0.0017462837,0.00026162982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011680452,0.000030577827,0.0004129255,0.0020750095,0.00058290217,0.00010976559,0.00023747736,0.990097,0.0013064144,0.002394363,0.0011399941,0.0016018989],"study_design_scores_gemma":[0.00026875312,0.000007787463,0.00073978654,0.00030170655,0.000109797256,0.00008757132,0.00017540118,0.9957675,0.00088195276,0.000048588136,0.000958527,0.000652671],"about_ca_topic_score_codex":0.00032277155,"about_ca_topic_score_gemma":0.000055369168,"teacher_disagreement_score":0.74306303,"about_ca_system_score_codex":0.0010188828,"about_ca_system_score_gemma":0.00012552917,"threshold_uncertainty_score":0.99972045},"labels":[],"label_agreement":null},{"id":"W2942950293","doi":"10.1109/icra.2019.8794214","title":"DeepSignals: Predicting Intent of Drivers Through Visual Signals","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Task (project management); Frame (networking); Artificial intelligence; Deep neural networks; Artificial neural network; Real-time computing; Computer vision; Machine learning; Human–computer interaction; Telecommunications; Engineering","score_opus":0.015241815558186574,"score_gpt":0.2468644330544139,"score_spread":0.23162261749622734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942950293","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":[0.6530858,0.0009901434,0.29962194,0.00011923157,0.0013699267,0.0007042087,0.000039312916,0.002411676,0.04165772],"genre_scores_gemma":[0.9963288,0.00026021653,0.0029773372,0.000053569125,0.00005850851,0.000023019522,0.000025308751,0.00005192811,0.0002213369],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986052,0.000027901453,0.00054923026,0.00033294823,0.00017396107,0.00031075338],"domain_scores_gemma":[0.9991768,0.000109503824,0.00014270954,0.00043398602,0.00010076755,0.0000362333],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020981654,0.00031389427,0.00057001744,0.00012170702,0.00003030231,0.000010992281,0.00043790266,0.00080005015,0.00035776338],"category_scores_gemma":[0.00003969179,0.0003110393,0.0002009867,0.000093901785,0.00010645937,0.0000829994,0.00053252303,0.0009773384,0.000092131384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023728047,0.00009353635,0.01212866,0.0011785687,0.0011160246,0.000013518995,0.0017210699,0.9606556,0.008331121,0.002754693,0.0024945738,0.009488905],"study_design_scores_gemma":[0.0007739968,0.00022779584,0.002626862,0.0009312434,0.00025617206,0.00001320714,0.002236541,0.78685576,0.1939824,0.009014232,0.0018043477,0.0012774257],"about_ca_topic_score_codex":0.000037376336,"about_ca_topic_score_gemma":0.0000097038455,"teacher_disagreement_score":0.34324294,"about_ca_system_score_codex":0.000114075934,"about_ca_system_score_gemma":0.000059702128,"threshold_uncertainty_score":0.9999342},"labels":[],"label_agreement":null},{"id":"W2943871880","doi":"10.1177/0361198119844245","title":"Modeling Passing Maneuver Based on Vehicle Characteristics for In-Vehicle Collision Warning Systems on Two-Lane Highways","year":2019,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","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":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Drivetrain; Truck; Computer science; MATLAB; Torque; Collision; Simulation; Process (computing); Automotive engineering; Vehicle dynamics; Engineering","score_opus":0.05107580495819621,"score_gpt":0.32282359651703885,"score_spread":0.27174779155884266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943871880","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":[0.9882096,0.000059321905,0.008592146,0.00080143387,0.0006764756,0.0013167922,0.00007301081,0.0001191629,0.00015207396],"genre_scores_gemma":[0.998774,0.00015035617,0.0005984247,0.00003446406,0.000114174356,0.000092379814,0.000031597894,0.00008325291,0.000121364705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954537,0.0004423188,0.0012156814,0.0003666935,0.0016770426,0.00084455666],"domain_scores_gemma":[0.9968842,0.0013210485,0.00019092567,0.0004540082,0.00096545205,0.00018433713],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003488028,0.000258326,0.00050381426,0.0011077917,0.00037920952,0.00009789079,0.0006744245,0.0002842088,0.000042093583],"category_scores_gemma":[0.00010124796,0.00021351068,0.0002272126,0.0010299495,0.00013982046,0.00032441475,0.0000038617013,0.0024797253,0.00003427445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018595436,0.00015822945,0.05421207,0.00041586757,0.000049023278,0.000058583988,0.0005249372,0.9303738,0.007041289,0.0017876826,0.00026837585,0.0032505835],"study_design_scores_gemma":[0.0028203079,0.0007864983,0.16842243,0.00096960954,0.000019009987,2.5927113e-7,0.0005462848,0.82300645,0.0018766106,0.00032933912,0.0009980331,0.00022516128],"about_ca_topic_score_codex":0.00082972104,"about_ca_topic_score_gemma":0.0017835697,"teacher_disagreement_score":0.11421036,"about_ca_system_score_codex":0.00047721076,"about_ca_system_score_gemma":0.00024089988,"threshold_uncertainty_score":0.9998216},"labels":[],"label_agreement":null},{"id":"W2946585790","doi":"","title":"A study on lane detection methods for autonomous driving","year":2019,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University","keywords":"Computer science","score_opus":0.012598578956655483,"score_gpt":0.24743035600119478,"score_spread":0.2348317770445393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946585790","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13549922,0.00014829318,0.1231621,0.000018698875,0.0033386222,0.002933981,0.000031998385,0.002630812,0.73223627],"genre_scores_gemma":[0.49921113,0.000014374209,0.0036784008,0.000012078392,0.0000687957,0.000017141065,0.00009950029,0.00013777679,0.4967608],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988895,0.00007799173,0.00020202546,0.000429639,0.0000820148,0.00031879908],"domain_scores_gemma":[0.9993438,0.000114725364,0.00009955894,0.00034669338,0.000042992335,0.000052206815],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014778218,0.00036225375,0.00042674397,0.00031758126,0.00014785907,0.000025650745,0.00035065887,0.0005902858,0.0022921527],"category_scores_gemma":[0.000013263603,0.00043356567,0.00015517935,0.00028335638,0.00001573012,0.00010704705,0.000034057717,0.0005597813,0.00009969416],"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":[0.00014606553,0.00007894442,0.00025297413,0.00014171518,0.00031523916,0.000022734786,0.0009585606,0.001214693,0.0010853282,0.00035516868,0.00004626905,0.9953823],"study_design_scores_gemma":[0.006418604,0.0029636973,0.017316656,0.00040745467,0.0014148013,0.000017506574,0.043583725,0.03892737,0.027940545,0.00049070385,0.85733557,0.003183348],"about_ca_topic_score_codex":0.0000144835,"about_ca_topic_score_gemma":0.00046600716,"teacher_disagreement_score":0.99219894,"about_ca_system_score_codex":0.00034138444,"about_ca_system_score_gemma":0.000037559366,"threshold_uncertainty_score":0.9998116},"labels":[],"label_agreement":null},{"id":"W2946768382","doi":"10.1109/tits.2019.2909107","title":"An Ensemble Learning-Based Vehicle Steering Detector Using Smartphones","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","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":"McGill University","funders":"State Key Laboratory of Software Development Environment; National Natural Science Foundation of China","keywords":"Ensemble learning; Computer science; Mobile phone; Artificial intelligence; Heuristic; Energy consumption; Real-time computing; Machine learning; Engineering","score_opus":0.01411444362544113,"score_gpt":0.22541012207861147,"score_spread":0.21129567845317035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946768382","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":[0.49493623,0.000049355756,0.503076,0.000003221078,0.00083702186,0.0002571126,0.000017548446,0.0007826864,0.00004079182],"genre_scores_gemma":[0.99935275,0.000021715807,0.00030107662,0.000012698665,0.000028711254,0.00006269307,0.00001675652,0.00008158187,0.00012200788],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855644,0.000054350934,0.0005165555,0.00032038463,0.0002258545,0.0003264342],"domain_scores_gemma":[0.99936104,0.00006733298,0.000066231805,0.00033215128,0.00006849104,0.00010476682],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016636634,0.00027055424,0.00029840032,0.00028267925,0.00016509026,0.000040509978,0.00016866329,0.00026638867,0.0001605097],"category_scores_gemma":[6.2348266e-7,0.00030022988,0.00013200776,0.00031543346,0.000034951103,0.00023773176,9.942153e-8,0.0004566018,0.0002471107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044965873,0.00005840886,0.0008009235,0.00013237161,0.000056467743,0.0000048474926,0.00039695846,0.92887497,0.06637399,0.000035495705,0.0000011264067,0.0032194715],"study_design_scores_gemma":[0.0002994567,0.00016129958,0.0005330877,0.000083070125,0.000037592436,0.0000034658365,0.00051211094,0.71265346,0.28499174,0.0000039480533,0.0004307206,0.00029005646],"about_ca_topic_score_codex":0.00012402887,"about_ca_topic_score_gemma":0.00014017943,"teacher_disagreement_score":0.5044165,"about_ca_system_score_codex":0.0001572153,"about_ca_system_score_gemma":0.000036347563,"threshold_uncertainty_score":0.999945},"labels":[],"label_agreement":null},{"id":"W2950207687","doi":"10.1109/access.2019.2923438","title":"Coordinated Ramp Metering Based on Real-Time OD Information","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Alberta","keywords":"Metering mode; Computer science; Engineering","score_opus":0.005888379406548321,"score_gpt":0.21539665231949132,"score_spread":0.209508272912943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950207687","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":[0.95456433,0.0000037888403,0.008108613,0.00009014941,0.0003930137,0.00021177599,0.000008433778,0.0014303048,0.035189576],"genre_scores_gemma":[0.9995473,0.0000064618707,0.0001734909,0.000106296204,0.000015323034,0.000014705467,0.00001935752,0.00001623533,0.0001008531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994634,0.000010354777,0.00017542791,0.00008754387,0.00007684473,0.00018643642],"domain_scores_gemma":[0.9995952,0.00004327994,0.00003528387,0.00027460975,0.000023002967,0.00002858757],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010521647,0.000114432216,0.0001400927,0.00014811229,0.000037553633,0.000044942775,0.00027850372,0.00014939734,0.00022257997],"category_scores_gemma":[0.000008405469,0.000114922,0.00003242021,0.00018847665,0.000014463909,0.00063727767,0.00002220136,0.00016378034,0.0011888023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012767423,0.000047071593,0.0041375165,0.00025515372,0.000100121026,0.000011176463,0.00019156163,0.8663602,0.06029757,0.0006355203,0.0045220945,0.063314356],"study_design_scores_gemma":[0.00068072916,0.000060204897,0.0058653266,0.00003972358,0.00000964321,0.0000021263552,0.000008787476,0.8951918,0.094645835,0.00011877472,0.0031124018,0.0002646533],"about_ca_topic_score_codex":0.000016098775,"about_ca_topic_score_gemma":0.0000011962518,"teacher_disagreement_score":0.0630497,"about_ca_system_score_codex":0.0000647304,"about_ca_system_score_gemma":0.000011698036,"threshold_uncertainty_score":0.9995889},"labels":[],"label_agreement":null},{"id":"W2950709199","doi":"","title":"Minimum Predicted Distance: applying a common metric to collision avoidance strategies between typically developing children and adult walkers","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Collision avoidance; Metric (unit); Collision; Computer science; Engineering; Computer security","score_opus":0.006511984974659882,"score_gpt":0.2100189237369346,"score_spread":0.20350693876227471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950709199","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":[0.6138836,0.0007885299,0.3779733,0.0013225315,0.00009839401,0.0008922969,0.00015429838,0.0007600621,0.0041269795],"genre_scores_gemma":[0.9652559,0.00069029897,0.03303765,0.000037495305,0.000017317283,0.00011298298,0.000503076,0.0000600622,0.0002852225],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9973799,0.00071714655,0.000572672,0.0006579225,0.0002706733,0.00040170486],"domain_scores_gemma":[0.99702686,0.00074016943,0.00021204214,0.0012336649,0.000641645,0.00014560917],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013888035,0.00038647544,0.0005416135,0.00031729633,0.00027067054,0.00026086305,0.0009812593,0.0005574096,0.000008307254],"category_scores_gemma":[0.00038973344,0.00043032021,0.0000858899,0.0006447667,0.0001730581,0.00015220938,0.0009899578,0.00096920045,0.000021645368],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014145412,0.00040785476,0.35706273,0.002555329,0.0014602346,0.000016023025,0.023230108,0.037713256,0.002638514,0.17215794,0.0031181264,0.39949843],"study_design_scores_gemma":[0.0022044303,0.0000045532474,0.78550977,0.0078004804,0.0002755217,0.000021317739,0.0008155844,0.14722171,0.03541092,0.011903033,0.0059053414,0.002927347],"about_ca_topic_score_codex":0.00026345166,"about_ca_topic_score_gemma":0.0007524989,"teacher_disagreement_score":0.42844704,"about_ca_system_score_codex":0.00022060715,"about_ca_system_score_gemma":0.00017545407,"threshold_uncertainty_score":0.99981487},"labels":[],"label_agreement":null},{"id":"W2950955091","doi":"10.1109/tvt.2019.2921918","title":"End-to-End Autonomous Driving: An Angle Branched Network Approach","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Intersection (aeronautics); Position (finance); Computer science; Artificial intelligence; Trajectory; End-to-end principle; Path (computing); Acceleration; Simulation; Computer vision; Human–computer interaction; Engineering; Computer network; Aerospace engineering","score_opus":0.006315317844530527,"score_gpt":0.1994052447805501,"score_spread":0.1930899269360196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950955091","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":[0.50593626,0.00010830978,0.4863454,0.00034798382,0.00055975426,0.0005329699,0.00001192662,0.0045425813,0.001614775],"genre_scores_gemma":[0.98904574,0.000030132656,0.0100645395,0.00015492088,0.00005277094,0.00021150497,0.000011291084,0.00011426307,0.00031486587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978692,0.000044178832,0.0004077653,0.0006695323,0.00018030645,0.0008290397],"domain_scores_gemma":[0.99846834,0.000040688712,0.000049193077,0.0012459076,0.00004549357,0.00015040296],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022200316,0.00041978928,0.0005173782,0.0006620921,0.00023430027,0.000025577394,0.0006763242,0.0011188864,0.00030045086],"category_scores_gemma":[0.0000034490756,0.0004621513,0.00015774657,0.0012010488,0.00018025863,0.00018415003,0.000007229669,0.0012566228,0.00076250127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002628171,0.00026998096,0.00042820067,0.000034246877,0.00021715373,0.000024835566,0.00013433838,0.8927822,0.020109309,0.003468703,0.00012155961,0.08238315],"study_design_scores_gemma":[0.0028983518,0.0018088543,0.0026716455,0.00011760866,0.00026155487,0.0005439197,0.0005863853,0.6858801,0.25220513,0.0048076953,0.045411527,0.0028071997],"about_ca_topic_score_codex":0.000009488433,"about_ca_topic_score_gemma":0.00004899327,"teacher_disagreement_score":0.48310944,"about_ca_system_score_codex":0.00020306637,"about_ca_system_score_gemma":0.00004541461,"threshold_uncertainty_score":0.99978304},"labels":[],"label_agreement":null},{"id":"W2953412990","doi":"10.1109/icse-companion.2019.00024","title":"Software Engineering for Automated Vehicles: Addressing the Needs of Cars That Run on Software and Data","year":2019,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"DevOps; Software; Robot; Process (computing); Software engineering; Computer science; Engineering; Systems engineering; Automation; Transport engineering; Engineering management; Manufacturing engineering; Software deployment; Artificial intelligence; Operating system","score_opus":0.03520527522300876,"score_gpt":0.2530496720706279,"score_spread":0.21784439684761914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953412990","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":[0.9563871,0.0006082516,0.03795845,0.00010051013,0.00024102085,0.0005044541,0.00013539051,0.003901831,0.00016300404],"genre_scores_gemma":[0.98835576,0.00002873333,0.01138489,0.000047413057,0.000016513459,0.000013416762,0.00004141784,0.000043820208,0.00006802584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999335,0.000007093388,0.00017504102,0.00015958589,0.00008271476,0.00024055451],"domain_scores_gemma":[0.99892116,0.0002991035,0.000034562334,0.000695101,0.00001953363,0.00003055152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018862303,0.00016179256,0.00021585435,0.00010253434,0.000064813416,0.000018556413,0.0004218804,0.00017433445,0.000017082912],"category_scores_gemma":[0.00007844862,0.00012230677,0.00003259115,0.00013761975,0.000047774607,0.00015005046,0.00014511062,0.00018999382,0.000008574563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019126844,0.00015612575,0.16173527,0.00412028,0.0014671008,0.000012268348,0.0048626782,0.48186404,0.01827541,0.0131798005,0.015259367,0.2988764],"study_design_scores_gemma":[0.00072681933,0.00008096921,0.023494348,0.0002021163,0.000048528327,0.000012628776,0.0006026033,0.95984864,0.010500647,0.00007218061,0.004089669,0.00032086787],"about_ca_topic_score_codex":0.000008849972,"about_ca_topic_score_gemma":0.0000034054572,"teacher_disagreement_score":0.47798458,"about_ca_system_score_codex":0.000024368284,"about_ca_system_score_gemma":0.000015625783,"threshold_uncertainty_score":0.4987525},"labels":[],"label_agreement":null},{"id":"W2954371384","doi":"10.1155/2019/1019078","title":"Hierarchical Sarsa Learning Based Route Guidance Algorithm","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Reinforcement learning; Cluster analysis; State space; Scale (ratio); Task (project management); State (computer science); Artificial intelligence; Differential (mechanical device); Algorithm; Engineering; Mathematics","score_opus":0.0033944006102671425,"score_gpt":0.20482759907186512,"score_spread":0.201433198461598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954371384","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":[0.77534753,0.0002904312,0.2235337,0.00010941255,0.0003445873,0.000069608024,0.00000483088,0.00013032135,0.000169579],"genre_scores_gemma":[0.94950294,0.000066133325,0.05028164,0.000028802588,0.00004238692,0.0000014738389,0.00001184729,0.000019315501,0.000045469853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99922746,0.000014156041,0.00038732274,0.000080086065,0.00014548004,0.00014548303],"domain_scores_gemma":[0.99963504,0.000043816683,0.00012912326,0.00008156701,0.000066600594,0.00004383332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014747879,0.00009913004,0.00019542147,0.00010402697,0.00003080123,0.0000049469436,0.00010487999,0.00010002884,0.00004327308],"category_scores_gemma":[0.000008190917,0.00009736481,0.00008902594,0.00013180482,0.00002130224,0.00026520196,7.5594346e-7,0.0005182059,0.000014212341],"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":[0.000042232787,0.000014257728,0.005486086,0.000026867994,0.00002479994,0.000025555386,0.00018439765,0.8986901,0.008491052,0.0001399187,0.0000075241487,0.08686718],"study_design_scores_gemma":[0.0044808458,0.00071035186,0.73898476,0.00030290266,0.000085543004,0.00004058454,0.00033406165,0.19675368,0.028581753,0.0019770511,0.027200626,0.00054783234],"about_ca_topic_score_codex":5.645486e-7,"about_ca_topic_score_gemma":0.0000030028325,"teacher_disagreement_score":0.7334987,"about_ca_system_score_codex":0.000054617583,"about_ca_system_score_gemma":0.000031102303,"threshold_uncertainty_score":0.39704219},"labels":[],"label_agreement":null},{"id":"W2963599868","doi":"10.1177/0361198119850470","title":"Development of Warning System and Driving Model for Overtaking Trucks on Two-Lane Highways","year":2019,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","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":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overtaking; Truck; Simulation; Collision; Computer science; Warning system; Radar; Driving simulator; Real-time computing; Engineering; Automotive engineering; Transport engineering; Computer security","score_opus":0.0515183762349952,"score_gpt":0.3244006571345813,"score_spread":0.2728822808995861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963599868","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":[0.9691658,0.00008114463,0.02930787,0.0001537048,0.00021639868,0.0007837552,0.000018870536,0.000090007234,0.00018243179],"genre_scores_gemma":[0.9873991,0.000102992286,0.012237385,0.00000504244,0.000036093614,0.000048515933,0.000008372103,0.0000508342,0.00011165167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99669397,0.00015690408,0.0011072378,0.00025275318,0.0012170199,0.00057213125],"domain_scores_gemma":[0.9978296,0.00066652265,0.00025701127,0.00027923458,0.0008219639,0.00014563165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002896237,0.0001908567,0.00042811994,0.0007019077,0.00034698885,0.000031701034,0.00050702627,0.00018249724,0.00001936085],"category_scores_gemma":[0.000040918883,0.0001537449,0.00016107813,0.00053812395,0.000175114,0.0002596814,0.0000051738766,0.001324405,0.000005264876],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028039883,0.00025191632,0.29958597,0.0042591286,0.00097183895,0.00004729994,0.021684073,0.4606331,0.09989957,0.056393165,0.000666,0.05280396],"study_design_scores_gemma":[0.004571852,0.0006773168,0.55879736,0.0024029547,0.00008478262,0.0000015680586,0.004980441,0.39367464,0.031080497,0.0015831426,0.0016624845,0.00048293584],"about_ca_topic_score_codex":0.00012001285,"about_ca_topic_score_gemma":0.0032463889,"teacher_disagreement_score":0.2592114,"about_ca_system_score_codex":0.00024713983,"about_ca_system_score_gemma":0.00027652425,"threshold_uncertainty_score":0.62695354},"labels":[],"label_agreement":null},{"id":"W2967833234","doi":"10.1016/j.trc.2019.08.003","title":"Risk-based autonomous vehicle motion control with considering human driver’s behaviour","year":2019,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Innovate UK","keywords":"Trajectory; Motion (physics); Path (computing); Control (management); Computer science; Active safety; Vehicle dynamics; Motion planning; Model predictive control; Simulation; Engineering; Control theory (sociology); Automotive engineering; Artificial intelligence","score_opus":0.02035050304572326,"score_gpt":0.27706511366588127,"score_spread":0.256714610620158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967833234","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":[0.98211586,0.00013428954,0.008640251,0.00058784336,0.00007582585,0.00073752814,0.00005306367,0.007314145,0.00034117018],"genre_scores_gemma":[0.9982951,0.00006092986,0.0012409565,0.000009385763,0.000010102694,0.0002131338,0.000052740892,0.000059000955,0.000058637277],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980034,0.00006119722,0.00039567487,0.0004380445,0.00040674076,0.0006949055],"domain_scores_gemma":[0.9989484,0.00013531992,0.00008599611,0.0006145829,0.00016362223,0.000052070925],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00050977967,0.0002562237,0.00031547993,0.00055218814,0.0003992033,0.000035168505,0.00040129412,0.00036661845,0.00012492767],"category_scores_gemma":[0.00003229158,0.00024939954,0.000071308335,0.00060431735,0.00042662938,0.00023229526,0.000012198213,0.0012086029,0.000106632564],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063860825,0.00008922467,0.81728816,0.00013514342,0.00013228993,0.000069513226,0.00027466196,0.1525331,0.013185791,0.0066769123,0.00027270443,0.0092786625],"study_design_scores_gemma":[0.0075690374,0.0012139964,0.57227474,0.00040544156,0.0001844289,0.0000061830538,0.0051683374,0.1464487,0.2516272,0.005814833,0.007510434,0.0017766458],"about_ca_topic_score_codex":0.0001254788,"about_ca_topic_score_gemma":0.00048783005,"teacher_disagreement_score":0.24501336,"about_ca_system_score_codex":0.00014217402,"about_ca_system_score_gemma":0.000051499293,"threshold_uncertainty_score":0.9999958},"labels":[],"label_agreement":null},{"id":"W2968008415","doi":"10.1109/cvpr.2019.00886","title":"End-To-End Interpretable Neural Motion Planner","year":2019,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":389,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Planner; SAFER; Artificial intelligence; Position (finance); Set (abstract data type); Motion (physics); Trajectory; Motion planning; Data set; Volume (thermodynamics); Lidar; Raw data; Reinforcement learning; Machine learning; Sample (material); Robot; Computer vision; Geography","score_opus":0.004082958647840696,"score_gpt":0.18376942094997079,"score_spread":0.1796864623021301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968008415","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":[0.91085863,0.000032302585,0.013361787,0.00023349414,0.000396217,0.00011802165,0.0000037107195,0.0010867891,0.07390904],"genre_scores_gemma":[0.9968646,0.0000021515673,0.00045407328,0.00015812708,0.000019117808,0.0000060010934,0.0000052782807,0.00001487154,0.0024757637],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955964,0.000004986363,0.000098721095,0.000111934896,0.000043926004,0.0001807655],"domain_scores_gemma":[0.9997526,0.000014271486,0.000006326899,0.00018560134,0.000006853859,0.000034350473],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00005086127,0.000082271006,0.00009593575,0.00006326347,0.000018345558,0.000008382537,0.000113941765,0.000097099684,0.0018041994],"category_scores_gemma":[0.0000035268026,0.00007470048,0.000025725432,0.0000784282,0.000010368882,0.00010170625,0.000034846988,0.0001490606,0.0019118328],"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":[0.000087585024,0.00009207306,0.08071131,0.000141521,0.00022714717,0.000031164185,0.0013429009,0.38273993,0.07775091,0.048034865,0.015437266,0.39340332],"study_design_scores_gemma":[0.00047802698,0.00013153953,0.047619585,0.0000191818,0.000013936576,0.000033007916,0.00015548395,0.8868735,0.032484733,0.00074047764,0.030956969,0.0004935903],"about_ca_topic_score_codex":0.000016058224,"about_ca_topic_score_gemma":0.000012186406,"teacher_disagreement_score":0.5041335,"about_ca_system_score_codex":0.00003011977,"about_ca_system_score_gemma":0.0000027529,"threshold_uncertainty_score":0.9991083},"labels":[],"label_agreement":null},{"id":"W2968247225","doi":"10.1007/978-3-030-20503-4_6","title":"Bayesian Artificial Intelligence-Based Driver for Fully Automated Vehicle with Cognitive Capabilities","year":2019,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Automation; Automotive industry; Computer science; Quality (philosophy); Bayesian probability; Advanced driver assistance systems; Artificial intelligence; Engineering","score_opus":0.012060598188919478,"score_gpt":0.23566069233307393,"score_spread":0.22360009414415447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968247225","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":[0.008371244,0.014309027,0.935084,0.000030195872,0.0013226373,0.0025152136,0.00011477279,0.0018640801,0.03638883],"genre_scores_gemma":[0.9971101,0.00028758871,0.0007953205,0.000019220322,0.000117638374,0.000040547686,0.000041779538,0.00009526088,0.0014924922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982266,0.000019001074,0.0007156519,0.00048851356,0.00014932799,0.00040090465],"domain_scores_gemma":[0.9988712,0.0005454151,0.00019059164,0.00022174539,0.00011565056,0.00005537185],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002316291,0.00045420532,0.00066772907,0.0002421047,0.000118365366,0.00004779993,0.00017744815,0.00041084632,0.000018856537],"category_scores_gemma":[0.000016553124,0.000427128,0.0000836553,0.000062781364,0.00024368602,0.00011392762,0.000041140447,0.0004511014,0.000021510936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015664398,0.000029268278,0.00053453963,0.002049685,0.00018685387,0.000027847918,0.00073164154,0.62491655,0.000010265572,0.192203,0.000015248973,0.17913845],"study_design_scores_gemma":[0.00015469587,0.00027718977,0.000015731874,0.0025268705,0.00004779259,0.000014079062,0.0009611396,0.98453367,0.00047810047,0.005246174,0.0051189805,0.0006255782],"about_ca_topic_score_codex":0.00001786261,"about_ca_topic_score_gemma":0.0000934821,"teacher_disagreement_score":0.9887389,"about_ca_system_score_codex":0.00015779413,"about_ca_system_score_gemma":0.000065938264,"threshold_uncertainty_score":0.999818},"labels":[],"label_agreement":null},{"id":"W2969182950","doi":"10.2200/s00932ed1v01y201906aat008","title":"Deep Learning for Autonomous Vehicle Control: Algorithms, State-of-the-Art, and Future Prospects","year":2019,"lang":"en","type":"article","venue":"Synthesis lectures on advances in automotive technology","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Software deployment; Variety (cybernetics); Deep learning; Computer science; Controller (irrigation); Control (management); Artificial neural network; Control engineering; Artificial intelligence; State (computer science); Engineering; Systems engineering; Software engineering; Algorithm","score_opus":0.002148647022243859,"score_gpt":0.20186003186132803,"score_spread":0.19971138483908418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969182950","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":[0.9081861,0.027705567,0.044519156,0.004605563,0.0015734511,0.0043298397,0.000058333037,0.0045313756,0.00449065],"genre_scores_gemma":[0.99640137,0.0007115447,0.002432445,0.00006479711,0.000029801318,0.00023082928,0.0000012919255,0.000057674966,0.00007026362],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985513,0.00005524501,0.00035326253,0.00043425034,0.000113232156,0.00049270934],"domain_scores_gemma":[0.9989339,0.0004440646,0.000142068,0.00039361732,0.00005688292,0.000029479625],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020247442,0.00030759137,0.0005466073,0.00038883527,0.00010529924,0.000008827085,0.0004049747,0.00040630365,0.00002227841],"category_scores_gemma":[0.00024607457,0.00025175876,0.0000857214,0.0004693067,0.00034376932,0.00012209233,0.000066005865,0.0007788703,0.000015445252],"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":[0.00013130679,0.00007732207,0.01536474,0.00019920435,0.00010993736,0.0000066124485,0.00020554138,0.10092802,0.0039227274,0.010231005,0.000017131124,0.8688064],"study_design_scores_gemma":[0.004179027,0.0014116665,0.055164468,0.00036546032,0.00009846804,0.00006290767,0.0004929924,0.32839555,0.42847976,0.08895072,0.09092982,0.0014691749],"about_ca_topic_score_codex":9.4467026e-7,"about_ca_topic_score_gemma":0.00005172695,"teacher_disagreement_score":0.8673373,"about_ca_system_score_codex":0.00014777665,"about_ca_system_score_gemma":0.000024605128,"threshold_uncertainty_score":0.99999344},"labels":[],"label_agreement":null},{"id":"W2969818521","doi":"10.3390/s19173672","title":"A Personalized Behavior Learning System for Human-Like Longitudinal Speed Control of Autonomous Vehicles","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Beijing Institute of Technology Research Fund Program for Young Scholars; Beijing Institute of Technology; National Natural Science Foundation of China","keywords":"Cruise control; Planner; Reinforcement learning; Smoothness; PID controller; Computer science; Controller (irrigation); Motion (physics); Control (management); Artificial intelligence; Driving simulator; Motion control; Control engineering; Engineering; Robot; Human–computer interaction; Simulation","score_opus":0.011253834689783826,"score_gpt":0.22827022086417673,"score_spread":0.2170163861743929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969818521","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":[0.99634194,0.00017936967,0.0008533028,0.000018410048,0.00023328474,0.00061012397,0.000025117517,0.00086988934,0.0008685683],"genre_scores_gemma":[0.99875456,0.000002974926,0.0002478368,0.000004832447,0.0000371983,0.000028770866,0.000010674973,0.000049441667,0.0008636863],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900925,0.000029319925,0.00032143897,0.00021833242,0.000104464845,0.0003171819],"domain_scores_gemma":[0.99950165,0.00008860448,0.000089154484,0.00021876661,0.00005701459,0.00004478883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019499421,0.00018470737,0.0004252833,0.00011054815,0.00009362678,0.000009466016,0.00014981971,0.00021717921,0.0000465758],"category_scores_gemma":[0.00001083938,0.00019225289,0.00016120578,0.00008637791,0.00008736454,0.000044244458,0.000016464199,0.00025037513,0.00006905285],"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":[0.0004457684,0.00019280713,0.33466184,0.0019447021,0.0009055607,0.00008813003,0.0019211255,0.23648861,0.38610235,0.029101785,0.000117473624,0.008029861],"study_design_scores_gemma":[0.016001515,0.0010379972,0.14516038,0.00039349703,0.00085930567,0.00023122938,0.0050906776,0.75065213,0.06987371,0.00015243064,0.008737083,0.0018100612],"about_ca_topic_score_codex":0.000021165157,"about_ca_topic_score_gemma":0.000004644837,"teacher_disagreement_score":0.5141635,"about_ca_system_score_codex":0.000107937354,"about_ca_system_score_gemma":0.000017124565,"threshold_uncertainty_score":0.7839845},"labels":[],"label_agreement":null},{"id":"W2975762091","doi":"10.1504/ijehv.2019.10024315","title":"A review of autonomous vehicle technology landscape","year":2019,"lang":"en","type":"review","venue":"International Journal of Electric and Hybrid Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Automotive industry; Distraction; Field (mathematics); Engineering; Autonomous system (mathematics); Transport engineering; Computer science; Artificial intelligence","score_opus":0.013624331062789598,"score_gpt":0.2730713613215522,"score_spread":0.2594470302587626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975762091","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":[0.0003954456,0.99823576,0.00015480975,0.00015387763,0.00036318472,0.00018057063,0.000025663341,0.00007560294,0.0004151024],"genre_scores_gemma":[0.0038075917,0.9957555,0.00014149334,0.00006443447,0.00013547916,0.000006636849,0.00000952511,0.00003895644,0.000040367762],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99826676,0.000036840745,0.0011102677,0.00015885851,0.0002203501,0.00020691403],"domain_scores_gemma":[0.99864566,0.00014351,0.00070200453,0.00018908217,0.00027088873,0.000048829803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000329273,0.00027548592,0.001462747,0.000896066,0.000021159141,0.000013670691,0.00073079125,0.00024388585,0.000040104936],"category_scores_gemma":[0.00007842488,0.00022696318,0.00034746152,0.00033723284,0.000065234715,0.00010432692,0.00008089944,0.00078680634,0.000020404092],"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":[0.0000057501725,0.000027466658,0.000024819938,0.01216654,0.0006499625,0.000060774757,0.0000038135445,0.000007217139,0.000013146859,0.00045425186,0.001937087,0.9846492],"study_design_scores_gemma":[0.00028714456,0.00014784478,0.000010763128,0.035182606,0.0005748587,0.0024923128,0.000003594941,0.00044059884,0.00013829932,0.00042898493,0.9600409,0.00025206007],"about_ca_topic_score_codex":0.0000010121253,"about_ca_topic_score_gemma":1.8415706e-7,"teacher_disagreement_score":0.9843971,"about_ca_system_score_codex":0.00010280374,"about_ca_system_score_gemma":0.00024046624,"threshold_uncertainty_score":0.925529},"labels":[],"label_agreement":null},{"id":"W2977673628","doi":"","title":"Vehicle Trajectory Reconstruction Based on Improved Cubic Hermite Interpolation","year":2013,"lang":"en","type":"article","venue":"Journal of Transport Information and Safety","topic":"Autonomous Vehicle Technology and Safety","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":"Ministry of Transportation of Ontario","funders":"","keywords":"Monotone cubic interpolation; Hermite interpolation; Spline interpolation; Cubic Hermite spline; Trajectory; Interpolation (computer graphics); Cubic function; Mathematics; Hermite polynomials; Bicubic interpolation; Computer science; Algorithm; Mathematical analysis; Physics; Artificial intelligence; Statistics; Bilinear interpolation; Motion (physics)","score_opus":0.00347569097369761,"score_gpt":0.17023552674169157,"score_spread":0.16675983576799397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977673628","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":[0.93956625,0.000041303272,0.05433181,0.0003383419,0.0003768207,0.00017683583,0.000008606888,0.00013496043,0.0050250776],"genre_scores_gemma":[0.9981164,0.00009138409,0.0015645514,0.00017458324,0.00002596318,0.0000027641943,0.000008532065,0.0000062954045,0.000009530291],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991738,0.000009652706,0.00058857485,0.000035543057,0.000089392466,0.000103011174],"domain_scores_gemma":[0.99960935,0.000022118997,0.0001567918,0.00007524394,0.000078856356,0.000057650297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019263277,0.00009512319,0.00015056889,0.00019939216,0.00005774566,0.000014224996,0.0000618648,0.00013007762,0.00014917649],"category_scores_gemma":[0.0000061044584,0.00008491837,0.00006909937,0.0000865425,0.00003455501,0.0012092008,0.0000015551024,0.00028826378,0.000015383452],"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":[0.00043593138,0.00004752708,0.0143887345,0.00018730569,0.00010417333,0.0000026782677,0.0013595251,0.042500407,0.007953003,0.00046845851,0.00024872174,0.93230355],"study_design_scores_gemma":[0.0015073688,0.00019858169,0.2520136,0.00008451671,0.000024295792,0.000064402884,0.00031943613,0.74046755,0.0018940697,0.00014660081,0.003101597,0.00017796856],"about_ca_topic_score_codex":0.0000046325695,"about_ca_topic_score_gemma":0.000003485418,"teacher_disagreement_score":0.93212557,"about_ca_system_score_codex":0.000059011018,"about_ca_system_score_gemma":0.000028522225,"threshold_uncertainty_score":0.34628704},"labels":[],"label_agreement":null},{"id":"W2979083863","doi":"10.1109/iccchina.2019.8855835","title":"Smart Cyber Forensics of Rear-End Collision based on Multi-Access Edge Computing","year":2019,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Thompson Rivers University","funders":"","keywords":"Collision; Computer science; Hidden Markov model; Enhanced Data Rates for GSM Evolution; End-to-end principle; Computer security; Edge computing; Artificial intelligence","score_opus":0.013555608419055731,"score_gpt":0.23789747903401165,"score_spread":0.22434187061495592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979083863","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":[0.9139016,0.000016417105,0.0581048,0.00006268465,0.00029915376,0.00021804166,0.0000065562263,0.00055208843,0.026838645],"genre_scores_gemma":[0.9923088,0.0000029722105,0.007315278,0.00008173147,0.000011747479,0.0000015743616,0.000008573003,0.00002288891,0.00024642018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936056,0.000011583997,0.00020876793,0.00014752326,0.00008957202,0.00018198058],"domain_scores_gemma":[0.99949926,0.00009958208,0.000035847446,0.0003039277,0.000032712152,0.000028689388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013909997,0.00011795135,0.0001956248,0.000082873536,0.000034215664,0.000008477477,0.00018134849,0.00016901623,0.00013675205],"category_scores_gemma":[0.000013228391,0.00010737614,0.000049176113,0.00015522588,0.000041176503,0.0000670863,0.000055207714,0.00018741017,0.000092741386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009033606,0.00017358016,0.11945114,0.00027094813,0.00008653921,0.000012720426,0.00019377706,0.8062086,0.008628294,0.009390671,0.002232746,0.053260617],"study_design_scores_gemma":[0.0006219432,0.00005213126,0.042885024,0.00004559431,0.000005716489,0.0000010704807,0.000020985215,0.9075761,0.04767686,0.000056000656,0.0009136333,0.0001449826],"about_ca_topic_score_codex":0.000026682443,"about_ca_topic_score_gemma":0.000021184456,"teacher_disagreement_score":0.10136742,"about_ca_system_score_codex":0.000039546252,"about_ca_system_score_gemma":0.000015181108,"threshold_uncertainty_score":0.43786716},"labels":[],"label_agreement":null},{"id":"W2982093541","doi":"10.1504/ijvas.2019.10024836","title":"Towards an immersive and safer driving experience using computer vision integrated with encoded vibro-tactile feedback","year":2019,"lang":"en","type":"article","venue":"International Journal of Vehicle Autonomous Systems","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":"University of Ottawa","funders":"","keywords":"Haptic technology; Robustness (evolution); Video tracking; Computer science; SAFER; Tracking system; Computer vision; Perception; Artificial intelligence; Engineering; Simulation; Human–computer interaction; Object (grammar); Kalman filter","score_opus":0.007120767418804742,"score_gpt":0.2376012326567451,"score_spread":0.23048046523794036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982093541","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":[0.96206474,0.00018365069,0.03571969,0.00006171905,0.0013490613,0.00016504926,0.0000073393203,0.000094551535,0.00035419496],"genre_scores_gemma":[0.9969365,0.000026733343,0.0027337873,0.00003146983,0.00019347407,0.0000029292776,0.0000041619123,0.000037740327,0.000033177286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855053,0.000055560715,0.0005796155,0.00021831933,0.00035245746,0.00024354219],"domain_scores_gemma":[0.99898,0.000049365066,0.00029487404,0.00018853409,0.0003676318,0.00011958409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002368218,0.0002258943,0.00037209425,0.00026722546,0.000064689324,0.0001436579,0.0004600741,0.00016479791,0.000046186287],"category_scores_gemma":[0.0000071145782,0.00018749118,0.00006309635,0.00012777727,0.00008957618,0.0009303767,0.00007305329,0.00038358461,0.000014888349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066953624,0.00029891444,0.053280696,0.00011297187,0.0012942256,0.00062770327,0.008265477,0.48373342,0.38236544,0.0016709879,0.00010215954,0.06757848],"study_design_scores_gemma":[0.0014976463,0.00083462946,0.016371686,0.00048761888,0.000030490875,0.0022768392,0.002288591,0.9601717,0.014449157,0.000037877136,0.0011670296,0.0003867519],"about_ca_topic_score_codex":0.00011702418,"about_ca_topic_score_gemma":0.000009475096,"teacher_disagreement_score":0.47643828,"about_ca_system_score_codex":0.0003826607,"about_ca_system_score_gemma":0.00010945153,"threshold_uncertainty_score":0.76456684},"labels":[],"label_agreement":null},{"id":"W2989630914","doi":"10.1109/itsc.2019.8916969","title":"Dynamic States Prediction in Autonomous Vehicles: Comparison of Three Different Methods","year":2019,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Coding (social sciences); Process (computing); Fuzzy logic; Artificial intelligence; Vehicle dynamics; Long short term memory; Time series; Perception; Machine learning; Engineering; Artificial neural network","score_opus":0.010565591214977948,"score_gpt":0.2738974221611833,"score_spread":0.2633318309462053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989630914","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":[0.9107887,0.00025359742,0.08597348,0.00003536614,0.0001971872,0.00019536374,0.0000075432868,0.0005428085,0.0020059312],"genre_scores_gemma":[0.9923902,0.000023282773,0.007404496,0.0000053863914,0.0000027095673,0.000012536596,0.000012941568,0.000018479905,0.00012995763],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991954,0.000026683934,0.00037345578,0.00014637852,0.00006296038,0.00019510512],"domain_scores_gemma":[0.9995801,0.00009963915,0.000038395458,0.00024631413,0.00001198637,0.000023583596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016161076,0.00012843052,0.00031720364,0.0001389386,0.000013813485,0.0000039002957,0.00014343356,0.00017438132,0.00016409202],"category_scores_gemma":[0.0000054542325,0.00011495566,0.00004203375,0.00012605816,0.00003989337,0.00006662242,0.000037975566,0.0002574705,0.000044181073],"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":[0.000028143897,0.0001626022,0.63656837,0.00017089419,0.00009249683,9.993253e-7,0.00072903157,0.18313178,0.037281215,0.0027935228,0.00008265874,0.13895829],"study_design_scores_gemma":[0.00026859154,0.00006477663,0.22724636,0.000016681053,0.0000064482238,7.305875e-7,0.00016621922,0.75447273,0.016002873,0.0015611663,0.000106154956,0.000087297856],"about_ca_topic_score_codex":0.000028698958,"about_ca_topic_score_gemma":0.00022168044,"teacher_disagreement_score":0.5713409,"about_ca_system_score_codex":0.000116714866,"about_ca_system_score_gemma":0.000008608988,"threshold_uncertainty_score":0.4687756},"labels":[],"label_agreement":null},{"id":"W2989704640","doi":"10.1109/cvpr42600.2020.01408","title":"CoverNet: Multimodal Behavior Prediction Using Trajectory Sets","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nutrasource","funders":"","keywords":"Trajectory; Computer science; Set (abstract data type); Probabilistic logic; State (computer science); Frame (networking); State space; Artificial intelligence; Machine learning; Data mining; Algorithm; Mathematics; Statistics","score_opus":0.027001099412033554,"score_gpt":0.24500441130863776,"score_spread":0.2180033118966042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989704640","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":[0.9370693,0.0002622287,0.051700708,0.0000501654,0.001312386,0.0005535146,0.0002807067,0.0048757587,0.0038952467],"genre_scores_gemma":[0.99378824,0.000045930654,0.0057452912,0.00003190878,0.00011675125,0.000055362885,0.00009908539,0.00006632769,0.00005110033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989949,0.000016256314,0.00029476677,0.00034314467,0.00012008407,0.00023084399],"domain_scores_gemma":[0.9995232,0.0000137067,0.000044889668,0.0003268979,0.000018534014,0.00007276825],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000059348873,0.00028225456,0.00029615912,0.00008200799,0.000051628544,0.000018038736,0.00023056708,0.00087847817,0.00020608789],"category_scores_gemma":[0.00000704796,0.00031824035,0.00012032492,0.00006292692,0.000052079064,0.000058377675,0.00020965599,0.001130216,0.00005896568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031956013,0.00012418907,0.013808294,0.0005347695,0.00042245872,0.00013816274,0.00070105866,0.92767835,0.0245695,0.00041899647,0.0019469616,0.029625334],"study_design_scores_gemma":[0.0002493383,0.000021070693,0.028925039,0.000035246907,0.00013293365,0.000016941412,0.00003198708,0.965488,0.0039291857,0.00023692202,0.00056553195,0.000367765],"about_ca_topic_score_codex":0.00006724347,"about_ca_topic_score_gemma":0.000016182681,"teacher_disagreement_score":0.056718953,"about_ca_system_score_codex":0.0002480347,"about_ca_system_score_gemma":0.00006242761,"threshold_uncertainty_score":0.999927},"labels":[],"label_agreement":null},{"id":"W2990404565","doi":"10.1108/jicv-07-2019-0008","title":"Prediction of instantaneous driving safety in emergency scenarios based on connected vehicle basic safety messages","year":2019,"lang":"en","type":"article","venue":"Journal of Intelligent and Connected Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Engineering; Computer science; Warning system; Active safety; Computer security; Automotive engineering","score_opus":0.008420245883379432,"score_gpt":0.19913400320643776,"score_spread":0.19071375732305834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990404565","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":[0.99578506,0.0007901089,0.0014482951,0.00022447295,0.0004134592,0.00024980056,0.000027137863,0.000121896854,0.00093974546],"genre_scores_gemma":[0.99769455,0.0020100477,0.00013948772,0.00002900382,0.000041051273,0.0000023477783,0.000006978859,0.000033644763,0.000042859876],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979234,0.00010071047,0.0012105729,0.00020237529,0.00026452448,0.0002984603],"domain_scores_gemma":[0.9987582,0.0004076241,0.00032097244,0.00023382463,0.00018820068,0.000091181166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053038175,0.00025326293,0.0005478373,0.00050028996,0.00006687876,0.000011805679,0.00023911746,0.00027985408,0.00031268122],"category_scores_gemma":[0.00018087741,0.00023104849,0.0001355777,0.0004271965,0.000073549934,0.00017191951,0.000033590983,0.00065306365,0.000010263973],"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":[0.002830497,0.00063806796,0.45292675,0.0005156434,0.00054847024,0.00020428204,0.0017013616,0.20610759,0.21826017,0.0032104636,0.00022406841,0.11283263],"study_design_scores_gemma":[0.003757436,0.0018233191,0.28304198,0.0013708536,0.00012373725,0.00014887787,0.0017216725,0.58531094,0.119075164,0.0010790305,0.0018849146,0.00066207716],"about_ca_topic_score_codex":0.000009304199,"about_ca_topic_score_gemma":0.00005679398,"teacher_disagreement_score":0.37920335,"about_ca_system_score_codex":0.00015157547,"about_ca_system_score_gemma":0.00006228232,"threshold_uncertainty_score":0.9421884},"labels":[],"label_agreement":null},{"id":"W2990831234","doi":"10.1109/jproc.2019.2952735","title":"Learning Driving Models From Parallel End-to-End Driving Data Set","year":2019,"lang":"en","type":"article","venue":"Proceedings of the IEEE","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Real world data; End-to-end principle; Computer science; Heading (navigation); Data set; Global Positioning System; Set (abstract data type); Artificial intelligence; Computer vision; Engineering; Data science","score_opus":0.01762986572833407,"score_gpt":0.216986778027467,"score_spread":0.1993569122991329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990831234","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":[0.9915503,0.00007062825,0.0015838527,0.00027337877,0.00030005717,0.00022502804,0.000017240198,0.0005231432,0.005456329],"genre_scores_gemma":[0.995902,0.000033254728,0.0035087622,0.00003464128,0.000073118514,0.000008853748,0.000006227588,0.000048572958,0.00038453835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883306,0.000004464328,0.00027845873,0.00034327002,0.00020362696,0.00033710522],"domain_scores_gemma":[0.99933016,0.000072817085,0.00009847773,0.00039670308,0.00004611689,0.00005573161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003133266,0.00019067753,0.00027552724,0.000079072866,0.000105200415,0.00003357564,0.0014274572,0.00017155358,0.000059059672],"category_scores_gemma":[0.000066069486,0.00016525168,0.000057656034,0.0002064769,0.00004872037,0.0005188054,0.00054478267,0.0005584794,0.00007707538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036077166,0.000048869504,0.18852654,0.0002479597,0.00044742436,0.0000016938003,0.0050710468,0.4031863,0.3753845,0.004202834,0.0060823024,0.016764447],"study_design_scores_gemma":[0.00040721495,0.0000478878,0.012537695,0.00030674314,0.00006506575,0.000007707924,0.0007785992,0.9392708,0.03670784,0.006123744,0.0032601242,0.00048659486],"about_ca_topic_score_codex":0.00003932197,"about_ca_topic_score_gemma":0.0000120825625,"teacher_disagreement_score":0.5360845,"about_ca_system_score_codex":0.000052020172,"about_ca_system_score_gemma":0.000015130111,"threshold_uncertainty_score":0.6738768},"labels":[],"label_agreement":null},{"id":"W2991484432","doi":"10.1109/iccv.2019.00636","title":"PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction","year":2019,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":436,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Pedestrian; Trajectory; Computer science; Anticipation (artificial intelligence); Context (archaeology); Scale (ratio); Artificial intelligence; Key (lock); Estimation; Motion (physics); Human behavior; Machine learning; Data mining; Engineering; Transport engineering; Geography","score_opus":0.008266477558872182,"score_gpt":0.20633307820959937,"score_spread":0.19806660065072718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991484432","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":[0.4698783,0.0000963601,0.52853614,0.000047765745,0.00006954208,0.0002761766,0.00045703896,0.00030358927,0.00033506987],"genre_scores_gemma":[0.9930782,0.000052689953,0.0061219796,0.000014962477,0.00000786933,0.000022555347,0.0006182281,0.000009182599,0.000074387695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966204,0.0000041300054,0.00009789261,0.000113553695,0.000026343509,0.000096064745],"domain_scores_gemma":[0.9998466,0.000017925408,0.000011773821,0.00009431902,0.000007829308,0.000021555019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010895752,0.000061807536,0.00007488023,0.000046133613,0.000039041304,0.000010131144,0.000025959098,0.00010069497,0.000014900607],"category_scores_gemma":[0.0000041672706,0.000060945124,0.000009956252,0.000028249826,0.000015542395,0.00024794813,0.000013378396,0.00006496407,0.000004646879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050568575,0.00035487884,0.038625006,0.0029333103,0.0004736222,0.0000033558854,0.003469449,0.42984593,0.023322666,0.05038058,0.030636854,0.41944867],"study_design_scores_gemma":[0.00052570156,0.000047963087,0.0035116668,0.00000815051,0.000014104272,0.000005507831,0.000102198625,0.99255896,0.00029879337,0.0019376451,0.00092639815,0.00006288949],"about_ca_topic_score_codex":0.0000052105115,"about_ca_topic_score_gemma":0.000030713007,"teacher_disagreement_score":0.5627131,"about_ca_system_score_codex":0.000015107592,"about_ca_system_score_gemma":0.0000036679633,"threshold_uncertainty_score":0.248527},"labels":[],"label_agreement":null},{"id":"W2991567876","doi":"","title":"Predicting vehicle behaviour using LSTMs and a vector power representation for spatial positions","year":2019,"lang":"en","type":"article","venue":"The European Symposium on Artificial Neural Networks","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Task (project management); Representation (politics); Artificial intelligence; Spatial analysis; Power (physics); Machine learning; Computer vision; Engineering; Remote sensing; Systems engineering; Geography","score_opus":0.013684521483022974,"score_gpt":0.22897090859382235,"score_spread":0.2152863871107994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991567876","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":[0.9887702,0.0000406175,0.007116242,0.0004454878,0.00076058216,0.0005569144,0.00001417343,0.00047476185,0.0018210467],"genre_scores_gemma":[0.99931276,0.000006409312,0.00006111821,0.00011698299,0.0003741094,0.000009421519,0.000014330878,0.00006524068,0.000039605526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989519,0.00014073512,0.00027137602,0.0002489462,0.00008806946,0.00029895426],"domain_scores_gemma":[0.9994363,0.00013112473,0.00006455961,0.0002921165,0.000026100628,0.000049793365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028041372,0.00016543675,0.00014594948,0.000047750844,0.00030440607,0.000062661384,0.00015855451,0.000075839336,0.000013310162],"category_scores_gemma":[0.0000101641535,0.00014179903,0.00006704303,0.00012041013,0.000070984155,0.000104696395,0.000061837854,0.0003240209,0.000023040513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018728003,0.000051003237,0.010051497,0.000011763682,0.000049664795,0.000012453301,0.00058553636,0.89908296,0.07946144,0.0011166647,0.00011368578,0.009276022],"study_design_scores_gemma":[0.00023871793,0.00015918042,0.022966782,0.000021027496,0.000039814007,0.000015991018,0.00007651225,0.9741269,0.002076231,0.00006939891,0.00003919788,0.00017029775],"about_ca_topic_score_codex":0.000018847824,"about_ca_topic_score_gemma":0.000012777447,"teacher_disagreement_score":0.07738521,"about_ca_system_score_codex":0.000031034466,"about_ca_system_score_gemma":0.0000038936164,"threshold_uncertainty_score":0.5782396},"labels":[],"label_agreement":null},{"id":"W2995122008","doi":"10.1109/lra.2019.2961051","title":"MapLite: Autonomous Intersection Navigation Without a Detailed Prior Map","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Autonomous Vehicle Technology and Safety","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":"Université de Montréal; Computer Research Institute of Montréal","funders":"Toyota Research Institute","keywords":"Intersection (aeronautics); Computer science; Planner; Global Positioning System; Road map; Fuse (electrical); Scope (computer science); Frame (networking); Computer vision; Scale (ratio); Point (geometry); Plan (archaeology); Navigation system; Artificial intelligence; Path (computing); Autonomous system (mathematics); Real-time computing; Geography; Cartography; Engineering; Telecommunications; Computer network; Mathematics","score_opus":0.00475685519518063,"score_gpt":0.19671742578388277,"score_spread":0.19196057058870214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995122008","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":[0.9008499,0.000039764032,0.09580512,0.0010873515,0.0007864387,0.00025919566,0.0000026122334,0.0010098906,0.00015977008],"genre_scores_gemma":[0.99546003,0.000007777587,0.004120834,0.00019469712,0.00005032745,0.000014189243,0.000019444547,0.000027679394,0.00010502749],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992708,0.000017731392,0.00024580132,0.00018474385,0.00008848108,0.00019243169],"domain_scores_gemma":[0.9996766,0.000022426355,0.00006147468,0.00018057475,0.000019194846,0.000039717313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009204892,0.00015340724,0.00016750164,0.00011462649,0.00007556244,0.000045726545,0.00007897385,0.00014144616,0.000021657303],"category_scores_gemma":[0.0000027303524,0.00016124747,0.00003965033,0.000098265504,0.00004070033,0.00021912041,0.000014196529,0.00020317946,0.00019611852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024757783,0.000040971372,0.02392637,0.00046829137,0.00016777439,0.000014474501,0.0011994628,0.7498687,0.18224867,0.0030391973,0.0009267126,0.038074654],"study_design_scores_gemma":[0.00057000265,0.000038677463,0.019389916,0.0000803976,0.00002813182,0.000039163024,0.00005542246,0.97299045,0.005529609,0.00022053107,0.0007623728,0.00029533013],"about_ca_topic_score_codex":0.0000033736203,"about_ca_topic_score_gemma":0.0000032888101,"teacher_disagreement_score":0.22312178,"about_ca_system_score_codex":0.00011285139,"about_ca_system_score_gemma":0.0000091989505,"threshold_uncertainty_score":0.6575481},"labels":[],"label_agreement":null},{"id":"W2995454304","doi":"10.1109/tvt.2019.2958622","title":"Deep Learning-Based Driving Maneuver Prediction System","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Advanced driver assistance systems; Driving simulator; Vehicle dynamics; Set (abstract data type); Fuse (electrical); Computer science; Engineering; Artificial neural network; Work (physics); Safe driving; Simulation; Artificial intelligence; Control engineering; Automotive engineering","score_opus":0.0028150077038126154,"score_gpt":0.16858926215265754,"score_spread":0.16577425444884492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995454304","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":[0.38934234,0.00008249571,0.60199773,0.0001272854,0.0006242743,0.00028464873,0.000003958925,0.0069206385,0.00061661616],"genre_scores_gemma":[0.9989724,0.000024520901,0.0005844633,0.000021895783,0.000015669355,0.000118446434,0.0000048099614,0.00007035379,0.00018742475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987166,0.000034877557,0.0003068121,0.00037043056,0.00015428945,0.0004169822],"domain_scores_gemma":[0.9992649,0.000042308453,0.000046949317,0.0005530805,0.000042645628,0.00005009395],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012543879,0.00026657438,0.00029946218,0.0006888972,0.0001989343,0.000012913916,0.0002768586,0.00089873787,0.0001217647],"category_scores_gemma":[0.0000029341506,0.00029087535,0.00013012122,0.00058623153,0.000110518224,0.000096851116,0.000002013355,0.001246444,0.0008774011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011656879,0.00004691451,0.0008723809,0.000083787876,0.00009541588,0.000025187968,0.000020278938,0.97338086,0.008727649,0.00049550657,0.000011119267,0.016229222],"study_design_scores_gemma":[0.00065303594,0.00022439605,0.0003824976,0.00008139852,0.000055922108,0.00008015992,0.0001968846,0.9036875,0.091716155,0.000049957103,0.002581664,0.00029043574],"about_ca_topic_score_codex":0.0000030976853,"about_ca_topic_score_gemma":0.000014541062,"teacher_disagreement_score":0.60963005,"about_ca_system_score_codex":0.00028942316,"about_ca_system_score_gemma":0.00001830341,"threshold_uncertainty_score":0.99995434},"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":"W3000240795","doi":"10.1109/cvci47823.2019.8951694","title":"Trajectory Planning and Tracking for Autonomous Vehicle Considering Human Driver Personality","year":2019,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Ministry of Transportation of Ontario","funders":"","keywords":"Trajectory; Computer science; Tracking (education); Autonomy; Range (aeronautics); Control (management); Advanced driver assistance systems; Simulation; Human–computer interaction; Engineering; Artificial intelligence; Psychology","score_opus":0.01954204663175676,"score_gpt":0.24250741106311594,"score_spread":0.22296536443135917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000240795","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":[0.9878543,0.0002967136,0.003817006,0.00004786862,0.000099671095,0.00024264293,0.0000048135266,0.00095279614,0.006684181],"genre_scores_gemma":[0.9979986,0.000002512328,0.0015813025,0.000061018585,0.000029913035,0.000015617736,0.0000036028107,0.000028962362,0.00027847575],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993036,0.0000079030815,0.00016929056,0.00020693216,0.000047028745,0.00026526503],"domain_scores_gemma":[0.99968684,0.00008580896,0.000021167612,0.0001472491,0.00001455871,0.000044392746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014691158,0.00013790213,0.00019342884,0.00005744504,0.0001285178,0.000021222873,0.00007972341,0.00015243563,0.00012487148],"category_scores_gemma":[0.0000064561086,0.0001465333,0.00004833327,0.000039500632,0.000059952832,0.00014046127,0.000024413275,0.00019128349,0.000015459696],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000567157,0.000079877274,0.48574328,0.0008850657,0.00046265198,0.000034664183,0.007652646,0.031161549,0.39258572,0.04513972,0.00092302647,0.035275076],"study_design_scores_gemma":[0.0037098378,0.00027891927,0.552946,0.00013562289,0.00008393369,0.00007832818,0.002262164,0.33992502,0.08062234,0.0039917864,0.014453219,0.0015128644],"about_ca_topic_score_codex":0.000010450602,"about_ca_topic_score_gemma":0.000012105789,"teacher_disagreement_score":0.31196338,"about_ca_system_score_codex":0.00005279743,"about_ca_system_score_gemma":0.000011196663,"threshold_uncertainty_score":0.59754544},"labels":[],"label_agreement":null},{"id":"W3000596933","doi":"10.1109/cvci47823.2019.8951536","title":"Predictive Freeway Overtaking Strategy for Automated Vehicles Using Deep Reinforcement Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Overtaking; Reinforcement learning; Computer science; Artificial intelligence; Model predictive control; Simulation; Engineering; Transport engineering; Control (management)","score_opus":0.012634416662383595,"score_gpt":0.23653032597044302,"score_spread":0.22389590930805942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000596933","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":[0.68834275,0.00012417887,0.28676206,0.000012959178,0.00019478214,0.00055655465,0.0000019195422,0.005316736,0.018688062],"genre_scores_gemma":[0.99706936,0.000009078112,0.0024417243,0.000021052,0.000027895285,0.000020951678,0.000013878546,0.00003957974,0.00035650714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912167,0.000010974472,0.00024434226,0.00018490666,0.00008829387,0.00034982126],"domain_scores_gemma":[0.9996428,0.0000656585,0.00004726608,0.00016965835,0.00003652331,0.000038100043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012495491,0.00016546261,0.00019304192,0.00008888927,0.0001138195,0.000021493646,0.00013160669,0.00020931529,0.00019791644],"category_scores_gemma":[0.000014035166,0.00016437548,0.000060828814,0.00011222136,0.000026912958,0.00018510787,0.000036597154,0.00022922359,0.000045895526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017237284,0.000005329587,0.001872004,0.000045159388,0.000081768114,0.000001061852,0.0001278808,0.9823469,0.009696833,0.0031710074,0.000038582395,0.0025962535],"study_design_scores_gemma":[0.00047761502,0.00013096222,0.0013728482,0.000026467182,0.000019061215,0.000004296716,0.00033718062,0.9879427,0.008818266,0.00023101429,0.0004387033,0.00020088675],"about_ca_topic_score_codex":0.000023022287,"about_ca_topic_score_gemma":0.000009281936,"teacher_disagreement_score":0.30872658,"about_ca_system_score_codex":0.00013418433,"about_ca_system_score_gemma":0.00002298127,"threshold_uncertainty_score":0.67030376},"labels":[],"label_agreement":null},{"id":"W3003080599","doi":"10.4271/2020-01-0759","title":"A Personalized Deep Learning Approach for Trajectory Prediction of Connected Vehicles","year":2020,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Autonomous Vehicle Technology and Safety","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":"Trajectory; Computer science; Deep learning; Artificial intelligence; Machine learning; Human–computer interaction","score_opus":0.014322467192634778,"score_gpt":0.21211129053862413,"score_spread":0.19778882334598935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003080599","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":[0.9416625,0.0021782666,0.00068352703,0.002551935,0.00023462484,0.003016703,0.00029668518,0.020040821,0.02933492],"genre_scores_gemma":[0.9883004,0.00032197396,0.00990673,0.00044950287,0.0001294757,0.0005119466,0.0001446203,0.00016446332,0.00007087893],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9967637,0.000116855466,0.001025653,0.00087718345,0.00045686422,0.0007597586],"domain_scores_gemma":[0.99840856,0.00043231144,0.0001820382,0.00058416545,0.00010518128,0.00028773682],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042995252,0.00064271037,0.001051021,0.00017099455,0.0003033183,0.00002861538,0.00074732874,0.001218129,0.00017348325],"category_scores_gemma":[0.00071515096,0.0006211018,0.0005170364,0.00070102763,0.0010208298,0.0002779726,0.00015341974,0.0016092628,0.000018408293],"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":[0.00058573706,0.00017100756,0.000267728,0.00026802512,0.00014238086,0.0000075457783,0.00016217532,0.0021497696,0.98189145,0.008317981,0.00066086184,0.005375315],"study_design_scores_gemma":[0.0027542391,0.0027501415,0.96154904,0.00012909767,0.00024151675,0.000058909052,0.0006253404,0.0002113894,0.0018394017,0.0007971283,0.028101746,0.00094203145],"about_ca_topic_score_codex":0.000004392161,"about_ca_topic_score_gemma":0.001028455,"teacher_disagreement_score":0.98005205,"about_ca_system_score_codex":0.0001906407,"about_ca_system_score_gemma":0.000055084412,"threshold_uncertainty_score":0.999624},"labels":[],"label_agreement":null},{"id":"W3003459836","doi":"10.1155/2020/5715686","title":"MPP: A Novel Algorithm for Estimating Vehicle Space Headways from a Single Image","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Headway; Range (aeronautics); Point (geometry); Calibration; Computer science; Algorithm; Usability; Observational error; Image (mathematics); Computer vision; Artificial intelligence; Simulation; Mathematics; Engineering; Statistics; Geometry","score_opus":0.011329710503209947,"score_gpt":0.22343873668144396,"score_spread":0.21210902617823402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003459836","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24128716,0.00014351297,0.75760937,0.00048792685,0.00017140115,0.00009631674,0.00006503711,0.00012448149,0.000014792892],"genre_scores_gemma":[0.44661042,0.000008436427,0.5531848,0.00004273534,0.00011391408,0.0000032696526,0.000016709038,0.000018652741,0.000001084399],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925095,0.000003528407,0.00040094845,0.000098837954,0.00010119014,0.00014453485],"domain_scores_gemma":[0.99954987,0.000066938446,0.00016223066,0.000058714384,0.000089601526,0.00007264696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005976901,0.00011202698,0.00022364708,0.00004172919,0.000046755526,0.000009891742,0.00009765661,0.00008091354,0.0000089011855],"category_scores_gemma":[0.000026374119,0.00011700961,0.00009742555,0.0001172821,0.000022870525,0.0003841044,0.0000012318843,0.00021767037,0.0000023182574],"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":[0.00007819792,0.000048394326,0.000056077406,0.0000492069,0.0000680419,0.000019683343,0.0020634697,0.3501176,0.47202507,0.00007497348,0.000049726517,0.17534955],"study_design_scores_gemma":[0.0032191963,0.00045694486,0.00587342,0.00011323753,0.000108058055,0.000007760877,0.00073825347,0.8728075,0.113702714,0.0013844202,0.0013198173,0.00026865565],"about_ca_topic_score_codex":0.0000020600198,"about_ca_topic_score_gemma":0.0000053570157,"teacher_disagreement_score":0.52268994,"about_ca_system_score_codex":0.00004256326,"about_ca_system_score_gemma":0.000018885607,"threshold_uncertainty_score":0.47715133},"labels":[],"label_agreement":null},{"id":"W3004403095","doi":"10.1155/2020/8684912","title":"In-Lane Localization and Ego-Lane Identification Method Based on Highway Lane Endpoints","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Land, Infrastructure and Transport","keywords":"Computer science; Landmark; GNSS applications; Identification (biology); Computer vision; Artificial intelligence; Inertial navigation system; Orientation (vector space); Point (geometry); Projection (relational algebra); Position (finance); Inertial measurement unit; Global Positioning System; Mathematics; Algorithm; Telecommunications","score_opus":0.006730682438234035,"score_gpt":0.23404319538102558,"score_spread":0.22731251294279153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004403095","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":[0.22336325,0.00012379797,0.77496475,0.0010524428,0.0001903205,0.00012254194,0.000013093397,0.00009533826,0.00007448041],"genre_scores_gemma":[0.98573434,0.00013192062,0.013849052,0.0001766838,0.000043408825,0.0000027298581,0.00004150629,0.000016062735,0.000004311634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919486,0.000024732944,0.00044832155,0.00010682697,0.00013002827,0.00009521856],"domain_scores_gemma":[0.9996325,0.000050885214,0.00015170091,0.0000655275,0.000046944897,0.000052452087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017052075,0.00009853365,0.0001845092,0.00013595165,0.000024861685,0.0000066414023,0.00006419715,0.00009473547,0.000023134207],"category_scores_gemma":[0.000022653989,0.00009889547,0.00003025197,0.00019568761,0.000016128019,0.0002601937,6.7180594e-7,0.00023049199,0.0000040313566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020597149,0.000030425577,0.0012686664,0.000093862545,0.000015649388,0.00005189712,0.0006830192,0.9594884,0.019147014,0.00079172273,0.00003857561,0.018184792],"study_design_scores_gemma":[0.0066527184,0.0008534913,0.39521998,0.00027022045,0.00014567105,0.000034322275,0.00050223444,0.47255015,0.11251524,0.005008243,0.0056467084,0.0006010173],"about_ca_topic_score_codex":0.0000013830729,"about_ca_topic_score_gemma":0.000016427788,"teacher_disagreement_score":0.76237106,"about_ca_system_score_codex":0.00003807596,"about_ca_system_score_gemma":0.000013225984,"threshold_uncertainty_score":0.40328404},"labels":[],"label_agreement":null},{"id":"W3006895970","doi":"10.1109/cvpr42600.2020.01443","title":"Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Pedestrian; Graph; Inference; Adjacency matrix; Social graph; Trajectory; Artificial intelligence; Convolutional neural network; Kernel (algebra); Machine learning; Metric (unit); Adjacency list; Theoretical computer science; Algorithm; Mathematics; Social media; Geography; Engineering","score_opus":0.029810617279447865,"score_gpt":0.25258419060845555,"score_spread":0.22277357332900768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006895970","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":[0.51567215,0.0013828872,0.423086,0.008635385,0.0070592496,0.0053180973,0.0029081197,0.02303688,0.012901234],"genre_scores_gemma":[0.9927514,0.0000070366455,0.0015705385,0.00013033007,0.0026541257,0.00034846773,0.0023223087,0.00008245773,0.00013329322],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983485,0.000043557673,0.0005257016,0.0004544353,0.00017348443,0.00045435678],"domain_scores_gemma":[0.99955004,0.00003557116,0.00012878266,0.00015732969,0.00006600867,0.00006227322],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018243805,0.0003862316,0.0005095639,0.000098451834,0.00059826404,0.000032298085,0.00030897817,0.0011501557,0.00011871374],"category_scores_gemma":[0.0000070977667,0.00046017664,0.00041653225,0.00013916186,0.00018833768,0.000068925954,0.00016259549,0.001146201,0.000010409001],"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":[0.0003369906,0.00018370329,0.01872452,0.0016511156,0.0022697542,0.000021079039,0.002327159,0.25161824,0.0006368348,0.22917235,0.48571673,0.0073415046],"study_design_scores_gemma":[0.00245125,0.00022839583,0.14387593,0.000056561552,0.00044790102,0.0000092742775,0.00018061517,0.5809968,0.00021766666,0.24555922,0.024117054,0.0018593423],"about_ca_topic_score_codex":0.000036756413,"about_ca_topic_score_gemma":0.00018862775,"teacher_disagreement_score":0.4770793,"about_ca_system_score_codex":0.00021014755,"about_ca_system_score_gemma":0.000090683076,"threshold_uncertainty_score":0.999785},"labels":[],"label_agreement":null},{"id":"W3009808098","doi":"10.48550/arxiv.2003.02409","title":"Autonomous Driving at Intersections: A Critical-Turning-Point Approach for Left Turns","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Partially observable Markov decision process; Planner; Computer science; Motion planning; Point (geometry); Self driving; Process (computing); Markov decision process; Generator (circuit theory); Path (computing); Markov chain; Markov process; Artificial intelligence; Transport engineering; Markov model; Engineering; Machine learning; Robot; Mathematics","score_opus":0.04398929318936872,"score_gpt":0.18794429620599995,"score_spread":0.14395500301663122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009808098","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":[0.11439676,0.00016413337,0.8740677,0.00024526505,0.00081244565,0.0006220671,0.00007417481,0.0027475634,0.006869885],"genre_scores_gemma":[0.99506384,0.00010689803,0.0037603055,0.00005841158,0.0001250649,0.0000098673645,0.000082573824,0.00008789405,0.0007051279],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981737,0.000045843546,0.00030283522,0.0009208742,0.000045874007,0.00051087036],"domain_scores_gemma":[0.99879533,0.0001811909,0.00008999138,0.0006634959,0.00007901759,0.00019097468],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015468718,0.00044978026,0.00051839557,0.00025075086,0.00023059655,0.000040691037,0.00067800394,0.0007881942,0.00010147772],"category_scores_gemma":[0.00014471683,0.00057492155,0.00038082243,0.00019767883,0.00023357755,0.00015293938,0.00094284426,0.001295644,0.00006566467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008625087,0.00010972888,0.0023174197,0.0011697235,0.00059996336,0.00015773834,0.0013714242,0.945274,0.0006031328,0.04617638,0.0017795516,0.00035471836],"study_design_scores_gemma":[0.00039929844,0.00006349805,0.00036831884,0.000077844525,0.00022072904,0.00002719686,0.00040135317,0.9779497,0.0004682131,0.017154904,0.0022144418,0.00065449753],"about_ca_topic_score_codex":0.00002124684,"about_ca_topic_score_gemma":0.000077846686,"teacher_disagreement_score":0.8806671,"about_ca_system_score_codex":0.0007853402,"about_ca_system_score_gemma":0.00007893782,"threshold_uncertainty_score":0.9996702},"labels":[],"label_agreement":null},{"id":"W3012444015","doi":"10.23919/fusion43075.2019.9011374","title":"Quickest Detection of Abnormal Vehicle Movements on Highways","year":2019,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Process (computing); Track (disk drive); Position (finance); State (computer science); Real-time computing; Vehicle dynamics; Artificial intelligence; Simulation; Engineering; Automotive engineering; Algorithm","score_opus":0.004426025891742372,"score_gpt":0.1772570044118264,"score_spread":0.17283097852008403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012444015","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":[0.9683918,0.0000147918145,0.0012091593,0.000014829646,0.00020003541,0.000088558714,0.000002431193,0.00063126156,0.029447155],"genre_scores_gemma":[0.99947214,0.000007452569,0.00007137595,0.00003698931,0.000010926653,0.0000039590286,0.000001560528,0.00001156073,0.00038402772],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959356,0.0000043905106,0.00012800343,0.00008207698,0.00006343968,0.00012853439],"domain_scores_gemma":[0.9997646,0.00001458413,0.000017079474,0.00017586538,0.00001016416,0.00001767726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000052971576,0.00006988086,0.000093006485,0.000058241585,0.000018449644,0.0000020428822,0.00008107751,0.000100911115,0.000119812314],"category_scores_gemma":[0.000002116318,0.00006638108,0.000026938264,0.00007900878,0.00001613477,0.00006150539,0.00001552941,0.000114618575,0.00045627187],"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":[0.00007186568,0.000117763775,0.02607125,0.00010946684,0.000108329055,0.0000034475497,0.00016916524,0.026544161,0.8733118,0.012443962,0.0002445114,0.06080425],"study_design_scores_gemma":[0.00045124986,0.00018798772,0.06341681,0.000009707923,0.0000033212266,0.0000011842268,0.00004908769,0.058560614,0.8755986,0.00019829453,0.0013967395,0.00012642065],"about_ca_topic_score_codex":0.00001668431,"about_ca_topic_score_gemma":0.00001267673,"teacher_disagreement_score":0.06067783,"about_ca_system_score_codex":0.000032953238,"about_ca_system_score_gemma":0.0000035189844,"threshold_uncertainty_score":0.5864604},"labels":[],"label_agreement":null},{"id":"W3016691006","doi":"10.1002/rob.21958","title":"Zeus: A system description of the two‐time winner of the collegiate SAE autodrive competition","year":2020,"lang":"en","type":"preprint","venue":"Journal of Field Robotics","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Christian Studies; Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Competition (biology); ZEUS (particle detector); Suite; Software; Computer science; Simulation; Engineering; Political science; Operating system","score_opus":0.011898258343090495,"score_gpt":0.19841950963333574,"score_spread":0.18652125129024524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016691006","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":[0.6210864,0.002086,0.32835233,0.022724748,0.014132887,0.0017200717,0.00013917837,0.0004153676,0.009343029],"genre_scores_gemma":[0.9973239,0.00007285378,0.0023178866,0.000056072953,0.00014293272,0.0000011756697,8.9892393e-7,0.000016605161,0.000067730245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987921,0.00008460146,0.0007170055,0.000080634476,0.00021539425,0.000110247114],"domain_scores_gemma":[0.99864084,0.00008398647,0.00073344406,0.00034558083,0.0001676652,0.000028505232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022303962,0.0001509613,0.0004614177,0.000065107924,0.00005239076,0.0000099366,0.00058555015,0.00038024446,0.0000142866975],"category_scores_gemma":[0.00006574571,0.00009664907,0.00032111196,0.00013973651,0.00007440333,0.000040686642,0.00024951508,0.0012477711,0.0000023094804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027937225,0.000029551267,0.00078002247,0.000568761,0.00032437753,0.00000856743,0.000472157,0.98265904,0.0076792343,0.0056047463,0.0015140409,0.00033155605],"study_design_scores_gemma":[0.0027180335,0.00059557316,0.059602086,0.01081197,0.0020081766,0.0005281441,0.0014308156,0.83887714,0.069284126,0.012249431,0.0010211044,0.0008734171],"about_ca_topic_score_codex":0.000008463091,"about_ca_topic_score_gemma":0.000010076938,"teacher_disagreement_score":0.37623745,"about_ca_system_score_codex":0.00012655747,"about_ca_system_score_gemma":0.00011770315,"threshold_uncertainty_score":0.5421014},"labels":[],"label_agreement":null},{"id":"W3022272795","doi":"10.48550/arxiv.2005.06582","title":"Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Autonomous Vehicle Technology and Safety","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":"York University","funders":"","keywords":"Anticipation (artificial intelligence); Computer science; Pedestrian; Artificial intelligence; Feature (linguistics); Action (physics); Machine learning; Event (particle physics); Trajectory; Point (geometry); Recurrent neural network; Sensor fusion; Motion (physics); Variable (mathematics); Computer vision; Artificial neural network; Engineering","score_opus":0.11013488960895608,"score_gpt":0.19578482921845614,"score_spread":0.08564993960950006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022272795","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":[0.9606688,0.00001963477,0.038265146,0.000106053434,0.00008823569,0.00010484167,0.000004466918,0.00035299387,0.00038983917],"genre_scores_gemma":[0.9997199,0.000054855707,0.000083212195,0.00003850298,0.000030079704,1.2660159e-7,0.000008795282,0.000012576981,0.00005191514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995023,0.000026796852,0.00008350882,0.00019542772,0.000024037001,0.00016789873],"domain_scores_gemma":[0.99976844,0.000016590346,0.000030010868,0.000111326706,0.000017417753,0.00005619482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004655558,0.000103295104,0.00012421873,0.00009512271,0.000066969085,0.000007073338,0.000108296845,0.00019141032,0.00003069006],"category_scores_gemma":[0.000015933701,0.00012877244,0.000035109315,0.00044577185,0.00003684636,0.00022398216,0.000032495893,0.00027576927,0.000027593112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026323955,0.00006177064,0.05033255,0.00009526517,0.000060831888,0.0003242785,0.0011298744,0.89223313,0.040019043,0.011105826,0.00024696736,0.0041272235],"study_design_scores_gemma":[0.00080409314,0.000044139713,0.015669443,0.000015579311,0.00002398648,0.0000023649711,0.00063003384,0.97968763,0.0021034782,0.00027079572,0.00056940044,0.00017903518],"about_ca_topic_score_codex":0.000037463724,"about_ca_topic_score_gemma":0.00013279096,"teacher_disagreement_score":0.08745452,"about_ca_system_score_codex":0.0001293101,"about_ca_system_score_gemma":0.000019994364,"threshold_uncertainty_score":0.5251188},"labels":[],"label_agreement":null},{"id":"W3028846692","doi":"10.48550/arxiv.2005.14711","title":"PnPNet: End-to-End Perception and Prediction with Tracking in the Loop","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Autonomous Vehicle Technology and Safety","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":"University of Toronto","funders":"","keywords":"Trajectory; Computer science; Context (archaeology); Artificial intelligence; Object (grammar); Computer vision; End-to-end principle; Tracking (education); Exploit; Component (thermodynamics); Motion (physics); Video tracking; Perception; Joint (building); Engineering","score_opus":0.03685520485911134,"score_gpt":0.1654811313779798,"score_spread":0.12862592651886845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028846692","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":[0.9541628,0.000031419426,0.043227427,0.00023033893,0.00008194513,0.00028230864,0.000020085003,0.00038307745,0.0015806208],"genre_scores_gemma":[0.9995238,0.00016734905,0.00011624999,0.000058747537,0.000038714057,0.0000019715678,0.000020056505,0.000019701793,0.000053388932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992533,0.000041450676,0.00010650436,0.00038337067,0.000041938438,0.00017346934],"domain_scores_gemma":[0.99959445,0.000029084364,0.000032697408,0.00027925888,0.00001671649,0.000047784728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012652812,0.00018217576,0.00017144078,0.00015068003,0.000072274954,0.000024054823,0.00026593453,0.00028174854,0.00002832182],"category_scores_gemma":[0.000006300653,0.00017074116,0.000034969733,0.00028694398,0.00008100373,0.00011410854,0.00013060006,0.00078859733,0.000019459854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012514148,0.000044128195,0.04537282,0.00018952822,0.00013197826,0.0003566055,0.0039172694,0.93066835,0.00069556735,0.007222903,0.00015135968,0.01112434],"study_design_scores_gemma":[0.00070055766,0.00015863692,0.3579767,0.00016059111,0.00016089303,0.000029368004,0.0023178174,0.6328448,0.00010809534,0.0042264466,0.0008010378,0.00051502854],"about_ca_topic_score_codex":0.00004941551,"about_ca_topic_score_gemma":0.00017178399,"teacher_disagreement_score":0.3126039,"about_ca_system_score_codex":0.0001266702,"about_ca_system_score_gemma":0.000021961061,"threshold_uncertainty_score":0.69626224},"labels":[],"label_agreement":null},{"id":"W3033006756","doi":"10.1109/crv50864.2020.00024","title":"Domain Generalization via Optical Flow: Training a CNN in a Low-Quality Simulation to Detect Obstacles in the Real World","year":2020,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Generalization; Optical flow; Artificial intelligence; Artificial neural network; Machine learning; Domain (mathematical analysis); Bridge (graph theory); Robotics; Quality (philosophy); Flow (mathematics); Robot; Image (mathematics)","score_opus":0.033189915812343755,"score_gpt":0.2749973904257021,"score_spread":0.24180747461335833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033006756","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":[0.6138429,0.000009561606,0.3835662,0.0006516473,0.0000176786,0.00017652694,0.0000010784657,0.00024482806,0.001489584],"genre_scores_gemma":[0.9858954,0.0000036586844,0.013587403,0.0004263037,0.000036314945,0.000024969686,0.0000059266326,0.000013829062,0.0000061910655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913067,0.000074762705,0.00032009897,0.00016549483,0.00009658302,0.00021239225],"domain_scores_gemma":[0.9996644,0.00013996506,0.000014333842,0.00013148678,0.000008193331,0.00004159571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003731177,0.000106524894,0.00015678344,0.00010420483,0.00003161094,0.00001421132,0.00013860565,0.00010653765,0.000030599564],"category_scores_gemma":[0.00004679948,0.00009067749,0.00002815962,0.0006343417,0.000020511994,0.00007684541,0.000023463255,0.00021791962,0.000019016377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026495285,0.0000070550423,0.0020248436,0.000016458382,0.0000047013514,0.000009609107,0.004847403,0.9502803,0.0017821991,0.005021151,0.0000051351967,0.035974614],"study_design_scores_gemma":[0.000329164,0.000023299102,0.058592483,0.000013699753,0.000002409014,4.3166452e-7,0.00035627602,0.93752325,0.0010152315,0.0018294279,0.00017233721,0.00014201159],"about_ca_topic_score_codex":0.000046462144,"about_ca_topic_score_gemma":0.0021719423,"teacher_disagreement_score":0.3720525,"about_ca_system_score_codex":0.0000788539,"about_ca_system_score_gemma":0.000011511938,"threshold_uncertainty_score":0.36977205},"labels":[],"label_agreement":null},{"id":"W3033136248","doi":"10.1088/1757-899x/831/1/012013","title":"Automated Driving System Disengagement Analysis and Testing Recommendations","year":2020,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","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":"Western University","funders":"","keywords":"Automotive industry; Disengagement theory; Test (biology); Advanced driver assistance systems; Engineering; Software; Computer science; Systems engineering; Aeronautics; Artificial intelligence","score_opus":0.017789695633843153,"score_gpt":0.2102804493503822,"score_spread":0.19249075371653906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033136248","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":[0.98779106,0.00003393906,0.008482664,0.0004362456,0.00016937262,0.00010918602,0.000021091611,0.002695244,0.0002612143],"genre_scores_gemma":[0.9944308,0.000044880933,0.0054425173,0.000016710428,0.00002593608,0.000018330617,0.000005513742,0.000012735132,0.0000026196815],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906516,0.000011101701,0.0002482838,0.00026336985,0.000117209434,0.00029490094],"domain_scores_gemma":[0.9995962,0.00003462564,0.000034628618,0.00012911356,0.00007180812,0.000133623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035687152,0.00016209457,0.00025778642,0.00017854775,0.00026720445,0.00023735144,0.00016870271,0.000055618933,0.000018698664],"category_scores_gemma":[0.00014555732,0.00016419476,0.000012116169,0.00094179896,0.00015236737,0.0005100005,0.00013646836,0.000099738776,0.000004366146],"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":[0.0000026508453,0.00000254293,0.00096652744,0.00023421731,0.000079353544,0.000007163485,0.000748906,0.0112114,0.9796477,0.0039450047,0.000013274377,0.0031412684],"study_design_scores_gemma":[0.00009796617,0.000036979764,0.03192137,0.00007706212,0.00009217358,0.000015331027,0.00077512366,0.80723107,0.15927646,0.000009534198,0.00016584946,0.00030109324],"about_ca_topic_score_codex":0.000009028713,"about_ca_topic_score_gemma":0.0000034675068,"teacher_disagreement_score":0.8203712,"about_ca_system_score_codex":0.000048060105,"about_ca_system_score_gemma":0.000028810276,"threshold_uncertainty_score":0.6695668},"labels":[],"label_agreement":null},{"id":"W3034012845","doi":"10.1109/crv50864.2020.00026","title":"Leveraging Temporal Data for Automatic Labelling of Static Vehicles","year":2020,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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 Toronto","funders":"","keywords":"Computer science; Labelling","score_opus":0.05788399651429717,"score_gpt":0.25208730628011894,"score_spread":0.19420330976582179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034012845","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":[0.5393027,0.00023407774,0.4569219,0.0012541108,0.00005774368,0.00025254907,0.00006249816,0.0013813542,0.00053303206],"genre_scores_gemma":[0.9534444,0.000010017908,0.04632865,0.00011853598,0.000017126658,0.000005426387,0.00004148817,0.000019202396,0.000015162597],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943525,0.0000059504755,0.00023205242,0.00013227976,0.000050681672,0.00014381013],"domain_scores_gemma":[0.99956363,0.000084444015,0.000028069897,0.00027662577,0.000012818805,0.00003442255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001166652,0.00008195091,0.00016982878,0.000027304786,0.00003150975,0.00000558315,0.00031461465,0.00005508365,0.000039681418],"category_scores_gemma":[0.00003876004,0.000079636106,0.000020861147,0.00009593575,0.000028803384,0.00010777102,0.00006901902,0.0000878768,0.000012300997],"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":[0.00009634126,0.0002157858,0.032507684,0.009831808,0.0014688084,0.000038793893,0.010025451,0.2110185,0.059462115,0.024506366,0.045908883,0.60491943],"study_design_scores_gemma":[0.0002372885,0.00002517723,0.00022738523,0.000015899966,0.000017004813,7.844896e-7,0.0002086017,0.99053776,0.0061190343,0.0006981675,0.0018181528,0.00009472014],"about_ca_topic_score_codex":0.0000056953318,"about_ca_topic_score_gemma":0.0000021887781,"teacher_disagreement_score":0.77951926,"about_ca_system_score_codex":0.000009550369,"about_ca_system_score_gemma":0.000016141623,"threshold_uncertainty_score":0.3247466},"labels":[],"label_agreement":null},{"id":"W3034181129","doi":"10.1109/jiot.2020.3001026","title":"Vehicle Software Engineering (VSE): Research and Practice","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Social software engineering; Software development; Software Engineering Process Group; Software; Software analytics; Automotive industry; Context (archaeology); Software engineering; Software construction; Engineering","score_opus":0.03755380083380097,"score_gpt":0.27973502650555176,"score_spread":0.2421812256717508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034181129","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":[0.93351114,0.0014543275,0.05987421,0.0030197627,0.00043558993,0.000093840135,0.0000018400962,0.000448163,0.0011611264],"genre_scores_gemma":[0.9887834,0.0001329828,0.010727288,0.00016242985,0.00012128267,0.0000015781776,2.3216708e-7,0.000028041008,0.0000427356],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909765,0.00003391348,0.000284722,0.00011680761,0.00021282515,0.00025405892],"domain_scores_gemma":[0.9992829,0.0003000728,0.000060062703,0.00009284007,0.00013227192,0.00013183655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069651095,0.000105361214,0.0001721149,0.00011270575,0.00004875262,0.00004789841,0.0002913627,0.00013549406,0.000033661447],"category_scores_gemma":[0.0008565376,0.00010534978,0.000038820162,0.00014866414,0.000085102656,0.00047583433,0.000071686125,0.0012984683,0.000023565834],"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":[0.0015492615,0.00038518145,0.010130656,0.0017874176,0.0024202403,0.0022159733,0.09619154,0.05363428,0.59405994,0.008795643,0.076254375,0.15257546],"study_design_scores_gemma":[0.0025616353,0.0020227209,0.0023909793,0.00082624244,0.00011355058,0.0052334573,0.003004261,0.59288067,0.30632496,0.0026519252,0.0808802,0.0011094123],"about_ca_topic_score_codex":0.0000076092447,"about_ca_topic_score_gemma":2.719342e-7,"teacher_disagreement_score":0.5392464,"about_ca_system_score_codex":0.000049892333,"about_ca_system_score_gemma":0.000018138466,"threshold_uncertainty_score":0.5641271},"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":"W3036209432","doi":"10.1109/wcnc45663.2020.9120734","title":"Performance Evaluation of Driving Behavior Identification Models through CAN-BUS Data","year":2020,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","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 Ottawa","funders":"","keywords":"Computer science; Identification (biology); Profiling (computer programming); Energy consumption; Data modeling; Intelligent transportation system; Variety (cybernetics); Advanced driver assistance systems; Machine learning; Artificial intelligence; Engineering; Transport engineering; Database","score_opus":0.10831387520438421,"score_gpt":0.28812901205705005,"score_spread":0.17981513685266584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036209432","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":[0.95651126,0.00007981062,0.039794017,0.00019603329,0.000063388674,0.00019811527,0.000015673691,0.0003681488,0.002773541],"genre_scores_gemma":[0.9984674,0.000061649225,0.0013195225,0.000019113686,0.000015945578,0.000020852118,0.00007100886,0.000011388651,0.000013127938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999373,0.000011794659,0.00021210396,0.00014886803,0.00016310964,0.00009112124],"domain_scores_gemma":[0.9994805,0.000008382707,0.00003448161,0.00040334626,0.000056116936,0.000017162023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024492023,0.00006486286,0.00008860146,0.000018469955,0.00003446677,0.0000051792836,0.00029420812,0.000074702504,0.000066117536],"category_scores_gemma":[0.00001932791,0.00006716942,0.000012059634,0.00011961869,0.000025726626,0.00042225103,0.00006656505,0.00009391038,0.000016415062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004967585,0.000049028895,0.011464513,0.000097190925,0.00006703009,8.458426e-7,0.0018875713,0.69840795,0.039373092,0.0030149214,0.00081470254,0.24481817],"study_design_scores_gemma":[0.00011783515,0.000007780602,0.01894665,0.0000050034355,0.000059590126,8.084241e-7,0.00006643774,0.96316767,0.01737289,0.00014607937,0.000038638467,0.00007060777],"about_ca_topic_score_codex":0.00001099089,"about_ca_topic_score_gemma":0.000020660298,"teacher_disagreement_score":0.26475972,"about_ca_system_score_codex":0.000032944026,"about_ca_system_score_gemma":0.000025947982,"threshold_uncertainty_score":0.27390897},"labels":[],"label_agreement":null},{"id":"W3036303882","doi":"10.1007/978-3-030-50347-5_22","title":"Combining Asynchronous Events and Traditional Frames for Steering Angle Prediction","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Artificial intelligence; Asynchronous communication; Convolutional neural network; Deep learning; Event (particle physics); Artificial neural network; Computer vision; Path (computing); Curvature; Trajectory; State (computer science); Real-time computing; Algorithm","score_opus":0.014410080159468635,"score_gpt":0.19937585790642007,"score_spread":0.18496577774695144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036303882","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":[0.0016080762,0.00039876008,0.9959023,0.00017438829,0.00065633724,0.00024550402,0.000043544605,0.00035392639,0.00061713654],"genre_scores_gemma":[0.94513786,0.00003290086,0.054392718,0.0001337346,0.00022537731,0.000014276818,0.000020993022,0.00003083659,0.000011318765],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904484,0.0000029918785,0.00019967595,0.00038474528,0.00015403499,0.00021369144],"domain_scores_gemma":[0.99957734,0.0001501137,0.00004070911,0.00015325566,0.000023974573,0.00005458134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013247052,0.00020590062,0.0002295394,0.00016864245,0.00012846754,0.000031024843,0.00025846247,0.0002899046,0.000006859477],"category_scores_gemma":[0.000018427196,0.00022276648,0.00004003482,0.000077775,0.00020754621,0.00012628995,0.00008532051,0.00047872515,0.0000026578039],"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":[0.000012487566,0.000014809653,0.00034933985,0.00020835998,0.00005355153,0.000017499839,0.0006664109,0.3017453,0.000412826,0.01919234,0.000025245134,0.6773018],"study_design_scores_gemma":[0.0002543692,0.00016069566,0.0008734229,0.00017569854,0.000010817105,0.000031163574,2.7882567e-7,0.8849964,0.000669719,0.11207177,0.0004988839,0.00025678196],"about_ca_topic_score_codex":7.359441e-7,"about_ca_topic_score_gemma":0.000004127915,"teacher_disagreement_score":0.9435298,"about_ca_system_score_codex":0.00013213667,"about_ca_system_score_gemma":0.00005370874,"threshold_uncertainty_score":0.9084153},"labels":[],"label_agreement":null},{"id":"W3044279555","doi":"10.1155/2020/1528028","title":"Sectional Information-Based Collision Warning System Using Roadside Unit Aggregated Connected-Vehicle Information for a Cooperative Intelligent Transport System","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Land, Infrastructure and Transport","keywords":"Collision; Warning system; Transport engineering; Computer science; Engineering; Simulation; Computer security; Telecommunications","score_opus":0.011693010979694472,"score_gpt":0.21682594471892816,"score_spread":0.2051329337392337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044279555","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":[0.5272408,0.00004066981,0.47181594,0.000049440823,0.00021622732,0.00032589058,0.00006449736,0.00022784728,0.000018679053],"genre_scores_gemma":[0.99129474,0.000012074793,0.008224634,0.000059293026,0.000054437973,0.00001998996,0.00031522833,0.000019036464,5.504778e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998206,0.000024513238,0.0012420668,0.00008294445,0.00025750167,0.00018697412],"domain_scores_gemma":[0.9984091,0.000085568674,0.00056860317,0.00007225285,0.00075305894,0.00011141963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022600511,0.00019566674,0.0003429246,0.00024213977,0.00017740323,0.000024640849,0.00012324798,0.00018133543,0.0000053414583],"category_scores_gemma":[0.00003583751,0.00019442374,0.00013644663,0.000452349,0.000028240198,0.0019926506,0.0000010995735,0.0003186509,0.0000050356803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051610044,0.0000090161775,0.0010910935,0.00053571706,0.00008262872,0.000006505162,0.0025734967,0.9901381,0.0025461814,0.0007919534,0.000005461458,0.0017037382],"study_design_scores_gemma":[0.0026617572,0.00033507214,0.00981364,0.0004981585,0.000121856065,0.000025869676,0.0077331997,0.9486189,0.028568765,0.000008588787,0.001353907,0.00026031488],"about_ca_topic_score_codex":0.000005806649,"about_ca_topic_score_gemma":0.000010723435,"teacher_disagreement_score":0.46405396,"about_ca_system_score_codex":0.00030079836,"about_ca_system_score_gemma":0.00016474209,"threshold_uncertainty_score":0.792837},"labels":[],"label_agreement":null},{"id":"W3045933035","doi":"10.1109/icc40277.2020.9149085","title":"A Novel Lane Departure Warning System for Improving Road Safety","year":2020,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Lane departure warning system; Computer science; Offset (computer science); Key (lock); Warning system; Advanced driver assistance systems; Intelligent transportation system; Image processing; Real-time computing; Artificial intelligence; Computer vision; Image (mathematics); Transport engineering; Computer security; Engineering; Telecommunications","score_opus":0.009164938222034097,"score_gpt":0.1862290121958954,"score_spread":0.1770640739738613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045933035","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":[0.025497872,0.00017615019,0.9609314,0.0007697393,0.00017241311,0.0003837107,0.000032255535,0.0042667314,0.0077696904],"genre_scores_gemma":[0.9855045,0.0000026764124,0.014080965,0.00012681146,0.000111499045,0.00003321813,0.00001541416,0.00003277871,0.000092104114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932986,0.0000042182132,0.00019779165,0.0001761494,0.000049812606,0.00024215294],"domain_scores_gemma":[0.9997237,0.00003480168,0.000025192929,0.00013106709,0.000017257064,0.00006799604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009652241,0.0001301379,0.0001908451,0.000024237997,0.000091712434,0.000010812049,0.00014418729,0.00018391994,0.000018176108],"category_scores_gemma":[0.00003361871,0.000120644174,0.000058189184,0.00009417873,0.000013890362,0.00006554119,0.00003568186,0.00020780167,0.000040768216],"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":[0.00034691184,0.00006351656,0.0055775675,0.004733708,0.0006980078,0.00005100084,0.0025166806,0.120082356,0.37448382,0.07233322,0.0030857215,0.4160275],"study_design_scores_gemma":[0.00079350575,0.00005524634,0.002040372,0.000018888564,0.000036942023,0.000026676888,0.00034044604,0.9752196,0.008664667,0.000012631202,0.012498727,0.000292291],"about_ca_topic_score_codex":0.000012659809,"about_ca_topic_score_gemma":0.0000116561905,"teacher_disagreement_score":0.96000665,"about_ca_system_score_codex":0.00006328027,"about_ca_system_score_gemma":0.00001317345,"threshold_uncertainty_score":0.49197266},"labels":[],"label_agreement":null},{"id":"W3046335568","doi":"10.1155/2020/9405760","title":"Study on a Right-Turning Intelligent Vehicle Collision Warning and Avoidance Algorithm Based on Monte Carlo Simulation","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Collision; Collision avoidance; Monte Carlo method; Simulation; Computer science; Active safety; Algorithm; Control theory (sociology); Engineering; Mathematics; Automotive engineering; Artificial intelligence; Statistics; Computer security","score_opus":0.010620968506793218,"score_gpt":0.24078113586913608,"score_spread":0.23016016736234285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046335568","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":[0.9043606,0.000108618835,0.09494675,0.00015265016,0.00012253538,0.00019241954,0.0000051057355,0.00009954341,0.000011782382],"genre_scores_gemma":[0.9960687,0.00003679763,0.0037225732,0.000077450844,0.0000639562,0.000003875502,0.000002618399,0.000022253553,0.0000017733884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990676,0.000028629116,0.0004295993,0.00013880349,0.00021524743,0.00012009012],"domain_scores_gemma":[0.99945253,0.00014851404,0.00017538088,0.000073582036,0.000075331685,0.00007465381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014392052,0.00014151116,0.00022227498,0.00010849958,0.0000872438,0.000010796617,0.00007167312,0.00007560836,0.0000032203013],"category_scores_gemma":[0.000034305973,0.00013067677,0.00005317708,0.00017653688,0.000015807187,0.00021996279,0.0000013251821,0.00043915748,0.00000195863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021050998,0.000055350036,0.0016926246,0.000015754214,0.000023227696,0.00006686994,0.0031147213,0.9566813,0.0011360734,0.0000062485387,0.000001921464,0.036995362],"study_design_scores_gemma":[0.0014560809,0.0013474885,0.082440354,0.00011674092,0.00004114464,0.0000012807293,0.0011885845,0.9067746,0.0061957357,0.00003167715,0.00025981484,0.0001465172],"about_ca_topic_score_codex":8.2884713e-7,"about_ca_topic_score_gemma":0.0000037922202,"teacher_disagreement_score":0.09170811,"about_ca_system_score_codex":0.00006401096,"about_ca_system_score_gemma":0.000013556726,"threshold_uncertainty_score":0.5328844},"labels":[],"label_agreement":null},{"id":"W3046576094","doi":"10.1007/978-3-030-54407-2_15","title":"Homography-Based Vehicle Pose Estimation from a Single Image by Using Machine-Learning for Wheel-Region and Tire-Road Contact Point Detection","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Homography; Vanishing point; Robustness (evolution); Metric (unit); Flagging; Machine vision; Truck; Image (mathematics); Automotive engineering; Engineering","score_opus":0.009516551168784543,"score_gpt":0.20134953893227883,"score_spread":0.19183298776349428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046576094","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":[0.021977337,0.0005937899,0.976191,0.00016474519,0.00025550477,0.00032953534,0.000020070158,0.00042627438,0.00004175583],"genre_scores_gemma":[0.9292406,0.000011358778,0.070461504,0.00013379565,0.00007110464,0.0000063294538,0.00003030629,0.000042701642,0.0000023275393],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878746,0.000014681389,0.0002580205,0.0005357899,0.00014828483,0.0002557835],"domain_scores_gemma":[0.999354,0.0002087571,0.00011341015,0.00021144534,0.00004579901,0.000066611916],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013483172,0.00030303336,0.00030536027,0.00027649073,0.0002204051,0.00011205705,0.00023402488,0.00032645962,0.0000031515642],"category_scores_gemma":[0.00005152447,0.00032162855,0.000075432814,0.0001713558,0.00022580002,0.00021903559,0.00007610586,0.0005875501,0.0000017712854],"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":[0.000033039094,0.00001140053,0.000102470294,0.000075082724,0.000019229961,0.0000145982285,0.0001788628,0.08496104,0.06090669,0.000032222528,0.0000022271627,0.85366315],"study_design_scores_gemma":[0.00036985113,0.00018615516,0.00009196503,0.00016845918,0.00002297469,0.0000102857175,2.938058e-7,0.96802145,0.026930254,0.0038054711,0.00007758718,0.00031525097],"about_ca_topic_score_codex":0.00007363207,"about_ca_topic_score_gemma":0.00008722987,"teacher_disagreement_score":0.9072632,"about_ca_system_score_codex":0.00018540543,"about_ca_system_score_gemma":0.000045592526,"threshold_uncertainty_score":0.9999236},"labels":[],"label_agreement":null},{"id":"W3046603013","doi":"10.1155/2020/8859689","title":"UB-LSTM: A Trajectory Prediction Method Combined with Vehicle Behavior Recognition","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Australian Research Council; National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Acceleration; Trajectory optimization; Control theory (sociology); Artificial intelligence; Simulation; Algorithm; Control (management)","score_opus":0.011212569498272273,"score_gpt":0.2224824401525719,"score_spread":0.21126987065429964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046603013","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":[0.88349843,0.0000928934,0.1156436,0.00017746078,0.00013669982,0.00015644026,0.00002756599,0.00021834546,0.00004855123],"genre_scores_gemma":[0.96479714,0.0000800847,0.03493419,0.0000508551,0.00005982289,0.00001322574,0.00003743075,0.000024860881,0.0000023736316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99923587,0.000018479024,0.00039218916,0.00009437679,0.00014500173,0.00011406244],"domain_scores_gemma":[0.99958897,0.000026021242,0.0001489203,0.00005533348,0.00010442625,0.00007634772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009600334,0.00011205048,0.00019945939,0.00007587028,0.000040103256,0.0000051098496,0.00006828387,0.000099822515,0.00002692804],"category_scores_gemma":[0.0000067172905,0.00010343212,0.00006532777,0.00019773321,0.000022246923,0.00041613154,5.527367e-7,0.00033971446,0.000003443911],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023030734,0.0001871411,0.00916528,0.00018887741,0.00023198988,0.00021096964,0.0048213652,0.5246831,0.21864611,0.00009743056,0.000090187954,0.23937447],"study_design_scores_gemma":[0.007868599,0.004148552,0.84712696,0.00018648298,0.0007016813,0.00008846044,0.0016667665,0.012657149,0.12327993,0.0006501634,0.0011241733,0.0005010529],"about_ca_topic_score_codex":6.863878e-7,"about_ca_topic_score_gemma":0.000010754748,"teacher_disagreement_score":0.83796173,"about_ca_system_score_codex":0.00004261326,"about_ca_system_score_gemma":0.00002439119,"threshold_uncertainty_score":0.42178395},"labels":[],"label_agreement":null},{"id":"W3047914067","doi":"10.1109/tits.2020.3009674","title":"Improved Vehicle LiDAR Calibration With Trajectory-Based Hand-Eye Method","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","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":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Notation; Algorithm; Mathematics; Computer science; Arithmetic","score_opus":0.016391463531784806,"score_gpt":0.22934146416229248,"score_spread":0.21295000063050767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047914067","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":[0.049654983,0.000077645265,0.94748026,0.00030419402,0.00038369195,0.0006157275,0.0001461636,0.0012686524,0.00006869693],"genre_scores_gemma":[0.9964575,0.00001792292,0.0029219817,0.00017217749,0.00004431707,0.00021714064,0.000041811192,0.00007481597,0.00005230832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985349,0.00006113065,0.0005528529,0.00035801367,0.0002226829,0.00027042668],"domain_scores_gemma":[0.99939173,0.000070867565,0.00007429108,0.00022898066,0.0000730843,0.00016105095],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012408913,0.0002987229,0.00032785579,0.00015065687,0.00018732401,0.00005278378,0.0001585,0.00025687277,0.00009734498],"category_scores_gemma":[0.0000010739791,0.00028324922,0.00013053621,0.00044844393,0.00006797059,0.00020045706,8.5801375e-8,0.00042713858,0.000040996747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020765438,0.00006388365,0.00008489628,0.00018618019,0.00013489176,0.000009498188,0.0013120174,0.9679783,0.02582043,0.00010753838,0.00002440681,0.0040702987],"study_design_scores_gemma":[0.0005112264,0.00026890895,0.00011935941,0.000045180146,0.00007631878,0.0000013559631,0.00036531888,0.60511595,0.39229622,0.0000032271605,0.00093185436,0.00026508086],"about_ca_topic_score_codex":0.00006248373,"about_ca_topic_score_gemma":0.00013818136,"teacher_disagreement_score":0.94680256,"about_ca_system_score_codex":0.00008318615,"about_ca_system_score_gemma":0.000062825544,"threshold_uncertainty_score":0.999962},"labels":[],"label_agreement":null},{"id":"W3048702864","doi":"10.48550/arxiv.2008.06041","title":"DSDNet: Deep Structured self-Driving Network","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Exploit; Inference; Artificial intelligence; Plan (archaeology); Probabilistic logic; Motion (physics); Deep neural networks; Object (grammar); Machine learning; Sample (material); Scale (ratio); Artificial neural network; Computer security","score_opus":0.023006746079008634,"score_gpt":0.14649287904966196,"score_spread":0.12348613297065333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048702864","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":[0.6861401,0.0005235836,0.2915457,0.00012892767,0.001943124,0.0005399624,0.000025791634,0.008461312,0.0106914835],"genre_scores_gemma":[0.9974665,0.00034246608,0.0016733116,0.00005445437,0.00023671586,9.589522e-7,0.000032154447,0.000067589746,0.0001258384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985704,0.00004550817,0.00020723509,0.0006569824,0.00004657848,0.0004733101],"domain_scores_gemma":[0.99898905,0.000041775744,0.00009481022,0.0006830973,0.000034112054,0.0001571718],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007854493,0.00039989786,0.00043163268,0.000106590094,0.00014442991,0.000025757596,0.000800789,0.0008301332,0.00012289795],"category_scores_gemma":[0.000011015434,0.0005175613,0.00018942043,0.00039033723,0.000079263715,0.00009260473,0.00073036534,0.0014288133,0.00013451088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010579538,0.000009402846,0.0052043325,0.00009861973,0.00032233773,0.0002624946,0.00014744171,0.97188306,0.000032181466,0.020703001,0.0006874681,0.000639081],"study_design_scores_gemma":[0.00029663378,0.000020319712,0.005939911,0.000047537,0.00017312824,0.0000058193395,0.000051996205,0.9461682,0.00010003629,0.043391842,0.0031799932,0.00062461814],"about_ca_topic_score_codex":0.000010620845,"about_ca_topic_score_gemma":0.00008422338,"teacher_disagreement_score":0.31132638,"about_ca_system_score_codex":0.00025869475,"about_ca_system_score_gemma":0.00005662359,"threshold_uncertainty_score":0.9997276},"labels":[],"label_agreement":null},{"id":"W3048976565","doi":"10.1109/tits.2021.3102407","title":"On Social Interactions of Merging Behaviors at Highway On-Ramps in Congested Traffic","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Process (computing); Preference; Computer science; Task (project management); Space (punctuation); Human behavior; Filter (signal processing); Human–computer interaction; Transport engineering; Engineering; Artificial intelligence; Systems engineering","score_opus":0.021916374434087758,"score_gpt":0.2549370005134895,"score_spread":0.23302062607940174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048976565","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":[0.92397076,0.00005895182,0.07285046,0.000079834936,0.0017215546,0.0003199789,0.00020844127,0.00045427456,0.00033574237],"genre_scores_gemma":[0.999233,0.00008679779,0.000030920997,0.000018147315,0.000020012889,0.0001469316,0.00006252648,0.000048933074,0.00035270382],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983219,0.00006841015,0.00079677184,0.00031157574,0.00024663916,0.00025471317],"domain_scores_gemma":[0.99934345,0.0001880794,0.00009255595,0.0002346813,0.00008314882,0.000058077487],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011704314,0.00025383805,0.0003823433,0.00043338188,0.000153136,0.000012826377,0.00011697053,0.00025145104,0.00022964634],"category_scores_gemma":[0.0000020219989,0.00028880738,0.00019631731,0.000512578,0.00006641411,0.0000953402,2.1435211e-7,0.000539125,0.00008573032],"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":[0.00011719107,0.00039240767,0.00006803798,0.00009901866,0.00011878999,0.000040381008,0.002328131,0.9815259,0.006684161,0.0011809062,0.00006169062,0.0073834145],"study_design_scores_gemma":[0.0025140038,0.00046549883,0.009345843,0.0011514431,0.0003346881,0.000044727763,0.0070961085,0.06696692,0.907223,0.000048291822,0.0034467625,0.0013627385],"about_ca_topic_score_codex":0.0000386672,"about_ca_topic_score_gemma":0.0012551674,"teacher_disagreement_score":0.91455895,"about_ca_system_score_codex":0.00033773077,"about_ca_system_score_gemma":0.00003651346,"threshold_uncertainty_score":0.9999564},"labels":[],"label_agreement":null},{"id":"W3080427371","doi":"10.1007/s11432-019-2844-3","title":"Investigating the dynamic memory effect of human drivers via ON-LSTM","year":2020,"lang":"en","type":"article","venue":"Science China Information Sciences","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Trajectory; Computer science; Economic shortage; Long short term memory; Sequence (biology); Range (aeronautics); Artificial intelligence; Estimation; Machine learning; Artificial neural network; Recurrent neural network; Engineering","score_opus":0.007419482379214578,"score_gpt":0.23811060425424946,"score_spread":0.23069112187503488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080427371","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":[0.9834846,0.000007989919,0.0017312325,0.0007793156,0.00012482653,0.00014996475,0.0000017498875,0.00022432447,0.013495946],"genre_scores_gemma":[0.9995901,0.0000021526455,0.00017272161,0.00021932115,0.0000063307307,0.00000489291,8.096446e-7,0.0000017927501,0.0000019030039],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990257,0.000017859182,0.0002384655,0.000104624356,0.00040811326,0.00020525079],"domain_scores_gemma":[0.9996201,0.000059505826,0.000096976655,0.00013921956,0.000024264958,0.00005992666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012593373,0.00009152706,0.00010470017,0.00014596064,0.0007628047,0.00006059618,0.00086824986,0.00003676141,0.000013679261],"category_scores_gemma":[0.00020335928,0.000057502126,0.000028879631,0.0013850166,0.0024179756,0.0014431588,0.000075275915,0.00017915913,0.000051270734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006762987,0.0000100018715,0.010422614,0.00023696388,0.000018510855,7.282009e-7,0.04253078,0.58291286,0.11329573,0.018304417,0.00035399865,0.23190665],"study_design_scores_gemma":[0.00014920993,0.00034978794,0.021369895,0.000020470867,0.000003910059,0.0000024632927,0.0008164959,0.8921416,0.084276825,0.0006863029,0.000055900135,0.00012713287],"about_ca_topic_score_codex":0.0000084279145,"about_ca_topic_score_gemma":0.0000017111245,"teacher_disagreement_score":0.30922875,"about_ca_system_score_codex":0.000038525126,"about_ca_system_score_gemma":0.000054872613,"threshold_uncertainty_score":0.8909131},"labels":[],"label_agreement":null},{"id":"W3080505519","doi":"10.1109/itsc45102.2020.9294326","title":"Two-Stream Networks for Lane-Change Prediction of Surrounding Vehicles","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ministerio de Ciencia, Innovación y Universidades","keywords":"Computer science; Action recognition; Artificial intelligence; Context (archaeology); Action (physics); Computer vision; Machine learning; Geography","score_opus":0.041562446926988,"score_gpt":0.24542629310797157,"score_spread":0.20386384618098358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080505519","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":[0.20082067,0.0027164207,0.7728442,0.0006796764,0.0029406887,0.0022239804,0.0007552354,0.0064533567,0.010565786],"genre_scores_gemma":[0.9947646,0.0002864657,0.0039875624,0.000029817504,0.00039956204,0.00018176447,0.00025656517,0.00004971701,0.000043903536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999113,0.000012419946,0.00033058057,0.00025118177,0.00006921922,0.00022360074],"domain_scores_gemma":[0.99948436,0.000082825805,0.00007715276,0.00027747624,0.00003274996,0.00004544895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001434523,0.00021563435,0.0003644462,0.0000830367,0.000040050818,0.000011051318,0.00022231282,0.0005525174,0.000022350327],"category_scores_gemma":[0.000015032817,0.00022670935,0.00012882655,0.00007698986,0.00004214171,0.000051372517,0.00017283202,0.00054032216,0.000004054949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021786444,0.00016370426,0.04175014,0.005074056,0.0019334983,0.000023755725,0.0016904483,0.66401285,0.004507951,0.073126785,0.010723009,0.19677591],"study_design_scores_gemma":[0.0004154849,0.00005324536,0.005264143,0.00013406252,0.00009289369,0.0000031803188,0.00007604011,0.98497003,0.0019404495,0.005328898,0.0014631369,0.00025845575],"about_ca_topic_score_codex":0.000055942524,"about_ca_topic_score_gemma":0.000031949698,"teacher_disagreement_score":0.79394394,"about_ca_system_score_codex":0.00006838005,"about_ca_system_score_gemma":0.000015628635,"threshold_uncertainty_score":0.9244939},"labels":[],"label_agreement":null},{"id":"W3080564557","doi":"10.1177/0278364920950795","title":"On infusing reachability-based safety assurance within planning frameworks for human–robot vehicle interactions","year":2020,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Reachability; Computer science; Leverage (statistics); Trajectory; Active safety; Collision; Anticipation (artificial intelligence); Controller (irrigation); Planner; Collision avoidance; Robot; Advanced driver assistance systems; Simulation; Engineering; Computer security; Automotive engineering; Artificial intelligence","score_opus":0.08787062107509358,"score_gpt":0.38458152193309264,"score_spread":0.29671090085799906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080564557","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":[0.3292492,0.0002182579,0.61883086,0.048702523,0.0011467347,0.00037776676,0.000025761337,0.00016408094,0.0012848182],"genre_scores_gemma":[0.99195176,0.000009789163,0.0072882217,0.00038143215,0.000307845,0.000005589838,0.0000049061387,0.000025487192,0.000024938108],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840677,0.0001015054,0.00048629392,0.00011499763,0.00066925946,0.00022116043],"domain_scores_gemma":[0.99747616,0.0013280174,0.00012727597,0.00016336919,0.00081042614,0.00009474978],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001791128,0.0001065037,0.00017870095,0.00018773429,0.00021101903,0.000072396506,0.0008506123,0.0001452081,0.000019771356],"category_scores_gemma":[0.0012819285,0.00008509314,0.00009805657,0.00021404783,0.00014866336,0.00012809578,0.00007558408,0.0023483476,0.000010660933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033687375,0.00004134268,0.00025342914,0.000016364684,0.00012369089,0.000019456264,0.0006864867,0.9783883,0.00703176,0.010170078,0.0013636106,0.001568575],"study_design_scores_gemma":[0.0013141066,0.00050824205,0.0011168486,0.0003868035,0.000021449914,0.000035400015,0.0005504726,0.9550594,0.009238666,0.029095383,0.002485815,0.00018739222],"about_ca_topic_score_codex":0.0000039646,"about_ca_topic_score_gemma":0.000008837033,"teacher_disagreement_score":0.66270256,"about_ca_system_score_codex":0.00033511026,"about_ca_system_score_gemma":0.00015147569,"threshold_uncertainty_score":0.99995327},"labels":[],"label_agreement":null},{"id":"W3082368790","doi":"10.1155/2020/8495264","title":"Learn to Make Decision with Small Data for Autonomous Driving: Deep Gaussian Process and Feedback Control","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundo para o Desenvolvimento das Ciências e da Tecnologia; Science and Technology Development Fund; National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Process (computing); Artificial intelligence; Kriging; Exploit; Brake; Gaussian process; Sample (material); Acceleration; Machine learning; Control (management); Field (mathematics); Gaussian; Engineering","score_opus":0.013373239888756914,"score_gpt":0.23499161376994754,"score_spread":0.22161837388119063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082368790","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":[0.4765299,0.00012070522,0.52233166,0.0007042778,0.00004122822,0.0001865507,0.000019172645,0.000057976136,0.000008557912],"genre_scores_gemma":[0.93228734,0.000040970826,0.06744367,0.0001272152,0.000046012392,0.000006541944,0.000020055386,0.000025845453,0.0000023841799],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9992322,0.0000044576573,0.0003534736,0.00015706722,0.00009415798,0.00015864345],"domain_scores_gemma":[0.999474,0.00005897439,0.00012112052,0.00012587263,0.00008682584,0.00013324241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010885406,0.00011975861,0.00024084414,0.00006226514,0.00004232301,0.000012760329,0.00020151891,0.00007408306,0.000003499225],"category_scores_gemma":[0.0000361766,0.0001023938,0.000026282894,0.00012261972,0.000021056738,0.00026051726,0.0000024095593,0.00019340504,9.218246e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009728799,0.000021282733,0.0034479124,0.00011762855,0.00007043154,0.000030175395,0.0016927938,0.776361,0.0023941533,0.00010214393,0.000012843908,0.21477675],"study_design_scores_gemma":[0.021824133,0.0047904393,0.7252374,0.0006671158,0.0007725501,0.00013898648,0.00427942,0.19989257,0.007520559,0.0030079435,0.030511284,0.0013575441],"about_ca_topic_score_codex":2.9192788e-7,"about_ca_topic_score_gemma":0.00012961838,"teacher_disagreement_score":0.72178954,"about_ca_system_score_codex":0.000020981985,"about_ca_system_score_gemma":0.000033778273,"threshold_uncertainty_score":0.4175498},"labels":[],"label_agreement":null},{"id":"W3091006519","doi":"10.1613/jair.1.12531","title":"MADRaS : Multi Agent Driving Simulator","year":2021,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Microsoft (Canada); University of Alberta","funders":"Indian Institute of Technology Kharagpur; Eidgenössische Technische Hochschule Zürich","keywords":"Reinforcement learning; Computer science; Variety (cybernetics); Simulation; Track (disk drive); Interface (matter); Driving simulator; Generalization; Artificial intelligence; Operating system","score_opus":0.13556367506213882,"score_gpt":0.3935038671666261,"score_spread":0.25794019210448726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091006519","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":[0.8179413,0.0016127568,0.17824611,0.00072581344,0.0006508996,0.00009457984,0.0000017148035,0.00009623581,0.0006305869],"genre_scores_gemma":[0.9957659,0.00065187516,0.003183203,0.000012318096,0.00021619382,0.0000018130369,4.3839108e-7,0.00002233328,0.00014592358],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982003,0.00012424089,0.0006208255,0.00013271277,0.0004681049,0.00045381955],"domain_scores_gemma":[0.99865854,0.0002577576,0.00005823214,0.00024094994,0.0006327175,0.00015180801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016068808,0.000105509454,0.00022765323,0.00030264558,0.00016440389,0.0000631547,0.00035931636,0.00016900881,0.00039937272],"category_scores_gemma":[0.0006232449,0.0001003371,0.00012487412,0.0006078089,0.00016548877,0.0001665351,0.00009999365,0.0012085808,0.00024273884],"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":[0.000038607017,0.00033948215,0.0014431046,0.00006869947,0.00019790576,0.001591528,0.0012590635,0.16925214,0.19058672,0.010445738,0.00070397486,0.624073],"study_design_scores_gemma":[0.00005703571,0.00014353228,0.0012405744,0.0001161497,0.000013429576,0.00018709163,0.0024963145,0.2730862,0.7054213,0.011190775,0.005835745,0.00021184831],"about_ca_topic_score_codex":0.0000027730514,"about_ca_topic_score_gemma":0.00004185271,"teacher_disagreement_score":0.6238612,"about_ca_system_score_codex":0.00018962915,"about_ca_system_score_gemma":0.00019006217,"threshold_uncertainty_score":0.52507496},"labels":[],"label_agreement":null},{"id":"W3091927272","doi":"10.1109/iemtronics51293.2020.9216400","title":"Autonomous Mobility Vehicle","year":2020,"lang":"en","type":"article","venue":"2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Interfacing; Computer science; Installation; Image processing; Embedded system; Object detection; IVMS; Automotive engineering; Real-time computing; Artificial intelligence; Engineering; Computer hardware; Vehicle tracking system; Image (mathematics); Kalman filter","score_opus":0.014625467724355138,"score_gpt":0.2245360565727387,"score_spread":0.20991058884838357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091927272","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":[0.90558153,0.008274615,0.060414936,0.013664108,0.0014439474,0.0008253913,0.00019996597,0.0023159771,0.0072795292],"genre_scores_gemma":[0.99398005,0.0040923445,0.00079983514,0.00054940704,0.00022582193,0.000059302718,0.000054145385,0.000068645415,0.00017047729],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99752885,0.000038779628,0.0005640678,0.0006888621,0.0003261831,0.00085327966],"domain_scores_gemma":[0.99899834,0.00006768248,0.00011425922,0.0003782664,0.00015332001,0.0002881212],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026913232,0.0004397646,0.00045156886,0.0000824558,0.0001653058,0.00012116201,0.0007939462,0.00037829147,0.0003488103],"category_scores_gemma":[0.00004451554,0.0004885047,0.00015423015,0.00021270895,0.00014113678,0.00023905687,0.00016532988,0.001207312,0.00017718645],"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":[0.00024185094,0.00025405432,0.0007257895,0.000178933,0.0011086878,0.000050450562,0.0011496294,0.038532022,0.06727398,0.7862682,0.0038715962,0.10034484],"study_design_scores_gemma":[0.0010294152,0.0003192681,0.00015406484,0.000019525252,0.00005525292,0.000023879742,0.0001332833,0.8473607,0.014756188,0.011977936,0.123514794,0.0006556959],"about_ca_topic_score_codex":0.000014252446,"about_ca_topic_score_gemma":0.00007655028,"teacher_disagreement_score":0.80882865,"about_ca_system_score_codex":0.00048577887,"about_ca_system_score_gemma":0.00041828476,"threshold_uncertainty_score":0.99975663},"labels":[],"label_agreement":null},{"id":"W3091936287","doi":"10.1155/2020/8867757","title":"Safety of Autonomous Vehicles","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":242,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Commercialization; Transport engineering; Automation; Disengagement theory; Process (computing); Occupational safety and health; Vehicle safety; Automotive industry; Business; Computer security; Risk analysis (engineering); Engineering; Computer science; Aeronautics; Automotive engineering; Medicine; Marketing","score_opus":0.006843731743575368,"score_gpt":0.2064994454312283,"score_spread":0.19965571368765295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091936287","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":[0.9665922,0.0005259986,0.03192242,0.00044815947,0.00014033327,0.00005966051,0.000014088059,0.00009920911,0.00019792703],"genre_scores_gemma":[0.9922479,0.00026543374,0.0073884376,0.00003805117,0.000038985338,6.09738e-7,0.0000047124754,0.000013313076,0.0000025973027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9992409,0.000006164122,0.00051642896,0.00005227204,0.00009750598,0.000086732645],"domain_scores_gemma":[0.9996306,0.000024206554,0.00017086443,0.0000536575,0.000068871705,0.000051835348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006553753,0.000077348835,0.0002154182,0.000051380437,0.000017741027,0.0000014960256,0.00010344154,0.00007181368,0.000020131141],"category_scores_gemma":[0.000010175961,0.0000755919,0.00008371487,0.00013567653,0.000031562795,0.00020748242,8.667044e-7,0.0002018803,0.0000023754096],"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":[0.00025162476,0.000022715409,0.001858325,0.00010244795,0.00008599447,0.000030025798,0.0022613439,0.8548429,0.07771474,0.0013356864,0.00004454721,0.061449636],"study_design_scores_gemma":[0.00510785,0.0011136966,0.6565304,0.00019844122,0.00023944449,0.000035773643,0.001885386,0.009909783,0.3013487,0.0032470082,0.019831404,0.00055215677],"about_ca_topic_score_codex":3.774831e-7,"about_ca_topic_score_gemma":0.0000025887848,"teacher_disagreement_score":0.84493315,"about_ca_system_score_codex":0.000022414548,"about_ca_system_score_gemma":0.000024231284,"threshold_uncertainty_score":0.30825484},"labels":[],"label_agreement":null},{"id":"W3092263114","doi":"10.1109/tits.2020.3027421","title":"A Highly Efficient Vehicle Taillight Detection Approach Based on Deep Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Deanship of Scientific Research, King Saud University; National Natural Science Foundation of China; National Research Foundation Singapore","keywords":"Brake; Object detection; Computer science; Bounding overwatch; Artificial intelligence; Pyramid (geometry); Pooling; Pedestrian detection; Computer vision; Train; Deep learning; Pattern recognition (psychology); Minimum bounding box; Real-time computing; Engineering; Image (mathematics); Mathematics; Automotive engineering; Pedestrian; Geography","score_opus":0.012605262422364961,"score_gpt":0.194047347630348,"score_spread":0.18144208520798305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092263114","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":[0.09061801,0.00007487521,0.90566516,0.00009472411,0.00063365966,0.00051913114,0.00002878341,0.0017644153,0.00060125004],"genre_scores_gemma":[0.9992124,0.00003143495,0.00023723897,0.00010158305,0.00005079454,0.00022567106,0.000025724312,0.00007156414,0.0000436045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982807,0.0000667116,0.0005933296,0.00041433223,0.00033192255,0.00031299185],"domain_scores_gemma":[0.9993905,0.00007635549,0.000074948475,0.00021815344,0.000081126476,0.00015893698],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014765492,0.00030939368,0.00030138975,0.00024266519,0.0002580261,0.000035006247,0.00016413089,0.0002913365,0.000048885035],"category_scores_gemma":[0.0000032555154,0.00032053023,0.00018460241,0.00056747504,0.000048240578,0.00007450766,1.4143545e-7,0.000720062,0.00024633476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000112049325,0.00011424048,0.000018716837,0.00014469243,0.00006147817,0.0000064252645,0.001107129,0.98156357,0.0040522288,0.00010644642,0.0000062266904,0.012706771],"study_design_scores_gemma":[0.00036662634,0.00025763418,0.00011020454,0.00003881676,0.000050525356,0.0000017425334,0.00066056196,0.8840351,0.1125177,0.0000018657347,0.0016762341,0.00028300547],"about_ca_topic_score_codex":0.000021657357,"about_ca_topic_score_gemma":0.000016369504,"teacher_disagreement_score":0.90859437,"about_ca_system_score_codex":0.00016899155,"about_ca_system_score_gemma":0.000018565514,"threshold_uncertainty_score":0.99992466},"labels":[],"label_agreement":null},{"id":"W3093260109","doi":"","title":"Artificial intelligence for smart electric vehicle braking system","year":2020,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Automotive engineering; Braking system; Electric vehicle; Computer science; Engineering; Brake; Power (physics)","score_opus":0.06893754660720529,"score_gpt":0.29416515170621604,"score_spread":0.22522760509901074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093260109","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":[0.07680776,0.00032804356,0.9179356,0.00086415716,0.00025771206,0.0006200157,0.000008441328,0.0028635361,0.00031471608],"genre_scores_gemma":[0.99698156,0.00001860657,0.0025276248,0.000021230913,0.0002293253,0.00014204244,0.000003279477,0.00006894833,0.000007371009],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982388,0.000028743038,0.00036084067,0.0003233934,0.0002870917,0.00076110754],"domain_scores_gemma":[0.9991444,0.00032050363,0.0000133524745,0.00023851628,0.000083664985,0.00019959833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008539384,0.00016891472,0.00025174278,0.00017560883,0.00014106181,0.000042718544,0.0004543176,0.00026012654,0.000019843388],"category_scores_gemma":[0.00046107185,0.00018351746,0.00008730936,0.0008889889,0.000022003107,0.00007521852,0.000101606245,0.00090451125,0.00015627312],"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":[0.000058027148,0.00002932477,0.0000071301556,0.0007617552,0.00007974941,0.00003619265,0.00014472354,0.09626448,0.4599536,0.3539853,0.0002440636,0.08843564],"study_design_scores_gemma":[0.000045576053,0.00013058915,0.00001544774,0.000028793509,0.0000051570696,0.000005050046,0.000044860306,0.8230822,0.1746025,0.0008061111,0.0010759671,0.00015774382],"about_ca_topic_score_codex":0.0000030128513,"about_ca_topic_score_gemma":0.0000011796576,"teacher_disagreement_score":0.9201738,"about_ca_system_score_codex":0.00019172842,"about_ca_system_score_gemma":0.0000315751,"threshold_uncertainty_score":0.7483625},"labels":[],"label_agreement":null},{"id":"W3094393283","doi":"10.1155/2020/8866876","title":"Identifying Big Five Personality Traits through Controller Area Network Bus Data","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Youth Innovation Promotion Association; Youth Innovation Promotion Association of the Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Big Five personality traits; Personality; Trait; Controller (irrigation); Acceleration; Computer science; Applied psychology; Psychology; Simulation; Social psychology","score_opus":0.041843289376463415,"score_gpt":0.2572435859473544,"score_spread":0.215400296570891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094393283","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":[0.6415463,0.0020802803,0.35400295,0.0012455807,0.0005639822,0.00015546204,0.000088306166,0.000184589,0.00013255115],"genre_scores_gemma":[0.9897612,0.0003566103,0.009300896,0.00018051325,0.00032862503,0.0000016808755,0.00004572504,0.000019423262,0.00000533723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99899375,0.000015304247,0.0005077169,0.00014021464,0.0001657947,0.00017721843],"domain_scores_gemma":[0.99951005,0.000050004743,0.00019694181,0.00010298011,0.00007595029,0.00006408624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014485107,0.00012759145,0.00030003453,0.000024670024,0.00006390011,0.0000101798905,0.0002688032,0.00010079742,0.000020340482],"category_scores_gemma":[0.000023396185,0.00012286543,0.00008516625,0.00019211565,0.00003369089,0.00061817945,0.0000036831404,0.00039265436,0.000002850864],"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":[0.00034927868,0.000025777785,0.0009664422,0.00009573316,0.00027840148,0.00009587519,0.0054222983,0.9442872,0.0058826245,0.0006401465,0.0006936213,0.041262604],"study_design_scores_gemma":[0.012151846,0.0005879421,0.86702305,0.00042932935,0.00086481497,0.00008762317,0.005642852,0.05443762,0.0031466982,0.014771519,0.039668836,0.0011878639],"about_ca_topic_score_codex":0.0000020538055,"about_ca_topic_score_gemma":0.000040446757,"teacher_disagreement_score":0.8898496,"about_ca_system_score_codex":0.000028616583,"about_ca_system_score_gemma":0.000026528676,"threshold_uncertainty_score":0.5010307},"labels":[],"label_agreement":null},{"id":"W3094973372","doi":"10.1109/ojvt.2020.3036582","title":"A Review of Driving Simulation Technology and Applications","year":2020,"lang":"en","type":"review","venue":"IEEE Open Journal of Vehicular Technology","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; McMaster University","funders":"","keywords":"Chassis; Powertrain; Computer science; Driving simulator; Simulation; Automotive engineering; Perception; Human–computer interaction; Systems engineering; Control engineering; Engineering; Aerospace engineering","score_opus":0.020058497895949694,"score_gpt":0.3144009603268409,"score_spread":0.2943424624308912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094973372","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":[0.000013237536,0.98204905,0.01578414,0.0004461724,0.00009852158,0.0012136322,0.0000118384105,0.00023012266,0.00015331409],"genre_scores_gemma":[0.0007797734,0.99590075,0.003021556,0.000034585337,0.000056344456,0.00012127446,0.00000485972,0.00007430533,0.000006572918],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975886,0.00006300991,0.001658349,0.0002934318,0.00013244749,0.00026419066],"domain_scores_gemma":[0.9977667,0.000115862094,0.0012008549,0.00060598226,0.00023753066,0.00007310611],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00048190996,0.0004063059,0.0028766515,0.0014625369,0.000072705145,0.000016716835,0.0017231285,0.0013215549,0.000016933784],"category_scores_gemma":[0.0001718112,0.00036142612,0.0002849699,0.0022344773,0.00033887292,0.00015294626,0.00034438242,0.00166358,0.000017908353],"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":[0.0000011087202,0.000023421277,0.000006859546,0.022803206,0.00038932177,0.00004998663,0.0000035060746,0.00020555186,0.000023209634,0.0018573174,0.00013271227,0.9745038],"study_design_scores_gemma":[0.00017728587,0.00012803909,3.8101322e-7,0.0437494,0.00089726696,0.0011005347,0.000012235883,0.00038211868,0.00008774616,0.0016620688,0.9515273,0.00027562727],"about_ca_topic_score_codex":2.8280596e-7,"about_ca_topic_score_gemma":6.857013e-7,"teacher_disagreement_score":0.97422814,"about_ca_system_score_codex":0.00011812318,"about_ca_system_score_gemma":0.00020686252,"threshold_uncertainty_score":0.99997497},"labels":[],"label_agreement":null},{"id":"W3096272849","doi":"10.1155/2020/8894060","title":"Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Youth Innovation Promotion Association; National Key Research and Development Program of China; Hefei Institutes of Physical Science, Chinese Academy of Sciences; Natural Science Foundation of Anhui Province; Youth Innovation Promotion Association of the Chinese Academy of Sciences; Chinese Academy of Sciences","keywords":"Trajectory; Computer science; Knowledge base; A priori and a posteriori; Baseline (sea); Artificial neural network; Artificial intelligence; Machine learning","score_opus":0.004807795055674757,"score_gpt":0.18693693936674122,"score_spread":0.18212914431106647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096272849","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":[0.9820169,0.0015743668,0.015765151,0.00011637136,0.0002326433,0.00009673685,0.000014571684,0.0000818683,0.00010134211],"genre_scores_gemma":[0.9982571,0.0004407098,0.0011110401,0.000031183703,0.00010940656,0.0000035751357,0.000019794134,0.000019109242,0.000008066834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99925,0.000014624209,0.0004179049,0.00008573579,0.00009046144,0.00014128526],"domain_scores_gemma":[0.9997617,0.000017953307,0.00009734397,0.00004916349,0.000010622925,0.00006320127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006372912,0.00009651666,0.00017164958,0.000044140856,0.00002556658,0.0000025585173,0.00008690029,0.00010441713,0.000014648563],"category_scores_gemma":[0.000003767816,0.000104502295,0.00005324377,0.00015446344,0.00002279194,0.00027940678,0.0000010016538,0.00033355056,0.0000055001865],"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":[0.00008867538,0.000041211282,0.024630195,0.00002493332,0.00003411725,0.000015146216,0.001975866,0.9285198,0.03618976,0.00003322292,0.0006867046,0.0077603674],"study_design_scores_gemma":[0.0037648787,0.0005861479,0.9057254,0.00013508178,0.000085865184,0.000005804282,0.00064000045,0.0409921,0.017622499,0.00028033493,0.029815739,0.0003461028],"about_ca_topic_score_codex":2.8921576e-7,"about_ca_topic_score_gemma":0.0000128974325,"teacher_disagreement_score":0.8875277,"about_ca_system_score_codex":0.000075146185,"about_ca_system_score_gemma":0.00001210616,"threshold_uncertainty_score":0.426148},"labels":[],"label_agreement":null},{"id":"W3096677197","doi":"10.22215/etd/2020-13927","title":"Connected and Autonomous Vehicles for Intelligent Transportation Systems","year":2020,"lang":"en","type":"dissertation","venue":"","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":"Carleton University","funders":"","keywords":"Testbed; Redundancy (engineering); Computer science; Intelligent transportation system; Economic shortage; Real-time computing; Software; Floating car data; Traffic sign recognition; Embedded system; Engineering; Transport engineering; Traffic congestion; Traffic sign; Sign (mathematics); Computer network","score_opus":0.009866093455333859,"score_gpt":0.21415723027448902,"score_spread":0.20429113681915517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096677197","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":[0.8953776,0.011336798,0.06671898,0.0002542165,0.0029521836,0.0036008651,0.0004043338,0.007788757,0.011566272],"genre_scores_gemma":[0.9956336,0.00031694514,0.00052047876,0.000015984277,0.000049232076,0.00020072397,0.0020903011,0.00006763888,0.0011050986],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992061,0.000005917043,0.0003373737,0.00022960917,0.00005475296,0.0001662199],"domain_scores_gemma":[0.99968916,0.000055719196,0.000053777847,0.000103082566,0.00004395574,0.000054324424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000044959565,0.00022647057,0.00032343136,0.00008271499,0.00005906873,0.000022480794,0.00009357715,0.00049968745,0.000017030803],"category_scores_gemma":[0.000009815621,0.00023600199,0.000058998074,0.00007332046,0.000017070177,0.000046104175,0.0000014660394,0.00022514288,0.000013045536],"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":[0.0009901549,0.00016961883,0.00097085355,0.023269216,0.0032943515,0.00007954047,0.021267617,0.035152793,0.032119367,0.49330437,0.013159477,0.37622264],"study_design_scores_gemma":[0.001987138,0.00057373533,0.013190727,0.00053948705,0.0008610675,0.0000138983605,0.012563794,0.73388654,0.092422254,0.0035093639,0.13763162,0.0028203903],"about_ca_topic_score_codex":0.000020392037,"about_ca_topic_score_gemma":0.0001911159,"teacher_disagreement_score":0.69873375,"about_ca_system_score_codex":0.00004177404,"about_ca_system_score_gemma":0.000028527533,"threshold_uncertainty_score":0.9623882},"labels":[],"label_agreement":null},{"id":"W3104639963","doi":"10.1007/s42154-020-00113-1","title":"Deep Reinforcement Learning Enabled Decision-Making for Autonomous Driving at Intersections","year":2020,"lang":"en","type":"article","venue":"Automotive Innovation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":112,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Intersection (aeronautics); Markov decision process; Process (computing); Focus (optics); Artificial intelligence; Generalization; Convolutional neural network; Crash; Path (computing); Deep learning; Machine learning; Markov process; Transport engineering; Engineering; Mathematics","score_opus":0.01179573186215257,"score_gpt":0.24035606247401933,"score_spread":0.22856033061186676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104639963","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":[0.25227278,0.000019826626,0.7431313,0.00032304833,0.00022815357,0.00031334977,8.903955e-7,0.0018748911,0.0018357708],"genre_scores_gemma":[0.99321747,0.000005515919,0.0061302115,0.00029674312,0.000092375776,0.00008915491,0.00003905184,0.000043284817,0.00008617998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988736,0.000013830903,0.00046794963,0.00025327117,0.00010167732,0.00028967272],"domain_scores_gemma":[0.9993138,0.00019777699,0.00011829709,0.00013200588,0.00020747894,0.000030663385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019582734,0.00017953964,0.00019361466,0.00023405746,0.00033387356,0.000026314134,0.00014560812,0.00016792124,0.00014420868],"category_scores_gemma":[0.00058493187,0.00020311301,0.00005695332,0.0008502669,0.00003860046,0.00023341739,0.000095287076,0.00032979288,0.00007194983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003972974,0.000011145795,0.0025100198,0.00004319846,0.000110272114,0.000003817044,0.0020936904,0.9206206,0.0051137293,0.0070290742,0.00074838114,0.061676316],"study_design_scores_gemma":[0.00039155816,0.00013760451,0.0029553454,0.000050465453,0.0000171922,0.000005277557,0.00030908405,0.9845968,0.005233247,0.0010733962,0.005005274,0.00022475321],"about_ca_topic_score_codex":0.0000019959623,"about_ca_topic_score_gemma":0.000016809918,"teacher_disagreement_score":0.7409447,"about_ca_system_score_codex":0.00042178945,"about_ca_system_score_gemma":0.000023753022,"threshold_uncertainty_score":0.82827085},"labels":[],"label_agreement":null},{"id":"W3105161010","doi":"10.1049/iet-est.2020.0044","title":"Heuristics‐oriented overtaking decision making for autonomous vehicles using reinforcement learning","year":2020,"lang":"en","type":"article","venue":"IET Electrical Systems in Transportation","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":"","keywords":"Overtaking; Reinforcement learning; Heuristic; Heuristics; Computer science; Convergence (economics); Control (management); Artificial intelligence; Engineering; Transport engineering","score_opus":0.014525275903687622,"score_gpt":0.24392619226377005,"score_spread":0.22940091636008242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105161010","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":[0.39723015,0.00038772592,0.60135806,0.0000153643,0.00014416905,0.00039480074,0.0000041721223,0.0004082547,0.000057282505],"genre_scores_gemma":[0.99687797,0.000021875698,0.002850396,0.00003852708,0.00007622363,0.00005463582,0.00003450387,0.00004254335,0.000003322836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850917,0.000024135727,0.0006566661,0.00026534693,0.00017508099,0.0003695981],"domain_scores_gemma":[0.99953157,0.00017645676,0.00010333435,0.00008981289,0.00004488651,0.00005394895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018618158,0.00018445705,0.00030251185,0.00013094931,0.00011491765,0.000022289383,0.000115737,0.00023681002,0.000004248008],"category_scores_gemma":[0.00007921331,0.00020604617,0.000072654606,0.00048288473,0.000016708787,0.00012182948,0.0000031294371,0.00037164742,0.0000040307827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106937994,0.000011522494,0.0068372292,0.00014741844,0.000026556456,0.000015374126,0.00057237176,0.9713697,0.0040101847,0.0033833699,0.000018067063,0.013501258],"study_design_scores_gemma":[0.00061584084,0.00012255291,0.004723788,0.00012965106,0.000027799397,0.0000026438001,0.00007360868,0.99155533,0.00087250775,0.00015815019,0.0014918442,0.00022626351],"about_ca_topic_score_codex":0.000036795427,"about_ca_topic_score_gemma":0.000016392432,"teacher_disagreement_score":0.5996478,"about_ca_system_score_codex":0.0002547975,"about_ca_system_score_gemma":0.000030506715,"threshold_uncertainty_score":0.8402319},"labels":[],"label_agreement":null},{"id":"W3105436810","doi":"10.48550/arxiv.2011.03635","title":"Motion Prediction on Self-driving Cars: A Review","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Motion (physics); Computer science; Task (project management); Artificial intelligence; State (computer science); Self driving; Machine learning; Engineering; Transport engineering; Systems engineering; Algorithm","score_opus":0.03882443615128048,"score_gpt":0.16018279007045558,"score_spread":0.1213583539191751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105436810","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":[0.6103692,0.0051269205,0.32027957,0.000999417,0.0030489808,0.0025315373,0.00018745066,0.021818131,0.035638772],"genre_scores_gemma":[0.9876177,0.011884554,0.00012921798,0.000105943785,0.00007691339,0.0000023557732,0.00004971366,0.000036236837,0.000097415446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989721,0.00004996797,0.00018849397,0.0005275286,0.00004872108,0.00021315127],"domain_scores_gemma":[0.99924207,0.000024837545,0.00008217489,0.0005292519,0.000035944577,0.000085740714],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011222544,0.00026879107,0.00033152723,0.00012382209,0.00007533935,0.0000090921785,0.00035974762,0.00045329623,0.000039951075],"category_scores_gemma":[0.00002241058,0.0003390669,0.00016118883,0.00028376878,0.000034752833,0.00009324323,0.00023804554,0.0010143192,0.00016146689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021574326,0.000120099445,0.005444275,0.004224218,0.00066752435,0.00041331752,0.00023690377,0.945642,0.00011062988,0.03091214,0.005624124,0.006583194],"study_design_scores_gemma":[0.00036249284,0.000082535465,0.0048247552,0.0022675938,0.00046115022,0.000007812492,0.000031774656,0.9781022,0.0002269477,0.006719721,0.006351907,0.0005610848],"about_ca_topic_score_codex":0.000009063687,"about_ca_topic_score_gemma":0.0000073510064,"teacher_disagreement_score":0.37724844,"about_ca_system_score_codex":0.000372243,"about_ca_system_score_gemma":0.00003459075,"threshold_uncertainty_score":0.9999061},"labels":[],"label_agreement":null},{"id":"W3106149304","doi":"10.3390/s20226532","title":"The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review","year":2020,"lang":"en","type":"review","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":152,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visibility; Cruise control; Perception; Computer science; Radar; Transport engineering; Environmental science; Engineering; Meteorology; Control (management); Artificial intelligence; Geography; Telecommunications","score_opus":0.022801953109040467,"score_gpt":0.2779844097190068,"score_spread":0.2551824566099663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106149304","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":[0.000030853866,0.99734765,0.000012838106,0.00005084905,0.00014837622,0.0018072869,0.00004633946,0.00028807123,0.00026770998],"genre_scores_gemma":[0.00064009015,0.99884665,0.000022631577,0.000016797265,0.000036936177,0.00025240114,0.000053003845,0.000055434335,0.00007606478],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9980229,0.000331863,0.0010955281,0.0002136269,0.00013023488,0.00020583377],"domain_scores_gemma":[0.9989675,0.00025272998,0.000247468,0.00046160904,0.000034330904,0.00003632193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000449708,0.00033843378,0.0017105556,0.00012587212,0.00004619961,0.000021615062,0.00032843195,0.0004130505,0.0000071284885],"category_scores_gemma":[0.00006510273,0.00021621127,0.0003626913,0.00046081506,0.00006704523,0.000034074674,0.000035490237,0.00066325895,0.00016020807],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.5505136e-7,0.000006335866,1.7323919e-7,0.93870926,0.000216413,0.000041042756,0.00026186358,0.000035466986,5.512486e-7,0.0014757782,0.00014167272,0.059110716],"study_design_scores_gemma":[0.000033932323,0.000014163947,0.0000014851644,0.718976,0.0005696585,0.00019183046,0.00031273763,0.0010568717,4.347658e-7,0.000021856658,0.27861542,0.00020559005],"about_ca_topic_score_codex":0.000002052541,"about_ca_topic_score_gemma":0.0000053431477,"teacher_disagreement_score":0.27847376,"about_ca_system_score_codex":0.00030416567,"about_ca_system_score_gemma":0.000027215709,"threshold_uncertainty_score":0.88168395},"labels":[],"label_agreement":null},{"id":"W3108004145","doi":"10.1109/icra48506.2021.9561910","title":"Multimodal dynamics modeling for off-road autonomous vehicles","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Hydro-Québec","keywords":"Computer science; Leverage (statistics); Robot; Artificial intelligence; Modalities; Modality (human–computer interaction); Lidar; Robotics; Dynamics (music); Computer vision; Simulation; Human–computer interaction; Machine learning","score_opus":0.013665702320058289,"score_gpt":0.2306386963409902,"score_spread":0.21697299402093193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108004145","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":[0.4526469,0.0015193366,0.53752375,0.0002200323,0.0008491321,0.00055713323,0.00011684954,0.0031969422,0.0033699211],"genre_scores_gemma":[0.9591142,0.00037375707,0.03919632,0.00004925036,0.00010778914,0.00021878268,0.0004266602,0.000119757904,0.00039347127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832356,0.000014756761,0.00051639025,0.0005490358,0.00010541519,0.0004908472],"domain_scores_gemma":[0.9990089,0.000047989663,0.00005412543,0.00070614455,0.00010438742,0.00007845476],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017941896,0.00044166847,0.0005717351,0.00014286903,0.000113988404,0.000070917835,0.0004829387,0.0012856036,0.000052253286],"category_scores_gemma":[0.000029039125,0.0004976535,0.00029584407,0.00007362291,0.000051016425,0.00008097083,0.0004649161,0.0010561734,0.000015902378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069749303,0.000026768927,0.00004649439,0.00023613364,0.00017225696,0.0000082622555,0.000112928625,0.7954951,0.00008786832,0.0014874412,0.00005587561,0.2022639],"study_design_scores_gemma":[0.0002992602,0.000012870437,0.00010724111,0.00006406099,0.000058885915,0.000007749308,0.00020163738,0.99650073,0.0005152077,0.001485163,0.00019607796,0.0005511252],"about_ca_topic_score_codex":0.0000968228,"about_ca_topic_score_gemma":0.00026938133,"teacher_disagreement_score":0.5064673,"about_ca_system_score_codex":0.000410047,"about_ca_system_score_gemma":0.00015521419,"threshold_uncertainty_score":0.9997475},"labels":[],"label_agreement":null},{"id":"W3109059966","doi":"10.1109/ccece47787.2020.9255732","title":"Training a Neural Network for Lane Demarcation Detection in the Infrared Spectrum","year":2020,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"National Research Council Canada","funders":"Government of Canada; Transport Canada","keywords":"Artificial neural network; Computer science; Infrared; Training (meteorology); Spectrum (functional analysis); Artificial intelligence; Optics; Physics","score_opus":0.02282513252865214,"score_gpt":0.2133464078860195,"score_spread":0.19052127535736735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109059966","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":[0.5928949,0.00014853314,0.38807672,0.00716217,0.00020814124,0.0006824258,0.0000032550297,0.0014722359,0.009351588],"genre_scores_gemma":[0.9983173,0.0000042773067,0.0010572304,0.00043584188,0.00012112239,0.000039662707,0.000004818802,0.000008293815,0.000011463081],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965125,0.00001145068,0.000101375605,0.00006835063,0.000026086776,0.00014149323],"domain_scores_gemma":[0.9998567,0.00005569847,0.000010510487,0.0000618894,0.000002634624,0.00001253453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010802461,0.000052805874,0.000065402564,0.000016682903,0.000042567484,0.000007075796,0.00008380678,0.0000652235,0.000012554739],"category_scores_gemma":[0.000019060106,0.000042182393,0.00002100657,0.00016404346,0.000009659601,0.00004489773,0.000005670504,0.0001394154,0.000005539441],"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":[0.00011637324,0.000017517297,0.0030385833,0.0000838196,0.000053369044,0.000008711303,0.009367957,0.4786493,0.0026541331,0.019668141,0.0019697794,0.48437232],"study_design_scores_gemma":[0.00024539087,0.00006625892,0.0114550525,0.000002466094,0.0000054154343,0.000005683147,0.00040061894,0.9764533,0.00092931,0.005833526,0.0045185005,0.00008447733],"about_ca_topic_score_codex":0.000003662435,"about_ca_topic_score_gemma":0.00014015455,"teacher_disagreement_score":0.49780402,"about_ca_system_score_codex":0.000014282718,"about_ca_system_score_gemma":0.0000038722487,"threshold_uncertainty_score":0.1720148},"labels":[],"label_agreement":null},{"id":"W3110317294","doi":"10.1109/iv51971.2022.9827055","title":"Multi-Modal Hybrid Architecture for Pedestrian Action Prediction","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Intelligent Vehicles Symposium (IV)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Huawei Technologies (Canada)","funders":"","keywords":"Pedestrian; Computer science; Modal; Artificial intelligence; Pedestrian detection; Feed forward; Architecture; Range (aeronautics); Machine learning; Pedestrian crossing; Engineering; Transport engineering; Control engineering","score_opus":0.020336593692258525,"score_gpt":0.24099926785322648,"score_spread":0.22066267416096796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110317294","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":[0.7855868,0.00049927895,0.20407118,0.0009086808,0.0038929505,0.0014059291,0.0007608587,0.002474642,0.00039967778],"genre_scores_gemma":[0.9962543,0.00026504364,0.001109489,0.0001206344,0.000325982,0.0007865934,0.00020482819,0.000109427056,0.00082365976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805576,0.00008707658,0.00051286485,0.0004928354,0.0002919468,0.00055950886],"domain_scores_gemma":[0.9991832,0.00011185211,0.00009810333,0.00045161325,0.000041228184,0.00011401462],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038339253,0.0003385712,0.00030502875,0.0002972602,0.00063649716,0.00003657398,0.00046623894,0.00016963576,0.0002108002],"category_scores_gemma":[0.000015275582,0.00038960337,0.00024538132,0.00026998066,0.000082437684,0.00012999921,0.00011337388,0.0009426784,0.000048509955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037658054,0.0002771997,0.0010838865,0.00018102872,0.00025890407,0.000025298667,0.00088867417,0.63015664,0.31679946,0.00020944171,0.0062655467,0.043477345],"study_design_scores_gemma":[0.0011087388,0.0006303445,0.0005383128,0.00001831534,0.00011970295,0.00020835604,0.00068301504,0.63718486,0.21546955,0.0009596029,0.1424157,0.0006635007],"about_ca_topic_score_codex":0.00003370324,"about_ca_topic_score_gemma":0.00005120492,"teacher_disagreement_score":0.21066754,"about_ca_system_score_codex":0.0005449532,"about_ca_system_score_gemma":0.00005296065,"threshold_uncertainty_score":0.9998556},"labels":[],"label_agreement":null},{"id":"W3110717661","doi":"10.21203/rs.3.rs-122422/v1","title":"Lane-Exchanging Driving Strategy for Autonomous Vehicle via Trajectory Prediction and Model Predictive Control","year":2020,"lang":"en","type":"preprint","venue":"Research Square","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"State Key Laboratory of Automotive Safety and Energy","keywords":"Trajectory; CarSim; Model predictive control; Kinematics; Computer science; Vehicle dynamics; Collision avoidance; Control theory (sociology); Field (mathematics); Simulation; Control (management); Collision; Engineering; Automotive engineering; Artificial intelligence; Mathematics","score_opus":0.03658462155171072,"score_gpt":0.3043904993533197,"score_spread":0.26780587780160897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110717661","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":[0.15231587,0.0022202851,0.8355242,0.000569458,0.00025499274,0.0033796956,0.0013389831,0.0028555398,0.0015409449],"genre_scores_gemma":[0.99737793,0.00027446705,0.0008292233,0.00001377207,0.0002863188,0.00088736165,0.0001534508,0.000112482616,0.000065007196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976681,0.000119180106,0.00037983482,0.0006797208,0.000357666,0.0007955365],"domain_scores_gemma":[0.99880224,0.00031783455,0.000056615612,0.00041912953,0.0001882996,0.00021587336],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008420747,0.00034902833,0.0004877993,0.00036490808,0.00032792636,0.00008174877,0.00034940732,0.0008606221,0.000013683403],"category_scores_gemma":[0.0001051358,0.0003900369,0.0001331719,0.00017435898,0.00018715208,0.0001379517,0.00030690464,0.0022820078,0.00000931178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012546437,0.000042046315,0.0013316366,0.001734643,0.00031136116,0.00002089359,0.001062941,0.9756441,0.0036328156,0.0009660395,0.0005661983,0.014561858],"study_design_scores_gemma":[0.00075976877,0.00027700898,0.0049056085,0.000205414,0.00004486035,0.000004966778,0.000262208,0.98373085,0.0005630739,0.008828141,0.0001375771,0.00028050697],"about_ca_topic_score_codex":0.000022748049,"about_ca_topic_score_gemma":0.000028675686,"teacher_disagreement_score":0.845062,"about_ca_system_score_codex":0.0004461147,"about_ca_system_score_gemma":0.00023982416,"threshold_uncertainty_score":0.99985516},"labels":[],"label_agreement":null},{"id":"W3111013195","doi":"10.18280/ijsse.100507","title":"Road Safety: An Accident Prevention Using Intelligent Vehicular Network","year":2020,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Intersection (aeronautics); Collision; Transport engineering; Mechanism (biology); Position (finance); Computer science; Intelligent transportation system; Point (geometry); Simulation; Engineering; Computer security; Business","score_opus":0.012020568302325296,"score_gpt":0.23510147808211426,"score_spread":0.22308090977978898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111013195","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":[0.6219352,0.0021264898,0.37368053,0.0007533057,0.0010841462,0.00010963346,0.000010777291,0.00019664601,0.000103264596],"genre_scores_gemma":[0.9948128,0.0014351567,0.0029597743,0.00008555888,0.0006696744,6.467278e-7,0.000009464682,0.000025153759,0.000001774873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988176,0.000022731565,0.00059625244,0.00012529318,0.0002447282,0.00019338191],"domain_scores_gemma":[0.99949825,0.000029200257,0.00011898948,0.00008083618,0.000115447125,0.00015726958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032237807,0.00016713611,0.00026293375,0.000099090816,0.000052347106,0.00003911627,0.00031243346,0.0001399467,0.000040768216],"category_scores_gemma":[0.000044615623,0.00017706923,0.00010794859,0.000116056886,0.000024475345,0.00043806245,0.000074484575,0.00048705353,0.0000028431025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014887341,0.000021726448,0.0006735978,0.000023160825,0.0002971919,0.00008641223,0.0008293133,0.9743649,0.0011210885,0.0019759242,0.000016347885,0.020441476],"study_design_scores_gemma":[0.0005751675,0.00012985457,0.004267715,0.00015776597,0.000049908038,0.00031142303,0.00014468035,0.9821223,0.0013633876,0.0007003014,0.009928029,0.00024944954],"about_ca_topic_score_codex":0.0000034280679,"about_ca_topic_score_gemma":0.0000037303303,"teacher_disagreement_score":0.3728776,"about_ca_system_score_codex":0.00013243251,"about_ca_system_score_gemma":0.000027300204,"threshold_uncertainty_score":0.72206736},"labels":[],"label_agreement":null},{"id":"W3112624553","doi":"10.1155/2020/5172305","title":"An Automatic Emergency Braking Model considering Driver’s Intention Recognition of the Front Vehicle","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Chang'an University; National Natural Science Foundation of China","keywords":"Brake; Hidden Markov model; Collision; Front (military); Computer science; Automotive engineering; Backpropagation; Artificial neural network; Simulation; Engineering; Artificial intelligence; Computer security","score_opus":0.01595365165132221,"score_gpt":0.22931370522236583,"score_spread":0.21336005357104362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112624553","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":[0.8758357,0.00008648256,0.12358469,0.0001362087,0.00017415121,0.000075306154,0.000006737408,0.00007345302,0.000027264454],"genre_scores_gemma":[0.9897461,0.00009861392,0.010092612,0.000021673708,0.000019540039,0.0000017212031,0.000005199642,0.000013546672,9.900871e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921215,0.000014140359,0.0005118177,0.000062658415,0.00011719679,0.00008203327],"domain_scores_gemma":[0.99952674,0.000009096469,0.00025778267,0.00007095302,0.00009890865,0.00003654035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007812245,0.00007691327,0.00015092305,0.000044515353,0.00004034342,0.000002478972,0.0001035233,0.000062513645,0.000028322345],"category_scores_gemma":[0.000016281629,0.00006692185,0.00009030652,0.000099180215,0.000021508791,0.00045385063,0.0000014008837,0.00020786263,0.0000012884253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024908373,0.00001780134,0.0020172705,0.00007882242,0.00003138469,0.000002516779,0.0026490341,0.79029495,0.17595541,0.00006751578,0.0000067157252,0.028853668],"study_design_scores_gemma":[0.0007947108,0.00015383797,0.1392782,0.00016949048,0.000113263326,0.0000064687797,0.0011078547,0.78633916,0.066146836,0.0057217353,0.000017091927,0.00015132478],"about_ca_topic_score_codex":0.0000010533331,"about_ca_topic_score_gemma":0.000022481943,"teacher_disagreement_score":0.13726093,"about_ca_system_score_codex":0.000028688135,"about_ca_system_score_gemma":0.000018002464,"threshold_uncertainty_score":0.2728994},"labels":[],"label_agreement":null},{"id":"W3115696625","doi":"10.1109/itsc45102.2020.9294679","title":"Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization","year":2020,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Generalization; Artificial intelligence; Motion planning; Machine learning; Motion (physics); Mathematical optimization; Core (optical fiber); Supervised learning; Integer (computer science); Integer programming; Artificial neural network; Algorithm; Mathematics; Robot","score_opus":0.020316560168818658,"score_gpt":0.23288976911064566,"score_spread":0.212573208941827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115696625","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":[0.062776424,0.00003384639,0.9323457,0.0006822402,0.000058604197,0.00024988802,0.0000032011899,0.003242646,0.00060743396],"genre_scores_gemma":[0.9897814,0.0000024781282,0.009926191,0.00013991556,0.000026600443,0.000036337035,0.00006129688,0.000018519546,0.000007278176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961025,0.000012521093,0.00010036574,0.000121503894,0.00004386961,0.00011150514],"domain_scores_gemma":[0.99983615,0.000057928857,0.000020866957,0.000035636036,0.00001830325,0.000031144973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007532631,0.00008323796,0.00008473913,0.00006270793,0.00007019274,0.0000104503515,0.000026293921,0.00009925441,0.000014752439],"category_scores_gemma":[0.000046302845,0.00008212885,0.000015808695,0.000092971386,0.000010569463,0.000028264907,0.0000064136407,0.00010483995,0.00000624589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018649229,0.000006090697,0.00031394407,0.000036578713,0.0000055697233,7.458756e-7,0.00024989806,0.9947415,0.00020097564,0.00019363445,0.000063970205,0.0041684257],"study_design_scores_gemma":[0.000347532,0.00010091492,0.0010168212,0.00001721566,0.000007732158,5.2452054e-7,0.000114131784,0.99720365,0.00093700585,0.0000035217429,0.00016081,0.00009013561],"about_ca_topic_score_codex":0.0000011115313,"about_ca_topic_score_gemma":3.5242388e-7,"teacher_disagreement_score":0.92700493,"about_ca_system_score_codex":0.00002454218,"about_ca_system_score_gemma":0.0000025470765,"threshold_uncertainty_score":0.33491173},"labels":[],"label_agreement":null},{"id":"W3115871817","doi":"10.5772/intechopen.95328","title":"Visibility-Based Technologies and Methodologies for Autonomous Driving","year":2021,"lang":"en","type":"book-chapter","venue":"Artificial intelligence","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visibility; Obstacle; Computer science; Sight; Lidar; Computer vision; Segmentation; Identification (biology); Artificial intelligence; Orientation (vector space); Geography; Remote sensing","score_opus":0.07498361055953667,"score_gpt":0.29998016043353093,"score_spread":0.22499654987399426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115871817","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":[0.0010290998,0.0050753444,0.96365404,0.0007935018,0.00064123183,0.00076144614,0.00006613247,0.0052649197,0.022714311],"genre_scores_gemma":[0.716614,0.0021658265,0.26109797,0.00016462937,0.00023296615,0.00032699655,0.00011218572,0.00036564533,0.018919745],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984536,0.000014760865,0.0005398121,0.00055045413,0.00008966323,0.00035171784],"domain_scores_gemma":[0.9983701,0.0008310131,0.000093438466,0.0005802576,0.0000905998,0.00003456921],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041551053,0.00039126098,0.0005455291,0.00020570758,0.00015698136,0.000051444942,0.00034260508,0.0009927145,0.00007213122],"category_scores_gemma":[0.00037280432,0.00041821587,0.0001551788,0.0000600469,0.0005264331,0.000064437605,0.00014356711,0.00061684404,0.000031712858],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008518537,0.0000055212076,0.000005857903,0.00011138865,0.000059506197,0.00001246547,0.00003784851,0.0023803916,0.00067770807,0.41882312,0.000045553264,0.5778321],"study_design_scores_gemma":[0.000024036757,0.00011501166,0.000008249236,0.00016296173,0.00008549408,0.000010457239,0.00036558678,0.04701543,0.14278257,0.78360534,0.025109304,0.00071553874],"about_ca_topic_score_codex":0.0000029476475,"about_ca_topic_score_gemma":0.00009292308,"teacher_disagreement_score":0.71558493,"about_ca_system_score_codex":0.0001046406,"about_ca_system_score_gemma":0.00007049058,"threshold_uncertainty_score":0.99982697},"labels":[],"label_agreement":null},{"id":"W3116005188","doi":"10.1109/itsc45102.2020.9294754","title":"Autonomous Driving at Intersections: A Critical-Turning-Point Approach for Left Turns","year":2020,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Partially observable Markov decision process; Planner; Computer science; Motion planning; Point (geometry); Process (computing); Generator (circuit theory); Markov decision process; Self driving; Path (computing); Advanced driver assistance systems; Markov process; Markov chain; Artificial intelligence; Transport engineering; Markov model; Engineering; Machine learning; Robot; Mathematics","score_opus":0.014682716421350136,"score_gpt":0.22660179956073861,"score_spread":0.21191908313938848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116005188","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":[0.028315093,0.00018034154,0.94063246,0.0024294073,0.00021221356,0.00033670873,0.000009803161,0.0035965007,0.02428747],"genre_scores_gemma":[0.9714537,0.000010648074,0.027640657,0.0003893457,0.000098673605,0.00006090987,0.000012075515,0.000044219265,0.0002897566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999069,0.000011088667,0.00025473203,0.00027167983,0.000054861634,0.0003386539],"domain_scores_gemma":[0.99952024,0.0001310905,0.00001681643,0.00018322107,0.000028115264,0.0001205133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009449899,0.00017509634,0.00021610482,0.000059006972,0.00013056347,0.000022703973,0.00018437435,0.00019433144,0.00029859872],"category_scores_gemma":[0.00018452879,0.00017327677,0.0001163114,0.0000932359,0.00008393097,0.00012518853,0.00009826483,0.00030136353,0.000072704745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041901608,0.0008248588,0.021774035,0.005169331,0.0022569774,0.00011702104,0.04957958,0.35627598,0.1459572,0.24380304,0.120586984,0.053235993],"study_design_scores_gemma":[0.00033843078,0.00011598078,0.0003468666,0.00001120353,0.000034549514,0.000053411008,0.0006946396,0.97432274,0.008021697,0.0007976892,0.014923065,0.00033974508],"about_ca_topic_score_codex":0.00000339136,"about_ca_topic_score_gemma":0.000022989243,"teacher_disagreement_score":0.9431386,"about_ca_system_score_codex":0.00013052902,"about_ca_system_score_gemma":0.000013151036,"threshold_uncertainty_score":0.70660216},"labels":[],"label_agreement":null},{"id":"W3116263883","doi":"10.1109/iccv48922.2021.01533","title":"Prediction by Anticipation: An Action-Conditional Prediction Method based on Interaction Learning","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Autonomous Vehicle Technology and Safety","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; Huawei Technologies (Canada)","funders":"","keywords":"Anticipation (artificial intelligence); Generalization; Probabilistic logic; Computer science; Generative model; Generative grammar; Artificial intelligence; Machine learning; Action (physics); Bayesian probability; Range (aeronautics); Conditional probability distribution; Process (computing); Pedestrian; Conditional probability; Mathematics; Engineering; Econometrics; Statistics","score_opus":0.042045737154585455,"score_gpt":0.3237950629773097,"score_spread":0.2817493258227243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116263883","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":[0.0835597,0.000011398451,0.90214545,0.0016561355,0.0050573996,0.00020213496,0.00025913576,0.0008720925,0.00623656],"genre_scores_gemma":[0.9897324,0.0000729185,0.0057761623,0.00037106164,0.00075066835,0.00005849359,0.002604699,0.00004309191,0.0005905495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774724,0.00023641637,0.00052838237,0.0006432302,0.00058401236,0.00026069063],"domain_scores_gemma":[0.99878186,0.00018682866,0.00014596165,0.0003260374,0.00043910553,0.0001202123],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031887004,0.0003159226,0.000255465,0.0003448039,0.00026423228,0.00019021881,0.0002685453,0.00032381288,0.0025497898],"category_scores_gemma":[0.000036823654,0.00035324233,0.0001252056,0.00025544464,0.000042795444,0.0006759564,0.000041866766,0.0009075514,0.00023030133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022092026,0.000571351,0.0008223571,0.000029458983,0.00019046852,0.00004276835,0.0001436154,0.8192616,0.0267464,0.006090367,0.013153118,0.13272758],"study_design_scores_gemma":[0.0007096614,0.0005204592,0.004360583,0.00014913401,0.000021248341,0.000039415565,0.0000826057,0.96509147,0.017621433,0.0005503405,0.010593181,0.00026045874],"about_ca_topic_score_codex":0.000009258994,"about_ca_topic_score_gemma":0.000011502911,"teacher_disagreement_score":0.90617263,"about_ca_system_score_codex":0.00041814984,"about_ca_system_score_gemma":0.00009207642,"threshold_uncertainty_score":0.99989194},"labels":[],"label_agreement":null},{"id":"W3118501642","doi":"10.36959/673/370","title":"Real Application for a Low-Cost Low-Power Self-Driving 1/10 Scale Car","year":2020,"lang":"en","type":"article","venue":"Journal of Robotics and Automation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Université du Québec à Chicoutimi","keywords":"Usability; Self driving; Computer science; Scale (ratio); Power (physics); Embedded system; Simulation; Real-time computing; Automotive engineering; Human–computer interaction; Engineering","score_opus":0.0050292656777176465,"score_gpt":0.2073342216909152,"score_spread":0.20230495601319756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118501642","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":[0.53847206,0.000058780653,0.4597287,0.0010364862,0.00009556905,0.00019825972,0.0000032056191,0.00023877886,0.0001681906],"genre_scores_gemma":[0.98313904,0.00009952946,0.016613977,0.00003239227,0.000087136475,0.000004695874,0.0000035544358,0.000014377007,0.0000052952064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994773,0.000006633962,0.00027959983,0.0000650706,0.000071434115,0.00009991302],"domain_scores_gemma":[0.9996544,0.000030042582,0.00012598984,0.00005847506,0.00007177114,0.00005930878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012412686,0.000075817574,0.00014875001,0.000048185477,0.00005834271,0.000018411321,0.000070756614,0.00010365261,0.0000044138324],"category_scores_gemma":[0.000016735803,0.00007290871,0.000045392822,0.000079126025,0.00001346072,0.0001403309,0.000011622892,0.00012612692,0.000004872772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005898765,0.00012166082,0.0043490985,0.00059258903,0.00021393357,0.000008492595,0.0031718477,0.8607735,0.058374,0.0044736513,0.0022649646,0.06559728],"study_design_scores_gemma":[0.0004114583,0.00007477835,0.0074502276,0.000035046807,0.000036537516,0.000014870764,0.00007345877,0.98773587,0.0029363323,0.00024497302,0.00088868086,0.00009774002],"about_ca_topic_score_codex":3.2863014e-7,"about_ca_topic_score_gemma":9.920581e-7,"teacher_disagreement_score":0.444667,"about_ca_system_score_codex":0.000042914704,"about_ca_system_score_gemma":0.000019649675,"threshold_uncertainty_score":0.2973131},"labels":[],"label_agreement":null},{"id":"W3118743384","doi":"10.1109/iv47402.2020.9304552","title":"Identifying the Operational Design Domain for an Automated Driving System through Assessed Risk","year":2020,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"Huawei Technologies","keywords":"Automotive industry; Domain (mathematical analysis); Set (abstract data type); Computer science; Risk analysis (engineering); Transport engineering; Engineering; Mathematics; Business","score_opus":0.03980776729237362,"score_gpt":0.2740720499105536,"score_spread":0.23426428261818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118743384","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":[0.06789076,0.000050100214,0.9245608,0.00037324737,0.00010798669,0.00043123876,0.00000819737,0.005633292,0.0009443762],"genre_scores_gemma":[0.9113736,0.000005977204,0.08830254,0.00011433792,0.00006854434,0.00008613108,0.000010579515,0.000027910408,0.000010344975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992507,0.00007152539,0.00022390191,0.00017417171,0.00008216166,0.0001975214],"domain_scores_gemma":[0.9995629,0.00016295354,0.00003277467,0.00017450131,0.00002973205,0.000037122416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032893362,0.00012435723,0.00014286116,0.000016812968,0.0003502046,0.000069840484,0.00025081282,0.0001247005,0.000021469687],"category_scores_gemma":[0.00003359904,0.00009291958,0.000044233424,0.00013831265,0.000031361356,0.0002899661,0.000027880731,0.00016981333,0.000032928307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004204567,0.000045261524,0.0017246526,0.00028643705,0.00047326286,0.00001299351,0.0054795314,0.7141567,0.04898136,0.2161653,0.01036854,0.0022638936],"study_design_scores_gemma":[0.00027009347,0.00003939438,0.0010341732,0.000011141513,0.000021447686,0.0000062582108,0.001143613,0.9888196,0.0072670747,0.00069391256,0.0005582538,0.000135032],"about_ca_topic_score_codex":0.000009480037,"about_ca_topic_score_gemma":0.000010382663,"teacher_disagreement_score":0.84348285,"about_ca_system_score_codex":0.00005437984,"about_ca_system_score_gemma":0.000025309198,"threshold_uncertainty_score":0.37891504},"labels":[],"label_agreement":null},{"id":"W3119361198","doi":"10.1109/iv47402.2020.9304591","title":"Do They Want to Cross? Understanding Pedestrian Intention for Behavior Prediction","year":2020,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":87,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pedestrian; Anticipation (artificial intelligence); Computer science; Task (project management); Trajectory; Action (physics); Scale (ratio); Work (physics); Point (geometry); Estimation; Human behavior; Artificial intelligence; Machine learning; Human–computer interaction; Transport engineering; Engineering; Mathematics","score_opus":0.06394491759693305,"score_gpt":0.27034999972180856,"score_spread":0.20640508212487552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119361198","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":[0.38321355,0.000012724317,0.6114186,0.0008819307,0.00022571911,0.0004429815,0.000029975385,0.0013702796,0.0024042365],"genre_scores_gemma":[0.99852926,0.000006966658,0.0011893061,0.0000676847,0.000048207086,0.00007011058,0.000011290133,0.000018484852,0.000058674326],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950993,0.0000036218482,0.00015018051,0.00013635808,0.000046282228,0.00015363313],"domain_scores_gemma":[0.9998063,0.000014287885,0.000011641744,0.00008764987,0.000014896229,0.00006524789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007144092,0.000080529666,0.000085968626,0.000041125175,0.00008709985,0.00002917024,0.0000824691,0.00011078678,0.000045115703],"category_scores_gemma":[0.000022655373,0.000077359546,0.000046274727,0.00007283181,0.000014506494,0.000091612186,0.000021000598,0.000100281395,0.000032256423],"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":[0.00089637795,0.00028966836,0.20407465,0.00078904704,0.0005168317,0.000042532247,0.012211415,0.07812225,0.17118831,0.4020875,0.014580934,0.11520047],"study_design_scores_gemma":[0.009010298,0.0034231027,0.15636365,0.00019496253,0.00054843986,0.00006603521,0.016395982,0.61285293,0.11460429,0.05781638,0.026077896,0.0026460388],"about_ca_topic_score_codex":0.000004983733,"about_ca_topic_score_gemma":0.000020859447,"teacher_disagreement_score":0.61531574,"about_ca_system_score_codex":0.0001217956,"about_ca_system_score_gemma":0.00000672062,"threshold_uncertainty_score":0.3154631},"labels":[],"label_agreement":null},{"id":"W3119833346","doi":"10.3390/s21020461","title":"A Multilane Tracking Algorithm Using IPDA with Intensity Feature","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","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":"General Dynamics (Canada); McMaster University","funders":"","keywords":"Computer science; Artificial intelligence; False positive paradox; Frame (networking); Filter (signal processing); Feature (linguistics); Set (abstract data type); Pixel; Pattern recognition (psychology); Tracking (education); Probabilistic logic; Computer vision","score_opus":0.009998182942996715,"score_gpt":0.20527415104938307,"score_spread":0.19527596810638637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119833346","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":[0.99267983,0.00023119424,0.0055016777,0.000152823,0.00015227769,0.00005557507,0.000008482364,0.000693512,0.00052462216],"genre_scores_gemma":[0.96788114,0.000022881426,0.031746533,0.000055326716,0.00005239369,6.3333835e-7,0.000008105383,0.00003055728,0.00020242114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994232,0.00001196773,0.00008831565,0.00017057933,0.000072066745,0.00023384948],"domain_scores_gemma":[0.9996467,0.000017606182,0.00001719199,0.00021230464,0.0000652424,0.00004094099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004428131,0.00013303538,0.0001814188,0.000042211534,0.0000767383,0.000013590291,0.000059535098,0.00017755327,0.000021820904],"category_scores_gemma":[0.000012707172,0.00012354161,0.00003667156,0.00018882754,0.000051994364,0.000048377104,0.000022760782,0.00038723176,0.000016118369],"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":[0.00008042563,0.00011957479,0.008564581,0.00017038999,0.0007028059,0.0059809047,0.0027068676,0.2505017,0.048914317,0.00027648063,0.00069477817,0.68128717],"study_design_scores_gemma":[0.00053052127,0.000025855441,0.009093825,0.00007657188,0.000056245703,0.001293848,0.00085611094,0.85514313,0.12880136,0.00007958943,0.0036191742,0.00042376947],"about_ca_topic_score_codex":0.000008850102,"about_ca_topic_score_gemma":0.000035438537,"teacher_disagreement_score":0.68086344,"about_ca_system_score_codex":0.000056146902,"about_ca_system_score_gemma":0.000019254705,"threshold_uncertainty_score":0.50378805},"labels":[],"label_agreement":null},{"id":"W3120385278","doi":"10.1109/iv47402.2020.9304698","title":"A Unified Evaluation Framework for Autonomous Driving Vehicles","year":2020,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Reliability (semiconductor); Software deployment; Fidelity; Automation; Safety assurance; Scenario testing; Reliability engineering; Track (disk drive); Test (biology); Engineering; Software engineering; Telecommunications; Artificial intelligence; Variety (cybernetics)","score_opus":0.028388957355102815,"score_gpt":0.25863275466246916,"score_spread":0.23024379730736633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120385278","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":[0.1988699,0.00018122423,0.7890756,0.0030647994,0.00015606699,0.00051628443,0.0000041922553,0.0026156092,0.005516324],"genre_scores_gemma":[0.96216786,0.000008598836,0.03727772,0.00032319082,0.00008322562,0.00007776488,0.000007629514,0.000026047524,0.000027947463],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938846,0.000011776492,0.00016817961,0.00015705587,0.00008083673,0.00019370009],"domain_scores_gemma":[0.9996337,0.00011135874,0.000019260178,0.00014188621,0.000035350604,0.000058430793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017118924,0.000106202024,0.00013043702,0.000031559994,0.0000719403,0.000014601249,0.0001398596,0.00019834403,0.00014485174],"category_scores_gemma":[0.00014726492,0.00010899404,0.000049691836,0.00013042227,0.000024331424,0.0000755049,0.000022409764,0.00018050177,0.000066275046],"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":[0.000038970207,0.00003685062,0.0021651536,0.00014468211,0.00021054974,0.0000036808703,0.0018404574,0.100338854,0.011201196,0.45867884,0.0040234416,0.4213173],"study_design_scores_gemma":[0.000300823,0.000050064085,0.0017720622,0.000010037468,0.000030509265,0.0000010891316,0.00013061978,0.9518092,0.010115552,0.029982282,0.0056154565,0.0001822554],"about_ca_topic_score_codex":0.0000011035615,"about_ca_topic_score_gemma":0.0000033933654,"teacher_disagreement_score":0.8514704,"about_ca_system_score_codex":0.000053410015,"about_ca_system_score_gemma":0.000027652657,"threshold_uncertainty_score":0.4444648},"labels":[],"label_agreement":null},{"id":"W3121355958","doi":"10.4271/2021-01-0175","title":"Infrastructure-Based Sensor Data Capture Systems for Measurement of Operational Safety Assessment (OSA) Metrics","year":2021,"lang":"en","type":"article","venue":"SAE International Journal of Advances and Current Practices in Mobility","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":"Transport Canada","funders":"","keywords":"Intersection (aeronautics); Computer science; Intelligent transportation system; Real-time computing; Lidar; Ranging; Transport engineering; Simulation; Engineering; Telecommunications; Remote sensing","score_opus":0.04440900978509965,"score_gpt":0.36019004911927405,"score_spread":0.3157810393341744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121355958","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":[0.34597868,0.15764704,0.4807704,0.0025669362,0.010109833,0.00085614953,0.0015081584,0.000058724785,0.00050406373],"genre_scores_gemma":[0.98249584,0.0065141222,0.010787657,0.000014663565,0.00011251222,0.00000682913,0.00006030152,0.0000066319517,0.00000145373],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984148,0.00008557153,0.0006964002,0.00017989227,0.00051989796,0.00010341833],"domain_scores_gemma":[0.99781686,0.00036549647,0.00062960613,0.00021064936,0.0009400583,0.000037351892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015493339,0.0001118404,0.00025272346,0.00011743466,0.00003353652,0.00003957391,0.00037300203,0.00006984568,0.000016837172],"category_scores_gemma":[0.0010242165,0.00009867142,0.000049444963,0.00012637148,0.000057431982,0.00079334027,0.00007615857,0.0003754987,9.682596e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006082734,0.0009405939,0.10510878,0.0012322202,0.00067096984,0.000033415396,0.00014615334,0.68818146,0.0026688564,0.0036565277,0.00034013335,0.1964126],"study_design_scores_gemma":[0.0050990377,0.00025743805,0.15223329,0.0007453453,0.00023325623,0.00022621857,0.0017010621,0.46614382,0.0023704013,0.002595874,0.36789837,0.00049588113],"about_ca_topic_score_codex":0.0000057309767,"about_ca_topic_score_gemma":0.00005468152,"teacher_disagreement_score":0.63651717,"about_ca_system_score_codex":0.00024286217,"about_ca_system_score_gemma":0.00030578827,"threshold_uncertainty_score":0.4023704},"labels":[],"label_agreement":null},{"id":"W3121941838","doi":"10.48550/arxiv.2101.06679","title":"End-to-end Interpretable Neural Motion Planner","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Autonomous Vehicle Technology and Safety","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 Toronto","funders":"","keywords":"Planner; Computer science; SAFER; Position (finance); Set (abstract data type); Motion (physics); Artificial intelligence; Trajectory; Data set; Volume (thermodynamics); Raw data; Motion planning; Lidar; Artificial neural network; Machine learning; Robot; Geography","score_opus":0.028126409605832032,"score_gpt":0.15840260660460245,"score_spread":0.1302761969987704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121941838","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":[0.87362957,0.00010741044,0.11812125,0.000060150156,0.0009290596,0.00017363865,0.000035903122,0.0010102883,0.0059327357],"genre_scores_gemma":[0.99847364,0.00008087937,0.00019533126,0.000059699243,0.00005301198,0.0000015115371,0.00007807151,0.000039508224,0.0010183459],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882305,0.000042201238,0.00016783708,0.0005767095,0.000043128886,0.0003471052],"domain_scores_gemma":[0.99909997,0.000029515022,0.00004757299,0.0006588634,0.000047438574,0.00011665525],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009208676,0.00029284914,0.00032233974,0.00023424411,0.000081046746,0.000036347534,0.0005131622,0.00054368674,0.0003827018],"category_scores_gemma":[0.000014568771,0.00037618808,0.00015487247,0.00029584585,0.00006637365,0.00015970985,0.00064827944,0.00095415005,0.00013644266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020923744,0.000027635208,0.0026351742,0.00007597952,0.00014383999,0.00037373728,0.00021484605,0.98981297,0.0002608593,0.0034968327,0.0002718274,0.0026653786],"study_design_scores_gemma":[0.00029056842,0.000032065225,0.0066360165,0.000099535224,0.000118443466,0.000016066979,0.0002649886,0.9874568,0.0013910666,0.0018328691,0.0012214988,0.00064010127],"about_ca_topic_score_codex":0.00009070211,"about_ca_topic_score_gemma":0.00010018477,"teacher_disagreement_score":0.124844074,"about_ca_system_score_codex":0.00027119936,"about_ca_system_score_gemma":0.00003841009,"threshold_uncertainty_score":0.999869},"labels":[],"label_agreement":null},{"id":"W3122110021","doi":"10.1155/2021/8858902","title":"Estimation of Driver Lane Change Intention Based on the LSTM and Dempster–Shafer Evidence Theory","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Workload; Computer science; Advanced driver assistance systems; Dempster–Shafer theory; Automation; Component (thermodynamics); Artificial intelligence; Reliability (semiconductor); Orientation (vector space); Estimation; Probability estimation; Machine learning; Engineering; Mathematics","score_opus":0.01559512529634817,"score_gpt":0.23555116995565886,"score_spread":0.21995604465931068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122110021","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":[0.9093879,0.00060262455,0.08932897,0.0004558205,0.00010712039,0.000068707224,0.0000036068895,0.000022422582,0.000022836724],"genre_scores_gemma":[0.9966013,0.00031622613,0.0029854374,0.000062499,0.000013123364,0.0000035439205,0.0000057089223,0.0000069908538,0.000005182991],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9995247,0.000028690309,0.00024058386,0.0000508402,0.00009911377,0.00005607736],"domain_scores_gemma":[0.9995577,0.00015150977,0.000118722135,0.00007494878,0.00008155174,0.000015577198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021952238,0.00006012424,0.000108995664,0.000058400678,0.000026182546,0.0000032816633,0.000045887955,0.000052293995,0.000023129405],"category_scores_gemma":[0.000045239867,0.000045581193,0.000041354233,0.00009558213,0.000032277843,0.0002587083,8.711429e-7,0.00015212083,7.612428e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004233809,0.00007739032,0.006580004,0.00028309276,0.00009261037,0.000054509295,0.0028397706,0.6622074,0.044105273,0.011659034,0.0000143754005,0.27166316],"study_design_scores_gemma":[0.000805079,0.00023735399,0.8737659,0.0009771264,0.00012319384,0.000018115492,0.00058597035,0.054739047,0.060184404,0.008342168,0.000084505955,0.00013714113],"about_ca_topic_score_codex":5.109985e-7,"about_ca_topic_score_gemma":0.000009439065,"teacher_disagreement_score":0.8671859,"about_ca_system_score_codex":0.000019577921,"about_ca_system_score_gemma":0.00001183551,"threshold_uncertainty_score":0.18587472},"labels":[],"label_agreement":null},{"id":"W3122354338","doi":"10.1155/2021/6617205","title":"Implications of a Narrow Automated Vehicle-Exclusive Lane on Interstate 15 Express Lanes","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University Transportation Centers; U.S. Department of Transportation","keywords":"Crash; Microsimulation; Transport engineering; Collision; Traffic flow (computer networking); Computer science; Differential (mechanical device); Automotive engineering; Simulation; Engineering; Computer security","score_opus":0.0066294921592213426,"score_gpt":0.23537721202580092,"score_spread":0.22874771986657957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122354338","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":[0.9934913,0.00030622975,0.00520495,0.00035423596,0.0001557225,0.000056673234,0.000035489025,0.0001859377,0.00020948063],"genre_scores_gemma":[0.9967096,0.0001345297,0.0030398911,0.000024666748,0.000017761246,0.0000032094295,0.00003742871,0.000014944524,0.000018009367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9993029,0.000013529595,0.00041960733,0.00007363611,0.000088488916,0.00010182886],"domain_scores_gemma":[0.9994968,0.000049676994,0.00017992155,0.000116596246,0.00012152558,0.0000354479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000047455167,0.000088936504,0.0001996828,0.00010040478,0.000029606066,0.0000032296377,0.000092459275,0.000075304655,0.000025934127],"category_scores_gemma":[0.0000095775395,0.00008739702,0.00007210126,0.00015776369,0.00002853647,0.00015508279,0.0000014419311,0.00019740498,0.0000022725148],"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":[0.0001246389,0.000079863625,0.001236338,0.00009119142,0.00009705754,0.00008163342,0.0018638354,0.6869103,0.29859746,0.0012996814,0.000116350006,0.009501683],"study_design_scores_gemma":[0.0029360212,0.00061142416,0.5253714,0.00041895648,0.0001560586,0.000103297665,0.002576967,0.007072665,0.45299098,0.0052438905,0.0021329701,0.00038539612],"about_ca_topic_score_codex":6.6020766e-7,"about_ca_topic_score_gemma":0.00001321873,"teacher_disagreement_score":0.6798376,"about_ca_system_score_codex":0.000029760891,"about_ca_system_score_gemma":0.000025990843,"threshold_uncertainty_score":0.3563947},"labels":[],"label_agreement":null},{"id":"W3124953873","doi":"10.1201/9781003144922","title":"Autonomous Safety Control of Flight Vehicles","year":2021,"lang":"en","type":"book","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Aeronautics; Control (management); Automotive engineering; Computer science; Transport engineering; Engineering; Artificial intelligence","score_opus":0.004184866547584369,"score_gpt":0.17377349927660174,"score_spread":0.16958863272901736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124953873","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009892436,0.005241921,0.015013016,0.00014473523,0.00037367138,0.00024813286,0.00015250598,0.0014465393,0.97728056],"genre_scores_gemma":[0.11709128,0.00078225415,0.0009446266,0.0001269028,0.00017238974,0.000019396546,0.00015959895,0.00014960645,0.88055396],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998657,0.000017544759,0.00060729077,0.00027757222,0.00013997377,0.00030064146],"domain_scores_gemma":[0.9990609,0.00012246631,0.00009649057,0.000588804,0.000070160946,0.000061173676],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013294021,0.0003600512,0.0008193351,0.00014334203,0.0000463391,0.000007663143,0.00033484056,0.0010799668,0.0011097199],"category_scores_gemma":[0.000012088676,0.00036364922,0.00024017402,0.00007098078,0.00015027645,0.00004580557,0.000061833285,0.00067304424,0.00015659335],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011534159,0.00015705817,0.0001923477,0.0019067834,0.004148906,0.00045450294,0.0003487917,0.018643653,0.0040381923,0.5900279,0.1874924,0.19247408],"study_design_scores_gemma":[0.0009170152,0.000054120013,0.00021994948,0.00017479992,0.00018711004,0.00003616436,0.00001705391,0.010385307,0.0059213145,0.0044763996,0.97688645,0.0007243323],"about_ca_topic_score_codex":0.0000031128377,"about_ca_topic_score_gemma":0.000017269042,"teacher_disagreement_score":0.789394,"about_ca_system_score_codex":0.000239827,"about_ca_system_score_gemma":0.0002823569,"threshold_uncertainty_score":0.99988157},"labels":[],"label_agreement":null},{"id":"W3125076488","doi":"10.4271/2021-01-0185","title":"Driver Identification Using Driving Behavior, Habits and Driver Characteristics","year":2021,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","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":"General Motors (Canada)","funders":"","keywords":"Identification (biology); Computer science","score_opus":0.010407388676866906,"score_gpt":0.23040182397472578,"score_spread":0.21999443529785886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125076488","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":[0.99038154,0.00042363492,0.000021524058,0.0006884884,0.0003824136,0.0005167402,0.000056852412,0.003821761,0.00370704],"genre_scores_gemma":[0.9944957,0.00077655446,0.0036427386,0.00033979857,0.00011088948,0.00014199546,0.000075221935,0.000152186,0.00026493432],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99650836,0.00009651865,0.0010542871,0.0010251534,0.00048676186,0.00082893844],"domain_scores_gemma":[0.99794817,0.00021443055,0.00017442447,0.0012414235,0.00013940084,0.00028212316],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037281247,0.0006798073,0.00082286604,0.00020786752,0.00049465994,0.00012789264,0.0005876238,0.0012081645,0.00030726418],"category_scores_gemma":[0.00034467684,0.00071544363,0.00025883035,0.0006330137,0.0009584558,0.0005724384,0.00046202066,0.0014766518,0.000088382825],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022670345,0.00016571597,0.00508382,0.000052248673,0.000051212133,0.00016382475,0.00003533786,0.000027788905,0.9782761,0.006312252,0.00025361715,0.009555434],"study_design_scores_gemma":[0.00038762586,0.00013114812,0.9867602,0.00015899507,0.00017293949,0.0003136989,0.00006099562,0.0000039878423,0.0020835411,0.0007426203,0.008450055,0.00073417387],"about_ca_topic_score_codex":0.000007205473,"about_ca_topic_score_gemma":0.0073955623,"teacher_disagreement_score":0.9816764,"about_ca_system_score_codex":0.00035725854,"about_ca_system_score_gemma":0.000078865705,"threshold_uncertainty_score":0.99952966},"labels":[],"label_agreement":null},{"id":"W3125222291","doi":"10.1609/aaai.v35i6.16715","title":"Solution Concepts in Hierarchical Games Under Bounded Rationality With Applications to Autonomous Driving","year":2021,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bounded rationality; Computer science; Context (archaeology); Bounded function; Set (abstract data type); Intersection (aeronautics); Game theory; Mathematical optimization; Artificial intelligence; Mathematical economics; Mathematics; Engineering","score_opus":0.04590703961343839,"score_gpt":0.298804343829207,"score_spread":0.2528973042157686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125222291","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":[0.81544775,0.000065969245,0.17262118,0.0046527754,0.00024428783,0.0014104175,0.000018948249,0.0003862332,0.0051524336],"genre_scores_gemma":[0.9951707,0.000038684786,0.0041635754,0.00009473364,0.000050916617,0.00039143316,0.000008298593,0.000026956915,0.000054680113],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981877,0.000018296672,0.00062362436,0.0005437035,0.00029026446,0.00033640364],"domain_scores_gemma":[0.99899405,0.00007718351,0.00018264898,0.00032777246,0.00034164166,0.000076697426],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033562045,0.00030384376,0.0004072457,0.00017084803,0.00013523865,0.00013301319,0.0008186716,0.00037319475,0.000062741674],"category_scores_gemma":[0.00009556217,0.00026654595,0.00009155103,0.00045311236,0.0004046092,0.00011149738,0.0004213732,0.0011728663,0.000019921265],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072720395,0.00021694227,0.0012446907,0.00022784722,0.0000829273,0.0000010244082,0.0018774007,0.05345229,0.022044566,0.88218796,0.00003348033,0.038558166],"study_design_scores_gemma":[0.00008330975,0.0001402097,0.010157043,0.0014265535,0.00006371428,0.00001085426,0.0021145595,0.22675729,0.3463593,0.41169658,0.00019245336,0.0009981309],"about_ca_topic_score_codex":0.000047554156,"about_ca_topic_score_gemma":0.00035227285,"teacher_disagreement_score":0.47049135,"about_ca_system_score_codex":0.00027671133,"about_ca_system_score_gemma":0.00029060064,"threshold_uncertainty_score":0.99997866},"labels":[],"label_agreement":null},{"id":"W3125605478","doi":"10.1109/iros51168.2021.9636035","title":"LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":200,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; ENCODE; Graph; Benchmark (surveying); Theoretical computer science; Artificial intelligence; Encoder; Representation (politics); Robotics; Motion (physics); Robot","score_opus":0.06709133067816217,"score_gpt":0.28068348370138163,"score_spread":0.21359215302321946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125605478","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":[0.07935241,0.00067473575,0.90547055,0.00088869286,0.004185328,0.0006656112,0.00059051864,0.00033425778,0.007837875],"genre_scores_gemma":[0.99686486,0.00065915816,0.0003007118,0.0000287981,0.00021875853,0.00013292236,0.0005923555,0.000024489289,0.0011779249],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858946,0.000044207536,0.00049345236,0.00038041544,0.00021983274,0.00027263656],"domain_scores_gemma":[0.9991066,0.00013215705,0.0001048133,0.00023438841,0.0003323511,0.000089708206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019377064,0.00021406965,0.00026657595,0.00016831455,0.00016819344,0.00015520111,0.0001964733,0.00018319767,0.00020828255],"category_scores_gemma":[0.00010163673,0.0002137922,0.000103936734,0.0002025479,0.000056158595,0.00012549903,0.000039638497,0.00024443504,0.000034070305],"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":[0.00010331732,0.00033693388,0.0074760825,0.00039740044,0.0009064397,0.0001623927,0.00075756904,0.419547,0.010047423,0.49397135,0.006232852,0.060061242],"study_design_scores_gemma":[0.00047977458,0.0000619098,0.0010133818,0.00023091644,0.00004216295,0.00010869316,0.0009550652,0.9824653,0.00656638,0.0027165634,0.005001733,0.00035808567],"about_ca_topic_score_codex":0.00003743222,"about_ca_topic_score_gemma":0.000036569854,"teacher_disagreement_score":0.9175125,"about_ca_system_score_codex":0.0001193944,"about_ca_system_score_gemma":0.00003872297,"threshold_uncertainty_score":0.87181926},"labels":[],"label_agreement":null},{"id":"W3125926538","doi":"10.1109/cvpr46437.2021.01417","title":"MP3: A Unified Model to Map, Perceive, Predict and Plan","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Exploit; Intersection (aeronautics); Scale (ratio); Key (lock); Artificial intelligence; SAFER; Plan (archaeology); Term (time); Robot; Planner; Motion (physics); Advanced driver assistance systems; Component (thermodynamics); Bridge (graph theory); Semantic mapping; Machine learning; Data mining; Computer security; Engineering","score_opus":0.01567825395237735,"score_gpt":0.2090059808122378,"score_spread":0.19332772685986047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125926538","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":[0.8183034,0.0004937146,0.14900671,0.0007360719,0.00033380187,0.0004419484,0.000087012326,0.0026858458,0.027911488],"genre_scores_gemma":[0.9839105,0.00017836245,0.01410085,0.00013629106,0.000032375145,0.000051709925,0.000060713042,0.000040622486,0.0014885551],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910575,0.000009917531,0.00020331651,0.00036113895,0.00007871975,0.00024116198],"domain_scores_gemma":[0.9993724,0.000017142886,0.000014805318,0.00046922878,0.000023040546,0.00010333109],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000075046824,0.00024569736,0.000307986,0.00011255855,0.000042805215,0.000035693673,0.00022774654,0.00063008437,0.00008107459],"category_scores_gemma":[0.000008544393,0.00025529077,0.00005035846,0.000052707914,0.000036041267,0.000034690056,0.00053081376,0.0007269059,0.00002597202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017809261,0.000026578582,0.0008075217,0.00043685036,0.00023421399,0.00005702457,0.0018348639,0.98002523,0.0009266071,0.0036877198,0.0048527517,0.0070927995],"study_design_scores_gemma":[0.0001624469,0.000016681966,0.0031789325,0.00008922592,0.000033883713,0.000012851108,0.00025623772,0.99272424,0.000549136,0.0017576059,0.0008130067,0.00040575615],"about_ca_topic_score_codex":0.000028846178,"about_ca_topic_score_gemma":0.00007094491,"teacher_disagreement_score":0.16560711,"about_ca_system_score_codex":0.000061159095,"about_ca_system_score_gemma":0.00005429932,"threshold_uncertainty_score":0.9999899},"labels":[],"label_agreement":null},{"id":"W3126608422","doi":"","title":"A Unified Evaluation Framework for Autonomous Driving Vehicles.","year":2020,"lang":"en","type":"article","venue":"IV","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":"Huawei Technologies (Canada)","funders":"","keywords":"Reliability (semiconductor); Software deployment; Computer science; Fidelity; Automation; Safety assurance; Test (biology); Reliability engineering; Scenario testing; Engineering; Software engineering; Variety (cybernetics); Artificial intelligence","score_opus":0.02694590652848825,"score_gpt":0.25801907683329056,"score_spread":0.23107317030480232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126608422","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":[0.45726514,0.00045120931,0.53156954,0.0038669347,0.00033822717,0.0006993561,0.000010173387,0.0025303098,0.003269118],"genre_scores_gemma":[0.9871032,0.000008624392,0.012378836,0.000259541,0.00011967765,0.000075417134,0.0000085938345,0.000028515797,0.000017592893],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994071,0.000012442895,0.00015424378,0.00015274873,0.00008141767,0.00019201868],"domain_scores_gemma":[0.99964833,0.000088286324,0.000023087936,0.00015348817,0.000031581327,0.000055230194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016578898,0.00009950633,0.00012687952,0.000029264891,0.00006824702,0.000013114721,0.00014232048,0.00020019735,0.00006629705],"category_scores_gemma":[0.00015174792,0.0001105011,0.000048055124,0.00012793363,0.000025364621,0.00006401603,0.000021644557,0.00021175841,0.000079253325],"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":[0.00006617411,0.0000554361,0.00336526,0.00023019101,0.00027912983,0.000011738769,0.003921838,0.13612618,0.01622539,0.1905899,0.0058043906,0.6433244],"study_design_scores_gemma":[0.0004165224,0.00005485431,0.0033254684,0.000019025283,0.0000397132,0.0000016468645,0.00008024959,0.9491607,0.0058720894,0.026857834,0.013962032,0.00020984691],"about_ca_topic_score_codex":9.840116e-7,"about_ca_topic_score_gemma":0.0000022864663,"teacher_disagreement_score":0.81303453,"about_ca_system_score_codex":0.00006688179,"about_ca_system_score_gemma":0.000031146385,"threshold_uncertainty_score":0.45061043},"labels":[],"label_agreement":null},{"id":"W3128081710","doi":"10.18280/rces.070402","title":"Real Time Vision System for Autonomous Vehicles","year":2020,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Brake; Acceleration; Computer science; Steering wheel; Computer vision; Simulation; PID controller; Artificial intelligence; Real-time computing; Engineering; Automotive engineering; Control engineering","score_opus":0.011254333594394757,"score_gpt":0.2400681930385871,"score_spread":0.22881385944419233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128081710","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":[0.038790356,0.4673832,0.47789362,0.0023961705,0.0013026161,0.0022677884,0.00007284905,0.00932403,0.00056935346],"genre_scores_gemma":[0.7022349,0.1439964,0.15208635,0.0003031103,0.000878404,0.00024589905,0.0000286409,0.00019801629,0.000028248656],"study_design_codex":"systematic_review","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991312,0.000009551433,0.00042438423,0.00016588878,0.00007749708,0.00019145495],"domain_scores_gemma":[0.9995457,0.000118460586,0.00005373378,0.00017065808,0.00006705202,0.000044392782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015590919,0.00019636663,0.00067107077,0.000040668292,0.000033225548,0.0000043697637,0.00018751167,0.00006507038,0.0000018498272],"category_scores_gemma":[0.000020808202,0.00018073313,0.00013808113,0.00012427969,0.000026886455,0.00005624869,0.000078672994,0.00009922833,0.000029740419],"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":[0.00003252719,0.000054744363,0.000056956145,0.38654318,0.0026579902,0.00002767883,0.0013555009,0.33562735,0.016301075,0.009882347,0.033243496,0.21421714],"study_design_scores_gemma":[0.00037724784,0.00028936457,0.0002703666,0.010017657,0.00014888117,0.000012783768,0.000018618932,0.9476804,0.00329654,0.000011467965,0.0374498,0.00042684717],"about_ca_topic_score_codex":2.5680114e-7,"about_ca_topic_score_gemma":2.4716464e-8,"teacher_disagreement_score":0.6634446,"about_ca_system_score_codex":0.000060482354,"about_ca_system_score_gemma":0.000008578858,"threshold_uncertainty_score":0.73700833},"labels":[],"label_agreement":null},{"id":"W3130946423","doi":"10.1109/lra.2021.3060407","title":"Unsupervised Learning of Lidar Features for Use ina Probabilistic Trajectory Estimator","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Odometry; Lidar; Artificial intelligence; Computer science; Trajectory; Deep learning; Estimator; Unsupervised learning; Feature (linguistics); Inference; Machine learning; Mobile robot; Robot; Remote sensing; Mathematics; Geography; Statistics","score_opus":0.010972416070390078,"score_gpt":0.2102501805823955,"score_spread":0.19927776451200543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130946423","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":[0.7800866,0.00014199439,0.21850616,0.0005913505,0.00016208511,0.00014851673,0.000008164787,0.00033387836,0.000021274653],"genre_scores_gemma":[0.9653669,0.00002149774,0.034420054,0.00009217392,0.000022551932,0.000013270554,0.000017440892,0.000020208014,0.00002591599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994835,0.000018732671,0.00018419998,0.000121961995,0.000057751662,0.0001338558],"domain_scores_gemma":[0.99965894,0.00012794332,0.00003773434,0.000108100496,0.000040091483,0.00002720975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006876189,0.00010193241,0.00016290895,0.000057861416,0.000076862896,0.00002423574,0.000046673293,0.000098663026,0.0000038813228],"category_scores_gemma":[0.00006115324,0.00010875627,0.00004235674,0.00009063584,0.00005392538,0.00010096473,0.000008335485,0.00013776399,0.000001258234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004716877,0.000016713522,0.0008582215,0.0002996973,0.00004786089,0.000005374668,0.00020728834,0.92634505,0.066002905,0.0019370473,0.0003631085,0.0039120265],"study_design_scores_gemma":[0.0004959563,0.00003461033,0.023990849,0.00007173716,0.000051932348,0.000017826698,0.000052292216,0.95300734,0.021548998,0.00020305405,0.00031523587,0.00021014376],"about_ca_topic_score_codex":0.0000016649262,"about_ca_topic_score_gemma":0.0000023108653,"teacher_disagreement_score":0.18528031,"about_ca_system_score_codex":0.000025913585,"about_ca_system_score_gemma":0.000017847482,"threshold_uncertainty_score":0.4434952},"labels":[],"label_agreement":null},{"id":"W3131453904","doi":"","title":"HalentNet: Multimodal Trajectory Forecasting with Hallucinative Intents","year":2021,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Autonomous Vehicle Technology and Safety","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Trajectory; Computer science; Discriminative model; Artificial intelligence; Machine learning; Motion (physics); Feature learning; Robot; Key (lock); Representation (politics)","score_opus":0.04119373225655152,"score_gpt":0.2768955204699636,"score_spread":0.23570178821341206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131453904","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":[0.7321735,0.000057196783,0.03787368,0.0019346189,0.0005359059,0.00018278707,0.000025982512,0.00092187466,0.22629443],"genre_scores_gemma":[0.99556494,0.000038965736,0.0017374356,0.000035523564,0.00004474831,0.000041181356,0.000088323606,0.00002266452,0.0024262376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915123,0.000042714753,0.00018106958,0.00025143006,0.00020429966,0.0001692555],"domain_scores_gemma":[0.99937916,0.00010480182,0.000052611595,0.00014412755,0.00027506115,0.000044258122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006230555,0.00013168229,0.00011708079,0.00012387426,0.0001310157,0.000050223967,0.00017160807,0.000075853255,0.00061167014],"category_scores_gemma":[0.00015357991,0.00013276815,0.00004390974,0.00014125333,0.00010148391,0.0001616361,0.000038060614,0.0005059165,0.00007660239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015738978,0.0003425514,0.08070753,0.0000464498,0.0010923066,0.00067500543,0.006585112,0.71755576,0.015942538,0.0840201,0.00065016176,0.09222509],"study_design_scores_gemma":[0.0009125507,0.00009947155,0.053451363,0.00015239713,0.00002309674,0.00011980532,0.0030897711,0.92893815,0.0098382225,0.0014284545,0.0016116165,0.00033508177],"about_ca_topic_score_codex":0.000017329441,"about_ca_topic_score_gemma":0.0000750331,"teacher_disagreement_score":0.2633914,"about_ca_system_score_codex":0.00009036379,"about_ca_system_score_gemma":0.000055008204,"threshold_uncertainty_score":0.6697359},"labels":[],"label_agreement":null},{"id":"W3132507473","doi":"10.5220/0010241004500457","title":"Road Lane Detection and Classification in Urban and Suburban Areas based on CNNs","year":2021,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial intelligence; Road traffic; Pattern recognition (psychology); Geography; Transport engineering; Engineering","score_opus":0.008227425208326434,"score_gpt":0.18981238179094362,"score_spread":0.1815849565826172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132507473","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":[0.98652756,0.00014510125,0.002687439,0.00028637538,0.000036950645,0.000054920474,0.0000017524519,0.0002880496,0.009971852],"genre_scores_gemma":[0.99957764,0.000021866857,0.0002041626,0.000042992444,0.000008723021,0.000007326947,0.0000049925834,0.0000073865413,0.00012492896],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996774,0.000012535443,0.00008052969,0.00011773951,0.000028874872,0.000082887535],"domain_scores_gemma":[0.9998319,0.000025690662,0.000007299453,0.00010638261,0.000007337635,0.00002136217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000057683123,0.00005980908,0.00006925219,0.00006458554,0.000026113985,0.000008320995,0.000018481016,0.00010886427,0.000022804483],"category_scores_gemma":[0.0000163141,0.000061301456,0.0000072486473,0.00009931639,0.000020581168,0.000034474142,0.0000064812616,0.00012503428,0.0000060774782],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081442704,0.00016510123,0.2934351,0.00015744174,0.00004521635,0.000082923216,0.00049016916,0.0053713964,0.093927905,0.010550904,0.00041192453,0.59528047],"study_design_scores_gemma":[0.0002760564,0.000023133658,0.5856888,0.000009051442,0.0000035274159,0.000005324902,0.000051839248,0.3968451,0.01604764,0.00022138696,0.0007451648,0.00008295344],"about_ca_topic_score_codex":0.00001508361,"about_ca_topic_score_gemma":0.00036047318,"teacher_disagreement_score":0.5951975,"about_ca_system_score_codex":0.000031309522,"about_ca_system_score_gemma":0.000006201331,"threshold_uncertainty_score":0.24998009},"labels":[],"label_agreement":null},{"id":"W3132875902","doi":"10.1109/tie.2021.3059537","title":"Hybrid-Learning-Based Driver Steering Intention Prediction Using Neuromuscular Dynamics","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Energy Research Institute, Nanyang Technological University; State Key Laboratory of Automotive Safety and Energy; National Natural Science Foundation of China; Nanyang Technological University","keywords":"Computer science; Vehicle dynamics; Automotive engineering; Dynamics (music); Artificial intelligence; Control engineering; Engineering; Psychology","score_opus":0.014130203923795912,"score_gpt":0.2024343055280309,"score_spread":0.188304101604235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132875902","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":[0.37427187,0.000056957055,0.62382734,0.000052301,0.0008381403,0.00011939684,0.00002039718,0.00073923235,0.00007434588],"genre_scores_gemma":[0.9993651,0.000097685566,0.0002192491,0.00002186361,0.00006476894,0.000018282684,0.000035094494,0.00006132112,0.00011661917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987165,0.00007999033,0.0003158288,0.0002873905,0.00017932159,0.00042102346],"domain_scores_gemma":[0.9995104,0.00004694889,0.000047398862,0.00025882092,0.000070537084,0.000065896376],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014232827,0.00022192007,0.00021330077,0.00018496162,0.00027552134,0.000035589765,0.00011499378,0.00038314128,0.00008467891],"category_scores_gemma":[0.0000127126705,0.00028050144,0.00016458773,0.00038027516,0.000047171605,0.00015115633,0.0000014887516,0.0017762076,0.000015855785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003381491,0.00006092975,0.000036451842,0.0000107897895,0.000098513105,0.00001927764,0.000010980352,0.96785873,0.007244134,0.00008871407,0.000014033175,0.024523648],"study_design_scores_gemma":[0.0008515008,0.00014902998,0.000021107646,0.000034374978,0.00010950899,0.000037769933,0.00002763392,0.87407446,0.12298449,0.000054973716,0.0014518844,0.0002032648],"about_ca_topic_score_codex":0.000010825341,"about_ca_topic_score_gemma":0.000045236247,"teacher_disagreement_score":0.6250932,"about_ca_system_score_codex":0.0008656638,"about_ca_system_score_gemma":0.00021146142,"threshold_uncertainty_score":0.9999647},"labels":[],"label_agreement":null},{"id":"W3132951397","doi":"10.1155/2021/8894563","title":"A Decision-Making Model for Autonomous Vehicles at Urban Intersections Based on Conflict Resolution","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Beijing Municipal Science and Technology Commission; Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Intersection (aeronautics); Computer science; Sorting; Genetic algorithm; MATLAB; Conflict resolution; Key (lock); Process (computing); Mathematical optimization; Basis (linear algebra); Artificial intelligence; Operations research; Machine learning; Engineering; Algorithm; Transport engineering; Mathematics","score_opus":0.01122450240613985,"score_gpt":0.24976610448822076,"score_spread":0.23854160208208092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132951397","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":[0.44117287,0.00017544914,0.55805844,0.0000932293,0.00025742612,0.00007765442,0.000025102598,0.00008293503,0.000056880242],"genre_scores_gemma":[0.950979,0.00004841214,0.04877533,0.0000684389,0.000043432367,0.000011142015,0.000020227131,0.000023890194,0.000030147212],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914724,0.000008943865,0.0004556908,0.000117408395,0.00012238207,0.0001483252],"domain_scores_gemma":[0.9993551,0.00016656001,0.00014570897,0.0001217519,0.00017021618,0.000040671177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011964956,0.000113937334,0.00019085292,0.00015527505,0.00011452908,0.000007697291,0.00007569242,0.000120545716,0.000012145022],"category_scores_gemma":[0.000044092576,0.000119400596,0.0001566496,0.00013534739,0.000023979712,0.00018965374,0.0000014515347,0.00021737369,0.0000015824193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032250502,0.000035995523,0.00018226683,0.00002397497,0.000028776682,0.000018961655,0.00055968063,0.9773146,0.0070765764,0.00044361732,0.00012210764,0.013870927],"study_design_scores_gemma":[0.0012173747,0.00015449371,0.01117429,0.00021681437,0.000059434165,0.000016527414,0.00016945966,0.9771151,0.0064448295,0.001563446,0.0017233213,0.00014494194],"about_ca_topic_score_codex":2.3059089e-7,"about_ca_topic_score_gemma":0.000073537434,"teacher_disagreement_score":0.5098061,"about_ca_system_score_codex":0.00023617632,"about_ca_system_score_gemma":0.00006676745,"threshold_uncertainty_score":0.4869015},"labels":[],"label_agreement":null},{"id":"W3135173030","doi":"10.1049/itr2.12053","title":"Evaluation of emergency driving behaviour and vehicle collision risk in connected vehicle environment: A deep learning approach","year":2021,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Collision; Motor vehicle crash; Automotive engineering; Vehicle safety; Aeronautics; Computer science; Transport engineering; Engineering; Artificial intelligence; Computer security; Poison control; Human factors and ergonomics; Medical emergency; Medicine","score_opus":0.01773811941754338,"score_gpt":0.22566031091886934,"score_spread":0.20792219150132596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135173030","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":[0.9661463,0.0044836933,0.028079523,0.000007062149,0.00021076454,0.0004226915,0.0000112663165,0.00016304135,0.0004756743],"genre_scores_gemma":[0.9967007,0.0029411612,0.00013633684,7.3916164e-7,0.000017172202,0.000077341465,0.000052076186,0.000034521978,0.000039940278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981336,0.00021363168,0.0006830856,0.00031549705,0.00040166196,0.00025249933],"domain_scores_gemma":[0.9994483,0.00004625532,0.00010967608,0.000246551,0.00009053494,0.000058697995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012841937,0.00018670883,0.00033669345,0.00011899242,0.000080742546,0.000006539495,0.00010609397,0.00024987326,0.00007966041],"category_scores_gemma":[0.000040983065,0.00020951802,0.00007076332,0.00027111857,0.000037471757,0.00009827005,0.000015717023,0.00039107775,0.0000088233355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000088261495,0.00012084932,0.40535834,0.00011188013,0.00007398612,0.000010403777,0.0017503957,0.5808758,0.0049759606,0.0001427317,0.0000016462043,0.0065691373],"study_design_scores_gemma":[0.0005244471,0.000046313493,0.16606256,0.00008537045,0.0001750046,0.000008895186,0.0018202256,0.8204004,0.0104481075,0.00005278626,0.00015034946,0.00022551417],"about_ca_topic_score_codex":0.00010537745,"about_ca_topic_score_gemma":0.00014588445,"teacher_disagreement_score":0.23952459,"about_ca_system_score_codex":0.00014083897,"about_ca_system_score_gemma":0.000024998371,"threshold_uncertainty_score":0.85438967},"labels":[],"label_agreement":null},{"id":"W3152629223","doi":"10.1139/cjce-2020-0801","title":"Using single and multiple unmanned aerial vehicles for microscopic driver behaviour data collection at freeway interchange ramps","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Taibah University","keywords":"Differential GPS; Computer science; Data collection; Curvature; Software; Oblique case; Elevation (ballistics); Shadow (psychology); Traffic speed; Global Positioning System; Simulation; Differential (mechanical device); Real-time computing; Transport engineering; Marine engineering; Engineering; Telecommunications; Aerospace engineering","score_opus":0.03597989354488631,"score_gpt":0.22134653460147552,"score_spread":0.1853666410565892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152629223","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":[0.9586842,0.002012378,0.03773017,0.0000810617,0.0011505836,0.000110211266,0.00013337047,0.000057358277,0.000040665716],"genre_scores_gemma":[0.99523914,0.00003572085,0.0044374787,0.000013369676,0.00016467377,0.0000016135214,0.000026263875,0.000037090525,0.000044669832],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921715,0.000010537436,0.00027132034,0.00014135007,0.000052515265,0.0003071182],"domain_scores_gemma":[0.99938625,0.000060240298,0.00005286959,0.00020672864,0.00007746858,0.00021642575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012815835,0.00014223569,0.0002317641,0.0002806678,0.00013013728,0.000042264488,0.00019200557,0.00016418834,0.00003221523],"category_scores_gemma":[0.00011028592,0.00017092256,0.00004637576,0.00017715948,0.000039495797,0.00020712797,0.00005008441,0.00021719084,6.8356553e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006250519,0.00003877163,0.036078878,0.00038887514,0.0004622616,0.00050361105,0.0018198563,0.12051799,0.831425,0.00014166339,0.005080301,0.0034802738],"study_design_scores_gemma":[0.003439203,0.00021771701,0.010473718,0.000592864,0.00026359936,0.0016696332,0.0003422496,0.75840247,0.18879761,0.00010139047,0.03484929,0.0008502749],"about_ca_topic_score_codex":0.00015805686,"about_ca_topic_score_gemma":0.085508905,"teacher_disagreement_score":0.6426274,"about_ca_system_score_codex":0.00039858388,"about_ca_system_score_gemma":0.00016706064,"threshold_uncertainty_score":0.93117815},"labels":[],"label_agreement":null},{"id":"W3154724615","doi":"10.1016/j.trc.2021.103453","title":"A context-aware pedestrian trajectory prediction framework for automated vehicles","year":2021,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Pedestrian; Interpretability; Computer science; Trajectory; Context (archaeology); Machine learning; Block (permutation group theory); Artificial intelligence; Pedestrian detection; Resource (disambiguation); Focus (optics); Data mining; Engineering; Transport engineering","score_opus":0.055517397092676446,"score_gpt":0.33639876268364016,"score_spread":0.2808813655909637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154724615","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":[0.8242317,0.0036314803,0.11507901,0.004594089,0.0005776741,0.0011823132,0.00056419626,0.049908306,0.00023123372],"genre_scores_gemma":[0.9911205,0.0010748513,0.006816493,0.000013810776,0.000037808346,0.0005792397,0.00023476948,0.00005300998,0.000069488335],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981871,0.000049529008,0.00043679692,0.0003976806,0.00030115817,0.0006276874],"domain_scores_gemma":[0.9988045,0.00035563958,0.000043075797,0.0004566048,0.0002916223,0.000048561637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045733826,0.0002051062,0.00027043864,0.00036084684,0.0004031482,0.000035183017,0.00032459022,0.0006452097,0.000046987792],"category_scores_gemma":[0.00029097128,0.00022588398,0.00010739778,0.0009160154,0.0003269318,0.00018276859,0.000014602347,0.0009046559,0.000019920182],"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":[0.000443775,0.00068276643,0.06610629,0.0033380229,0.0015639769,0.0005404557,0.0055754613,0.06500531,0.03732517,0.2956715,0.069430776,0.4543165],"study_design_scores_gemma":[0.0031866229,0.000585419,0.052893538,0.0010955941,0.00014483118,0.000017738987,0.04080299,0.25406733,0.34780532,0.11832261,0.17948185,0.0015961488],"about_ca_topic_score_codex":0.000015650474,"about_ca_topic_score_gemma":0.00026137911,"teacher_disagreement_score":0.45272037,"about_ca_system_score_codex":0.00010425803,"about_ca_system_score_gemma":0.00010802497,"threshold_uncertainty_score":0.92112815},"labels":[],"label_agreement":null},{"id":"W3154814628","doi":"10.1155/2021/8872889","title":"An Indoor Vehicle-in-the-Loop Simulation Platform Testing Method for Autonomous Emergency Braking","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Key Research and Development Projects of Shaanxi Province; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Simulation; Computer science; Software; Embedded system; Hardware-in-the-loop simulation; Braking system; Automotive engineering; Real-time computing; Engineering; Operating system","score_opus":0.021915379264123614,"score_gpt":0.3047961124657563,"score_spread":0.28288073320163265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154814628","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":[0.6688791,0.0001814149,0.3304409,0.000062492654,0.0002114715,0.00011350528,0.000005347163,0.00005840299,0.000047363446],"genre_scores_gemma":[0.89995795,0.000026342803,0.099866,0.00002478979,0.00007473228,0.00000833828,0.000019990539,0.000017777395,0.000004083934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892646,0.000021157082,0.0006446265,0.00010845635,0.00012484181,0.00017444557],"domain_scores_gemma":[0.999231,0.000224501,0.00020712175,0.00011594966,0.00019029,0.00003118301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041777172,0.000112760485,0.00019706835,0.000112661815,0.00008055727,0.000009673629,0.00013053091,0.000111570014,0.000009181442],"category_scores_gemma":[0.00006896092,0.0001048441,0.00008174181,0.00033100042,0.0000087235885,0.0006096287,8.188488e-7,0.00029290997,4.8727634e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035580393,0.00003421219,0.0021953068,0.000041346866,0.000017873626,0.000029400277,0.0012063171,0.8534424,0.023795579,0.00045150926,0.0000012129468,0.1187493],"study_design_scores_gemma":[0.0020388262,0.00032440704,0.2697374,0.000102524034,0.00010951759,0.000057480134,0.0020066889,0.6970017,0.018797655,0.008772971,0.0007387852,0.00031206582],"about_ca_topic_score_codex":0.0000017486518,"about_ca_topic_score_gemma":0.00009183569,"teacher_disagreement_score":0.2675421,"about_ca_system_score_codex":0.00005936015,"about_ca_system_score_gemma":0.000046181358,"threshold_uncertainty_score":0.42754182},"labels":[],"label_agreement":null},{"id":"W3156199997","doi":"10.1109/itsc48978.2021.9565113","title":"Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Air Force Research Laboratory; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; Western Canada Research Grid; Compute Canada; Defense Advanced Research Projects Agency; Mitacs","keywords":"Trajectory; Computer science; Kinematics; Differentiable function; State (computer science); Acceleration; Artificial neural network; Artificial intelligence; Obstacle avoidance; Control theory (sociology); Simulation; Algorithm; Control (management); Robot; Mathematics; Mobile robot","score_opus":0.01747730950923697,"score_gpt":0.2338954679509317,"score_spread":0.2164181584416947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156199997","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":[0.24969742,0.00097239006,0.743647,0.000095078256,0.0012054245,0.00038277276,0.000016309637,0.0021884772,0.0017951382],"genre_scores_gemma":[0.99676514,0.0001105676,0.0021900877,0.00003289941,0.000112922564,0.0000821029,0.00014790846,0.00005255901,0.0005057955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988035,0.000051961968,0.0003706571,0.0003540898,0.00014277389,0.0002770336],"domain_scores_gemma":[0.9991932,0.00005051121,0.00006124801,0.0006052246,0.0000490608,0.000040792194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017218314,0.00029190944,0.0002763192,0.00009147891,0.00014768996,0.00006089478,0.0002686644,0.00052152015,0.00030646403],"category_scores_gemma":[0.000014504239,0.00024078207,0.00014643725,0.00009321817,0.00005912289,0.000092155955,0.00024775846,0.0011619816,0.000030388592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027943113,0.000035553418,0.0009927042,0.00008454132,0.00015473296,0.0000036083588,0.0005407086,0.97641665,0.0011582313,0.000021883221,0.00007472193,0.020513888],"study_design_scores_gemma":[0.00019542231,0.0000066457746,0.033143774,0.000048724218,0.00006733759,0.0000033065949,0.0001852109,0.9637533,0.0018236624,0.000091151276,0.00046357297,0.00021792496],"about_ca_topic_score_codex":0.000086967426,"about_ca_topic_score_gemma":0.00006580432,"teacher_disagreement_score":0.74706775,"about_ca_system_score_codex":0.00018506896,"about_ca_system_score_gemma":0.000037219972,"threshold_uncertainty_score":0.9818808},"labels":[],"label_agreement":null},{"id":"W3158273949","doi":"10.1155/2021/6676092","title":"Driver Lane-Changing Behavior Prediction Based on Deep Learning","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial neural network; Kinematics; Artificial intelligence; Predictive modelling; Recurrent neural network; Machine learning; Simulation","score_opus":0.004105592410086836,"score_gpt":0.20182859605790843,"score_spread":0.19772300364782158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158273949","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":[0.9094137,0.00014745582,0.089700185,0.000042931428,0.00034577225,0.000043945394,0.0000045137363,0.00013843749,0.00016309255],"genre_scores_gemma":[0.9940693,0.000092484566,0.005690354,0.000020463805,0.00004913499,0.0000036617166,0.000035338486,0.0000167297,0.000022561318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99941057,0.000012509112,0.00026238858,0.000068419824,0.00012786563,0.000118240525],"domain_scores_gemma":[0.99970955,0.000028036962,0.000087815584,0.000059344082,0.00008271255,0.000032551376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000085928885,0.00007882594,0.00012256787,0.00014639307,0.000058523685,0.0000048189954,0.000040250372,0.00008546744,0.000043259977],"category_scores_gemma":[0.000010668254,0.00008296776,0.00006983992,0.00016639009,0.000011254928,0.00017487972,5.4188325e-7,0.00035204235,0.0000031345587],"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":[0.000029386465,0.000028918368,0.008467964,0.000017071237,0.00001522596,0.00014915931,0.0003880234,0.9460319,0.013287215,0.000106861444,0.0000024901037,0.03147575],"study_design_scores_gemma":[0.002193411,0.00037086612,0.890415,0.0002006499,0.00018868549,0.000058342062,0.0012366447,0.05448916,0.04773788,0.0002516825,0.0025963604,0.00026126913],"about_ca_topic_score_codex":1.4445467e-7,"about_ca_topic_score_gemma":0.0000067419273,"teacher_disagreement_score":0.8915428,"about_ca_system_score_codex":0.00005525368,"about_ca_system_score_gemma":0.000016009919,"threshold_uncertainty_score":0.3383327},"labels":[],"label_agreement":null},{"id":"W3159893765","doi":"10.1109/iros47612.2022.9982283","title":"Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Counterfactual thinking; Trajectory; Artificial intelligence; Class (philosophy); Machine learning; Exploit","score_opus":0.05628910798199491,"score_gpt":0.2520721698402844,"score_spread":0.1957830618582895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159893765","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":[0.9384162,0.0010361919,0.027930846,0.00048445788,0.009072547,0.0010113604,0.0003192994,0.0009753923,0.020753734],"genre_scores_gemma":[0.9980264,0.00015424707,0.0000673078,0.00011587505,0.00019026481,0.00028266152,0.000084701765,0.00004770743,0.0010308773],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978091,0.00012313291,0.0006913091,0.0004786908,0.0005261152,0.0003716562],"domain_scores_gemma":[0.9991999,0.00009304022,0.00020716163,0.00028505595,0.00009472829,0.00012009171],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005267929,0.00033516306,0.000362795,0.00027726885,0.0004558296,0.00011963151,0.00052128534,0.00013780603,0.0006340999],"category_scores_gemma":[0.000024404717,0.00034393012,0.00011577474,0.00016639929,0.00010173242,0.00010288544,0.00017774891,0.0007566207,0.00003748495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050388368,0.00007124784,0.00082384987,0.000059246377,0.00020142489,0.00006956461,0.0005128106,0.95140773,0.0038471853,0.03327219,0.0007201139,0.008964238],"study_design_scores_gemma":[0.00026165217,0.00020892844,0.00007045069,0.00007784792,0.00001711943,0.00018667136,0.0015511359,0.9897996,0.0014544777,0.0006696051,0.0052988078,0.00040369772],"about_ca_topic_score_codex":0.00010777918,"about_ca_topic_score_gemma":0.0000606008,"teacher_disagreement_score":0.059610188,"about_ca_system_score_codex":0.0005381964,"about_ca_system_score_gemma":0.00005976133,"threshold_uncertainty_score":0.9999013},"labels":[],"label_agreement":null},{"id":"W3160450581","doi":"10.1109/saner50967.2021.00078","title":"An Analysis of Testing Scenarios for Automated Driving Systems","year":2021,"lang":"en","type":"article","venue":"","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":"Western University","funders":"","keywords":"Automotive industry; Test (biology); Automation; Computer science; Disengagement theory; Advanced driver assistance systems; Engineering; Systems engineering; Artificial intelligence","score_opus":0.012418323712484665,"score_gpt":0.24028757248991303,"score_spread":0.22786924877742837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160450581","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":[0.8932978,0.0001074033,0.10239517,0.000013179257,0.00007568481,0.00008734305,0.000009807703,0.0027255707,0.0012880535],"genre_scores_gemma":[0.988188,0.0000019220877,0.011688364,0.000005145798,0.000009370721,0.000012097661,0.00002138705,0.0000127261,0.00006096835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946046,0.000011287103,0.00022133173,0.000118634714,0.000042711174,0.0001455903],"domain_scores_gemma":[0.99950534,0.000113788796,0.000028562712,0.00023433585,0.00009062607,0.000027323982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012452387,0.0000715724,0.0002380614,0.0001388895,0.00004344013,0.000011304045,0.00008807004,0.00010626886,0.000022135397],"category_scores_gemma":[0.00006491191,0.00007275565,0.00005761101,0.00068870216,0.000015428637,0.000059986356,0.000012911614,0.00005584875,0.0000019240247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.9486547e-7,0.000016498863,0.026349131,0.000050294253,0.00044867615,0.0000034013688,0.000051654995,0.9324106,0.037837937,0.0012865212,0.000034687677,0.0015100115],"study_design_scores_gemma":[0.00007731209,0.000016238706,0.016874747,0.000012593333,0.00021254251,0.000002875213,0.00019013755,0.97660905,0.0058681783,0.000010798644,0.00004245176,0.000083092076],"about_ca_topic_score_codex":0.000025432246,"about_ca_topic_score_gemma":0.00007267089,"teacher_disagreement_score":0.09489023,"about_ca_system_score_codex":0.000029693945,"about_ca_system_score_gemma":0.000019240031,"threshold_uncertainty_score":0.2966889},"labels":[],"label_agreement":null},{"id":"W3162665729","doi":"10.1109/tits.2021.3076140","title":"Driving Behavior Analysis Guidelines for Intelligent Transportation Systems","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":162,"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","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Intelligent transportation system; Profiling (computer programming); Data collection; Key (lock); Risk analysis (engineering); Field (mathematics); Variety (cybernetics); Computer science; Advanced driver assistance systems; Data science; Transport engineering; Engineering; Systems engineering; Computer security; Artificial intelligence","score_opus":0.0463532577251805,"score_gpt":0.29372938588191216,"score_spread":0.24737612815673166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162665729","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":[0.104218714,0.00096562057,0.888738,0.00007392903,0.002971801,0.001114638,0.0008092514,0.0010553014,0.000052767547],"genre_scores_gemma":[0.9948764,0.00075988314,0.0012382356,0.000034179884,0.00010063543,0.0015376164,0.0006095142,0.00010265463,0.0007409009],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99654543,0.00006305226,0.0018322985,0.0006376653,0.0004309027,0.00049067143],"domain_scores_gemma":[0.9980419,0.00017883607,0.00018546282,0.0005553678,0.00085733074,0.00018106266],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003656979,0.0004857835,0.00076851156,0.00066333637,0.00028899626,0.000096399795,0.0002491199,0.00047389418,0.00012864865],"category_scores_gemma":[0.0000070759397,0.00053231255,0.000702843,0.0012845824,0.00006288301,0.00025031652,1.7824244e-7,0.00038897656,0.000054113985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032126398,0.0001767078,0.00086768717,0.0002839148,0.0012727897,0.00002883064,0.00080371037,0.98685044,0.002040982,0.0009868897,0.00015608521,0.0064998083],"study_design_scores_gemma":[0.0015089957,0.00035664145,0.0068155625,0.0006912571,0.009051242,0.00004358199,0.012142351,0.67321146,0.26533225,0.00007800198,0.028251821,0.0025168664],"about_ca_topic_score_codex":0.00016205934,"about_ca_topic_score_gemma":0.001596012,"teacher_disagreement_score":0.89065766,"about_ca_system_score_codex":0.00023751396,"about_ca_system_score_gemma":0.00007978666,"threshold_uncertainty_score":0.9997128},"labels":[],"label_agreement":null},{"id":"W3163654451","doi":"10.1061/jtepbs.0000535","title":"Preliminary Safety Evaluation of Self-Driving, Low-Speed Shuttle","year":2021,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Autonomous Vehicle Technology and Safety","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":"Regional Municipality of Niagara","funders":"","keywords":"Reliability (semiconductor); Aeronautics; Computer science; Downtown; Automotive engineering; Simulation; Reliability engineering; Engineering","score_opus":0.00965315511937113,"score_gpt":0.21380479177066214,"score_spread":0.20415163665129102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163654451","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":[0.9660848,0.0028856553,0.028662013,0.000034290853,0.0016544617,0.0001827355,0.000023472703,0.00023668319,0.0002359382],"genre_scores_gemma":[0.99884933,0.0001526973,0.0008022861,0.0000017444054,0.00011497399,0.00000520933,0.00002352248,0.000030673054,0.000019534853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834263,0.000038142247,0.0009363186,0.00009644782,0.00042665075,0.0001597913],"domain_scores_gemma":[0.9990132,0.00006102329,0.00022907802,0.00017122664,0.0004617893,0.00006370764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008106782,0.0001445039,0.00036288865,0.00016006517,0.000025802101,0.00000925778,0.000118244985,0.00016047433,0.000037119193],"category_scores_gemma":[0.00003625604,0.00015309284,0.00013594849,0.0002488543,0.000011856707,0.00019257254,0.0000022917336,0.00024810564,0.00000339467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013324919,0.00004683996,0.0015197924,0.00034675864,0.0002244268,0.000027332197,0.0005860905,0.98627895,0.0101022255,0.00028212264,0.00014960983,0.0004225368],"study_design_scores_gemma":[0.0013815127,0.000120971374,0.062757306,0.0007877152,0.0004229011,0.0001527115,0.00026779927,0.9033735,0.025509704,0.000014811807,0.004928035,0.00028303073],"about_ca_topic_score_codex":0.0000013932315,"about_ca_topic_score_gemma":0.0000029146888,"teacher_disagreement_score":0.08290544,"about_ca_system_score_codex":0.0001259981,"about_ca_system_score_gemma":0.00011387785,"threshold_uncertainty_score":0.6242945},"labels":[],"label_agreement":null},{"id":"W3165051784","doi":"10.4271/12-04-02-0012","title":"In Pursuit of Emergency Procedures for Automated Driving System-Involved Scenarios","year":2021,"lang":"en","type":"article","venue":"SAE International Journal of Connected and Automated Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Medical emergency; Aeronautics; Medicine; Engineering","score_opus":0.008354369210835533,"score_gpt":0.2543266181449094,"score_spread":0.24597224893407388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165051784","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":[0.9956891,0.0014967511,0.0003940873,0.000554512,0.00085004274,0.000113934,0.000027935948,0.000780809,0.00009285746],"genre_scores_gemma":[0.9983237,0.00033961027,0.0011769416,0.000022610717,0.0000799995,0.0000076767365,0.000011854503,0.00002352237,0.000014075687],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986144,0.00003609608,0.0008036781,0.00012775745,0.00022997524,0.00018815107],"domain_scores_gemma":[0.9988738,0.00010353673,0.00026395635,0.00008571227,0.000622043,0.00005099109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000268054,0.0001503652,0.00034452288,0.00031673972,0.000038769595,0.000027199365,0.0003051691,0.00017414875,0.000027651628],"category_scores_gemma":[0.00025997905,0.00014551735,0.00009828893,0.00022599097,0.000040178686,0.00023621634,0.000046285004,0.00019005538,0.0000013616295],"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":[0.00024635583,0.00035479057,0.035269,0.0012608622,0.0018361852,0.0006032352,0.0012897517,0.030749142,0.9038228,0.0115952045,0.0052595395,0.007713125],"study_design_scores_gemma":[0.002911244,0.00013205459,0.103080824,0.0014340902,0.000082783234,0.0004346191,0.0014633845,0.80553335,0.08248633,0.001390549,0.0007078882,0.0003428893],"about_ca_topic_score_codex":0.0000062041527,"about_ca_topic_score_gemma":0.00009434789,"teacher_disagreement_score":0.82133645,"about_ca_system_score_codex":0.00008627623,"about_ca_system_score_gemma":0.00013121022,"threshold_uncertainty_score":0.5934025},"labels":[],"label_agreement":null},{"id":"W3165441654","doi":"10.1145/3460319.3464802","title":"Automatic test suite generation for key-points detection DNNs using many-objective search (experience paper)","year":2021,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation of Korea; Fonds National de la Recherche Luxembourg; National Research Foundation; European Commission","keywords":"Suite; Deep neural networks; Object detection; Artificial neural network; Pattern recognition (psychology); Feature extraction; Face detection; Deep learning","score_opus":0.021814992283682045,"score_gpt":0.25543043313971026,"score_spread":0.2336154408560282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165441654","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":[0.6761968,0.000048430535,0.32241863,0.00006276882,0.00014554782,0.00017641958,0.0000046096197,0.0005605884,0.0003861961],"genre_scores_gemma":[0.98671025,0.000015719377,0.0128842555,0.000062388295,0.00007741094,0.00005918419,0.000009095275,0.000026468751,0.00015520703],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992131,0.00002036558,0.00019923614,0.00022389891,0.000085219675,0.00025820226],"domain_scores_gemma":[0.9995715,0.00009869337,0.000017697379,0.0002034397,0.000071337294,0.000037311158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012800207,0.00012357885,0.00013547017,0.00006963159,0.00021481693,0.00003394401,0.0000762343,0.00016011298,0.00018454216],"category_scores_gemma":[0.00009021559,0.00013147052,0.00005007086,0.00023180158,0.000039450784,0.00023465771,0.000033014338,0.00015364094,0.000025251358],"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":[0.0000022452184,0.000036131965,0.00090806413,0.000056290482,0.00003773955,0.000009005339,0.0014529915,0.008562123,0.94194514,0.0014034946,0.000019254345,0.04556755],"study_design_scores_gemma":[0.00013138141,0.000021421842,0.0015241539,0.000006326208,0.0000073182887,0.000024601191,0.0002689976,0.5962549,0.4014548,0.000116067946,0.00009021343,0.00009984998],"about_ca_topic_score_codex":0.000017767145,"about_ca_topic_score_gemma":0.00014645595,"teacher_disagreement_score":0.58769274,"about_ca_system_score_codex":0.00018706253,"about_ca_system_score_gemma":0.000038701324,"threshold_uncertainty_score":0.53612125},"labels":[],"label_agreement":null},{"id":"W3168387826","doi":"10.1155/2021/6687378","title":"Driving Style Recognition under Connected Circumstance Using a Supervised Hierarchical Bayesian Model","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Latent Dirichlet allocation; Computer science; Artificial intelligence; Software deployment; Machine learning; Intelligent transportation system; Bayesian probability; Engineering; Topic model","score_opus":0.014104018428553397,"score_gpt":0.2302910251917323,"score_spread":0.21618700676317892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168387826","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":[0.56702805,0.00015019132,0.43251553,0.00006986619,0.00009782028,0.000033884462,0.000007369665,0.00006572862,0.000031536758],"genre_scores_gemma":[0.9336279,0.00016611247,0.0660666,0.000039334725,0.000036919013,0.0000016182335,0.000031253254,0.000025657762,0.000004587034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907696,0.000021511541,0.00047235956,0.00011265013,0.00014534876,0.00017119646],"domain_scores_gemma":[0.9994903,0.000046078367,0.00011304437,0.00009476939,0.00019040334,0.00006539334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009634624,0.000116388976,0.00022221815,0.00010722297,0.00006606672,0.000010028525,0.00006849389,0.00013099232,0.000034642857],"category_scores_gemma":[0.000019409203,0.00013049717,0.00009295302,0.00023518126,0.000030042953,0.00043235745,0.0000012845956,0.00038254377,0.0000010014601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003466533,0.00002887314,0.00029617187,0.000031425276,0.000046367208,0.00006240199,0.0005392982,0.8234136,0.16545156,0.00032803632,0.0000021100107,0.009765519],"study_design_scores_gemma":[0.0027203378,0.00008474822,0.037980445,0.00037641232,0.00016559719,0.00015559139,0.0014404289,0.86540073,0.060518507,0.030664872,0.000044267763,0.00044803458],"about_ca_topic_score_codex":4.4803173e-7,"about_ca_topic_score_gemma":0.000044454107,"teacher_disagreement_score":0.36659986,"about_ca_system_score_codex":0.00009725205,"about_ca_system_score_gemma":0.00008739996,"threshold_uncertainty_score":0.532152},"labels":[],"label_agreement":null},{"id":"W3171161062","doi":"10.48550/arxiv.2105.14152","title":"Radar Odometry Combining Probabilistic Estimation and Unsupervised Feature Learning","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Odometry; Computer science; Radar; Artificial intelligence; Probabilistic logic; Trajectory; Feature (linguistics); Estimator; Computer vision; Mobile robot; Telecommunications; Mathematics; Robot","score_opus":0.02359011091873136,"score_gpt":0.15778053681759532,"score_spread":0.13419042589886396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171161062","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":[0.92465174,0.0005664693,0.07220025,0.000042852,0.00023065074,0.00019736933,0.0000058285045,0.0010162806,0.0010885694],"genre_scores_gemma":[0.99739933,0.00030880442,0.0017684811,0.000010797171,0.00001802878,0.0000010425606,0.00008574305,0.00003782645,0.00036993195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990009,0.00006677298,0.00013501018,0.0005075596,0.000042541957,0.00024721047],"domain_scores_gemma":[0.99934864,0.000087401095,0.00006560469,0.0003685642,0.000051309624,0.00007847572],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014256549,0.0002752511,0.00034254236,0.00020304159,0.00017804142,0.000048011058,0.00024120735,0.0006590235,0.000032689084],"category_scores_gemma":[0.00007377223,0.00035627303,0.00008259634,0.00035741812,0.00012957415,0.00013724067,0.00038963294,0.001482701,0.000010872971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013310736,0.0000214103,0.0032346616,0.00040124613,0.00016102598,0.00018946693,0.00026426686,0.9864158,0.00015370549,0.006019907,0.000036548143,0.0030886703],"study_design_scores_gemma":[0.0004334571,0.0000252875,0.0035311596,0.00017016858,0.00014423167,0.000013371412,0.00034716708,0.99050015,0.00014190248,0.004179997,0.00011750096,0.00039562944],"about_ca_topic_score_codex":0.000012946567,"about_ca_topic_score_gemma":0.00001074781,"teacher_disagreement_score":0.07274762,"about_ca_system_score_codex":0.00017309816,"about_ca_system_score_gemma":0.00005447358,"threshold_uncertainty_score":0.9998889},"labels":[],"label_agreement":null},{"id":"W3171619998","doi":"10.1109/icst49551.2021.00050","title":"Digital Twins Are Not Monozygotic – Cross-Replicating ADAS Testing in Two Industry-Grade Automotive Simulators","year":2021,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":32,"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","funders":"Electronic Components and Systems for European Leadership; National Research Foundation of Korea; VINNOVA; National Research Foundation; Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Advanced driver assistance systems; Automotive industry; Computer science; Automation; Test (biology); Replication (statistics); Process (computing); Software; Simulation; Embedded system; Reliability engineering; Artificial intelligence; Engineering","score_opus":0.02349157035368238,"score_gpt":0.2788021627154865,"score_spread":0.25531059236180415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171619998","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":[0.97777194,0.000055232875,0.001078654,0.00017715125,0.00008459118,0.00012373726,0.000016775417,0.001620767,0.019071177],"genre_scores_gemma":[0.9975291,0.0000011091525,0.001709886,0.00011977271,0.000047025314,0.000014867733,0.000007893356,0.000048230137,0.000522117],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985985,0.000015758113,0.0004140443,0.000387536,0.000116402785,0.0004677543],"domain_scores_gemma":[0.9990683,0.0002826939,0.000062532614,0.0004178699,0.00008190893,0.000086675056],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011859969,0.00023409844,0.00028193148,0.000108834734,0.00010334959,0.000094103874,0.00021385877,0.00033921108,0.000057577898],"category_scores_gemma":[0.0006068352,0.0002564259,0.000058742804,0.0006880885,0.000089357636,0.0003059769,0.0001485053,0.001075221,0.00005578088],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024330782,0.00007278035,0.63320136,0.000028508555,0.0000400298,0.00020725433,0.00017771315,0.34733036,0.0012555128,0.0006132996,0.000020286152,0.017050456],"study_design_scores_gemma":[0.0009090912,0.000021553988,0.36868006,0.00011583524,0.000012402737,0.00008002569,0.0007053044,0.58327246,0.044607792,0.00067914644,0.0003372889,0.00057901983],"about_ca_topic_score_codex":0.000026249285,"about_ca_topic_score_gemma":0.000073958356,"teacher_disagreement_score":0.26452127,"about_ca_system_score_codex":0.00018065529,"about_ca_system_score_gemma":0.000062245,"threshold_uncertainty_score":0.9999888},"labels":[],"label_agreement":null},{"id":"W3171935329","doi":"10.1109/lcsys.2021.3089641","title":"Safe Motion Planning Against Multimodal Distributions Based on a Scenario Approach","year":2021,"lang":"en","type":"article","venue":"IEEE Control Systems Letters","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bounding overwatch; Computer science; Polytope; Motion planning; Mathematical optimization; Preprocessor; Motion (physics); Artificial intelligence; Mathematics","score_opus":0.008145522934651312,"score_gpt":0.19226405905875196,"score_spread":0.18411853612410065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171935329","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":[0.18557577,0.00018694208,0.8096863,0.0008039037,0.0008548902,0.0003243077,0.00010091663,0.0008117044,0.0016552643],"genre_scores_gemma":[0.9985898,0.0000013396423,0.00031539955,0.00062772713,0.00018144575,0.00011453424,0.000114445946,0.000032915625,0.000022404221],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875295,0.00009924712,0.0003167841,0.00028871375,0.00015942224,0.00038286482],"domain_scores_gemma":[0.999361,0.00009624548,0.000054187567,0.00037790815,0.000036599533,0.00007408056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019517586,0.00021908218,0.00031792675,0.00009939764,0.00017498861,0.000047603015,0.00014406658,0.00021857332,0.0000037374505],"category_scores_gemma":[0.000028845334,0.00023031501,0.000111479734,0.00017517268,0.000051305935,0.00007675266,0.0000070139504,0.00041656572,0.000047539943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016430644,0.000042460397,0.0013788912,0.000060666825,0.000075321244,0.00008183615,0.00004371698,0.9620856,0.03424574,0.00017401666,0.0010954932,0.00069981377],"study_design_scores_gemma":[0.0015308135,0.000012470891,0.002692558,0.00008394878,0.000026222115,0.00001581299,0.000042146752,0.9929972,0.0008594541,0.0000018949871,0.0014944976,0.00024292593],"about_ca_topic_score_codex":0.000013417044,"about_ca_topic_score_gemma":0.000001689342,"teacher_disagreement_score":0.81301403,"about_ca_system_score_codex":0.00027487925,"about_ca_system_score_gemma":0.000027430739,"threshold_uncertainty_score":0.93919736},"labels":[],"label_agreement":null},{"id":"W3173280621","doi":"10.1109/cvpr46437.2021.00095","title":"SceneGen: Learning to Generate Realistic Traffic Scenes","year":2021,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Heuristics; Computer science; Fidelity; Artificial intelligence; Set (abstract data type); Autoregressive model; Perception; Scale (ratio); Machine learning; Computer vision","score_opus":0.0076423196709713166,"score_gpt":0.20114916063623103,"score_spread":0.19350684096525972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173280621","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":[0.9678076,0.00052102265,0.013161081,0.0006240353,0.00022813832,0.000062306244,0.0000023411296,0.0022002873,0.015393203],"genre_scores_gemma":[0.9929676,0.00008262653,0.003953463,0.00015426116,0.00005737421,0.000008852396,0.000010782756,0.000025827443,0.0027392085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938774,0.000018000912,0.00013708805,0.00016578917,0.000054921795,0.00023646762],"domain_scores_gemma":[0.9996939,0.000020329624,0.000007083602,0.00017828793,0.000031443535,0.00006895536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007021886,0.00010548572,0.00013013059,0.000052418545,0.000087808025,0.00001784334,0.000089661975,0.000108087945,0.00029999032],"category_scores_gemma":[0.000031816828,0.00011023717,0.000033388795,0.0002379482,0.000016788219,0.000033547698,0.000034161912,0.00018593472,0.00034370332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028961315,0.0000120001605,0.00026969478,0.000021289661,0.00003239464,0.00007781334,0.0001559509,0.9134431,0.014608725,0.003524042,0.0016019413,0.06625015],"study_design_scores_gemma":[0.00052132393,0.00008085202,0.0056030783,0.00004621935,0.000042298827,0.00014080944,0.0006080694,0.70197934,0.21288224,0.0003413314,0.076843575,0.00091089384],"about_ca_topic_score_codex":0.000003416209,"about_ca_topic_score_gemma":0.00005052463,"teacher_disagreement_score":0.21146378,"about_ca_system_score_codex":0.000036075064,"about_ca_system_score_gemma":0.000023286326,"threshold_uncertainty_score":0.44953412},"labels":[],"label_agreement":null},{"id":"W3174203047","doi":"10.15607/rss.2021.xvii.029","title":"Radar Odometry Combining Probabilistic Estimation and Unsupervised Feature Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Odometry; Probabilistic logic; Pattern recognition (psychology); Feature (linguistics); Radar; Unsupervised learning; Mobile robot; Robot","score_opus":0.005271317232354826,"score_gpt":0.19150731775360938,"score_spread":0.18623600052125455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174203047","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":[0.93763864,0.0014602372,0.046687756,0.00061516155,0.00011794421,0.00011127776,0.0000013802197,0.0018314156,0.011536192],"genre_scores_gemma":[0.9820917,0.000042815056,0.01724618,0.00003213087,0.0000090272415,0.0000044583726,0.000016506172,0.000016006847,0.00054114993],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956775,0.000018355659,0.0000945287,0.00013052073,0.000051043295,0.00013782307],"domain_scores_gemma":[0.9997562,0.000070750175,0.000010193085,0.00010942949,0.000021169762,0.00003222337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008461451,0.000090406735,0.00012281418,0.00004819619,0.0001028994,0.000020510326,0.00004270094,0.00014506749,0.00007637899],"category_scores_gemma":[0.00009861265,0.00009195247,0.000017857168,0.00019740587,0.000038322025,0.00007861475,0.000033048167,0.0003237234,0.000017032033],"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":[0.00002842146,0.00011523396,0.019883083,0.00095480605,0.00038049856,0.0002192242,0.0018153103,0.23879708,0.04436744,0.0920167,0.0018551232,0.59956706],"study_design_scores_gemma":[0.0006331409,0.000038210364,0.011225831,0.000044102526,0.00003456592,0.00010100934,0.0003742834,0.9730758,0.008460466,0.0034772423,0.0022494444,0.0002859172],"about_ca_topic_score_codex":0.0000011986089,"about_ca_topic_score_gemma":0.0000032087578,"teacher_disagreement_score":0.73427874,"about_ca_system_score_codex":0.00002643442,"about_ca_system_score_gemma":0.0000136644785,"threshold_uncertainty_score":0.37497127},"labels":[],"label_agreement":null},{"id":"W3174412502","doi":"10.1609/aaai.v35i6.16715","title":"Solution Concepts in Hierarchical Games Under Bounded Rationality With Applications to Autonomous Driving","year":2021,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"University of Waterloo","funders":"","keywords":"Bounded rationality; Computer science; Context (archaeology); Intersection (aeronautics); Set (abstract data type); Bounded function; Nash equilibrium; Game theory; Mathematical optimization; Artificial intelligence; Mathematical economics; Mathematics; Engineering","score_opus":0.01056612232632722,"score_gpt":0.2545281096781469,"score_spread":0.24396198735181965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174412502","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":[0.26995575,0.00006463791,0.7180228,0.0025743411,0.000029426239,0.00021843138,0.000003057425,0.0005994884,0.008532072],"genre_scores_gemma":[0.9815224,0.0000060576335,0.017688002,0.00023739715,0.000021816759,0.00014133706,0.000020784091,0.000012693337,0.00034953418],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99931574,0.000022115093,0.00017878687,0.00020268334,0.000078503384,0.00020214368],"domain_scores_gemma":[0.9996276,0.000056672485,0.000011457906,0.00021559109,0.00003382102,0.00005488738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000092269984,0.00009564422,0.0001264161,0.000060947346,0.00007735022,0.000019557348,0.000084627085,0.00011238843,0.00013032608],"category_scores_gemma":[0.000010348787,0.000095195835,0.000020740172,0.0003003336,0.00007108282,0.000080474994,0.000036581627,0.00021539479,0.000049975366],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000151337235,0.00015269051,0.015131484,0.000031682037,0.00007676009,0.000018957624,0.00041379183,0.103330284,0.011259838,0.8436525,0.0003775807,0.025539318],"study_design_scores_gemma":[0.0020740433,0.000112714086,0.63894,0.00008133718,0.000039769544,0.00013879657,0.00092566817,0.18902612,0.04326485,0.07961136,0.044451103,0.0013342403],"about_ca_topic_score_codex":0.000012022895,"about_ca_topic_score_gemma":0.0011201223,"teacher_disagreement_score":0.7640411,"about_ca_system_score_codex":0.00016804635,"about_ca_system_score_gemma":0.00010578148,"threshold_uncertainty_score":0.38819733},"labels":[],"label_agreement":null},{"id":"W3178384248","doi":"10.1109/jiot.2021.3093523","title":"GSAN: Graph Self-Attention Network for Learning Spatial–Temporal Interaction Representation in Autonomous Driving","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","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 Victoria","funders":"Research Grants Council, University Grants Committee","keywords":"Computer science; Generality; Graph; Representation (politics); Interaction information; Attention network; Trajectory; Visualization; Artificial intelligence; Human–computer interaction; Machine learning; Theoretical computer science","score_opus":0.009615958042463782,"score_gpt":0.24362451485211262,"score_spread":0.23400855680964883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178384248","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":[0.6989506,0.00014146067,0.29867673,0.00012114151,0.0015135966,0.00008666764,3.5896377e-7,0.00019394855,0.00031548744],"genre_scores_gemma":[0.98989403,0.00009383876,0.009591143,0.000023567178,0.00021372575,0.0000095079395,0.000010122115,0.000029782224,0.00013425191],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875546,0.00007766685,0.0006199425,0.00017102735,0.00011868438,0.00025721942],"domain_scores_gemma":[0.9993576,0.00010088783,0.00027873254,0.000108107095,0.00011219093,0.000042501062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053555454,0.00013803458,0.00025710725,0.00019993563,0.00007188551,0.00005736064,0.00015267672,0.0001742072,0.000041796935],"category_scores_gemma":[0.00007395411,0.00015689351,0.0001650409,0.0001822294,0.000022399881,0.00050291145,0.000029147446,0.0008310954,0.000004427527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018314902,0.00020196696,0.30276486,0.00021876229,0.00063996745,0.00019936205,0.005799653,0.54008967,0.039778277,0.0004966729,0.0027625856,0.1068651],"study_design_scores_gemma":[0.0013108205,0.00020413657,0.028209506,0.00065185677,0.00007403593,0.0007304605,0.00071004673,0.91410947,0.046760716,0.0049154256,0.0019600056,0.00036351214],"about_ca_topic_score_codex":0.00007972118,"about_ca_topic_score_gemma":0.00011351336,"teacher_disagreement_score":0.37401983,"about_ca_system_score_codex":0.00019145476,"about_ca_system_score_gemma":0.000033380235,"threshold_uncertainty_score":0.63979316},"labels":[],"label_agreement":null},{"id":"W3179755228","doi":"10.1109/sbst52555.2021.00019","title":"SWAT tool at the SBST 2021 Tool Competition","year":2021,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Polytechnique Montréal","funders":"","keywords":"Context (archaeology); Competition (biology); Test (biology); Computer science; Reliability engineering; Simulation; Engineering","score_opus":0.004745931818835941,"score_gpt":0.17892889057119743,"score_spread":0.17418295875236148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3179755228","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":[0.8490195,0.00070032064,0.011538926,0.0035110104,0.00043049504,0.00013139496,0.00001280786,0.00081415323,0.13384134],"genre_scores_gemma":[0.99007714,0.00014993838,0.00043205934,0.00028127557,0.00005370728,0.000013869855,0.000026787691,0.00001317479,0.008952075],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995366,0.000013831136,0.0001242908,0.000110684006,0.00006012067,0.00015452276],"domain_scores_gemma":[0.999613,0.000044698812,0.000008560103,0.00030062805,0.000020654976,0.0000124518865],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00007046941,0.00008088692,0.000086616994,0.000013711864,0.000117583666,0.000013767324,0.0000905369,0.00010435807,0.005585011],"category_scores_gemma":[0.000013194788,0.0000612564,0.00004597016,0.00010828375,0.000045110344,0.00005189922,0.00007157968,0.00015740447,0.0011426824],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028770402,0.0001354265,0.006598857,0.000135203,0.00037664562,0.00024198528,0.00056945055,0.028310604,0.14208858,0.55217236,0.08576176,0.18358035],"study_design_scores_gemma":[0.000642097,0.00002591793,0.023539834,0.000028265182,0.000035684105,0.0001377411,0.0003802567,0.03313611,0.31778988,0.0039522434,0.61977935,0.00055261544],"about_ca_topic_score_codex":0.0000031473533,"about_ca_topic_score_gemma":0.00013247388,"teacher_disagreement_score":0.54822016,"about_ca_system_score_codex":0.00006330938,"about_ca_system_score_gemma":0.000012508192,"threshold_uncertainty_score":0.99963504},"labels":[],"label_agreement":null},{"id":"W3181437774","doi":"","title":"Automatic Test Suite Generation for Key-Points Detection DNNs using Many-Objective Search (Experience Paper)","year":2021,"lang":"en","type":"preprint","venue":"Open Repository and Bibliography (University of Luxembourg)","topic":"Autonomous Vehicle Technology and Safety","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 Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation of Korea; Fonds National de la Recherche Luxembourg; National Research Foundation; European Commission","keywords":"Computer science; Key (lock); Artificial intelligence; Suite; Machine learning; Test suite; Data mining; Pattern recognition (psychology); Test case","score_opus":0.02348353678583482,"score_gpt":0.23864949312219247,"score_spread":0.21516595633635766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181437774","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":[0.93791145,0.0005347142,0.059467953,0.000026769292,0.000303491,0.0009250129,0.00004138687,0.00019670402,0.00059251505],"genre_scores_gemma":[0.9898495,0.0009063791,0.009059066,0.000009523605,0.00006061869,0.000011998924,0.000031759457,0.000024712797,0.00004643277],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987275,0.00008798916,0.00024065588,0.0005238467,0.00016066516,0.00025934065],"domain_scores_gemma":[0.9990811,0.00009844256,0.00015806798,0.00039705084,0.00017552616,0.00008982724],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030788142,0.00025501905,0.00044572633,0.0033085584,0.0007117637,0.00022200956,0.00049109466,0.00051121105,0.00003182972],"category_scores_gemma":[0.0000027723288,0.00033097732,0.00023176722,0.0027530624,0.00024337064,0.0006927234,0.00061718735,0.00045052503,5.167271e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021740854,0.000701273,0.15492727,0.0043891235,0.0027733392,0.0003915704,0.017491488,0.016099695,0.76517093,0.00052209,0.00051717216,0.036798622],"study_design_scores_gemma":[0.0015302703,0.00034153904,0.10973956,0.00066820957,0.000566944,0.00017756323,0.010095962,0.76458627,0.11026681,0.00038491967,0.0003369019,0.0013050578],"about_ca_topic_score_codex":0.00063788396,"about_ca_topic_score_gemma":0.000109346875,"teacher_disagreement_score":0.7484866,"about_ca_system_score_codex":0.00006070929,"about_ca_system_score_gemma":0.00007071336,"threshold_uncertainty_score":0.9999142},"labels":[],"label_agreement":null},{"id":"W3186212639","doi":"10.1145/3449356","title":"Transfer Reinforcement Learning for Autonomous Driving","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Modeling and Computer Simulation","topic":"Autonomous Vehicle Technology and Safety","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":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Fidelity; Computer science; Domain (mathematical analysis); Perception; Transfer of learning; Reinforcement; Artificial intelligence; Mathematics; Engineering","score_opus":0.016745849241885002,"score_gpt":0.23099925646590616,"score_spread":0.21425340722402117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186212639","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":[0.1257003,0.00005138317,0.8734002,0.00009929042,0.00013723571,0.00009621085,7.5827694e-7,0.00047746772,0.000037165693],"genre_scores_gemma":[0.98469096,0.000048829435,0.015058984,0.00005643415,0.000037318456,0.000020370635,0.0000132736395,0.000020921036,0.000052912015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941593,0.000012132508,0.00018844163,0.00017888322,0.000053894473,0.00015069632],"domain_scores_gemma":[0.9996494,0.00009672463,0.0000070414508,0.00016962684,0.000044898305,0.00003232232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006802341,0.000113102564,0.00011839714,0.00006913355,0.00023715915,0.000027127186,0.000058236143,0.00011432499,0.000010269553],"category_scores_gemma":[0.0000034247448,0.00013090706,0.00005913738,0.00007157875,0.000009518678,0.00013187397,0.0000029546343,0.00020105943,0.0000027036176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006257529,0.000009367008,0.0000056154977,0.000016549353,0.00003061629,6.0361447e-7,0.00017956102,0.8534689,0.00011185028,0.00019359085,4.658226e-7,0.1459766],"study_design_scores_gemma":[0.0003986074,0.000055875917,0.00001723192,0.000025066882,0.00002556404,0.0000034866075,0.00002656191,0.9971554,0.0010226861,0.0008473362,0.0002818439,0.00014033995],"about_ca_topic_score_codex":0.0000014600097,"about_ca_topic_score_gemma":0.000003133395,"teacher_disagreement_score":0.85899067,"about_ca_system_score_codex":0.000036013116,"about_ca_system_score_gemma":0.000012691361,"threshold_uncertainty_score":0.53382355},"labels":[],"label_agreement":null},{"id":"W3186498128","doi":"10.1109/tvt.2021.3097589","title":"A Novel Method for Approximating Object Location Error in Bounding Box Detection Algorithms Using a Monocular Camera","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Autonomous Vehicle Technology and Safety","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 Windsor; McMaster University; Canadian Association of Gastroenterology","funders":"","keywords":"Pixel; Computer vision; Artificial intelligence; Computer science; Range (aeronautics); Minimum bounding box; Monocular vision; Monocular; Algorithm; Image (mathematics); Engineering","score_opus":0.01941502966975609,"score_gpt":0.27264181962028544,"score_spread":0.25322678995052933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186498128","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":[0.11789974,0.0002886281,0.8796003,0.0001905297,0.00034627895,0.00048134947,0.000013472237,0.0011640486,0.000015641512],"genre_scores_gemma":[0.67254466,0.00001809777,0.32703012,0.000025585716,0.000016571923,0.00029483522,0.00000391366,0.000055302276,0.000010903545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983342,0.000045343677,0.0005100442,0.00049637357,0.00012035388,0.0004936698],"domain_scores_gemma":[0.99921954,0.00008701911,0.000082198414,0.00044845956,0.00012271099,0.000040075636],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038445432,0.00028685742,0.00041128526,0.00085756404,0.000306144,0.00002557975,0.00018085638,0.00081889663,0.0000072182647],"category_scores_gemma":[0.000039199338,0.00035248688,0.00014316312,0.0016301489,0.0000872427,0.00015973957,0.0000039037154,0.00084472756,0.0000060260577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012545872,0.00009664019,0.000008928468,0.00008000154,0.00009690195,0.000016347532,0.00011244516,0.45561188,0.41798615,0.00016150856,3.7494698e-7,0.12581624],"study_design_scores_gemma":[0.0005232632,0.000040868534,0.00000751615,0.00005283014,0.000047860533,0.00022812781,0.00034601442,0.5583864,0.43957302,0.0005133134,0.00008531341,0.00019550027],"about_ca_topic_score_codex":0.000047790374,"about_ca_topic_score_gemma":0.00027334268,"teacher_disagreement_score":0.55464494,"about_ca_system_score_codex":0.0005039008,"about_ca_system_score_gemma":0.0000841848,"threshold_uncertainty_score":0.9998927},"labels":[],"label_agreement":null},{"id":"W3189946487","doi":"10.1155/2021/7544355","title":"RS-Lane: A Robust Lane Detection Method Based on ResNeSt and Self-Attention Distillation for Challenging Traffic Situations","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Guangzhou Municipal Science and Technology Project; Guangzhou Science and Technology Program key projects; Shenzhen Fundamental Research Program; Natural Science Foundation of Guangdong Province","keywords":"Computer science; Artificial intelligence; Segmentation; Feature (linguistics); Computer vision; Shadow (psychology); Inference; Representation (politics); Line (geometry); Advanced driver assistance systems; Feature extraction; Pattern recognition (psychology); Real-time computing; Mathematics","score_opus":0.007753830313121349,"score_gpt":0.2324021715527679,"score_spread":0.22464834123964655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189946487","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":[0.3875418,0.00012560931,0.6117491,0.0001986569,0.00015221289,0.00010227403,0.000009426498,0.00010438897,0.000016526186],"genre_scores_gemma":[0.9215261,0.00017833167,0.07813678,0.000010908785,0.000053709668,0.0000102285085,0.00006107115,0.000018531864,0.0000043870514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993068,0.000023016079,0.00034197027,0.00011292549,0.00010823769,0.00010706805],"domain_scores_gemma":[0.9994898,0.00012450315,0.00013882102,0.000072521514,0.0001386584,0.000035726713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020325917,0.00010195793,0.00016324416,0.00015136725,0.00010423679,0.000010099912,0.000029921195,0.00010906916,0.0000038149647],"category_scores_gemma":[0.00002597989,0.0001080597,0.000070663504,0.00015629113,0.000008726994,0.00020393128,4.7446153e-7,0.00017667124,4.6364184e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006498168,0.000042880998,0.00009750038,0.00008307873,0.000030347524,0.000008678871,0.0002354946,0.93027997,0.008019377,0.00021538688,0.0000015552256,0.060920764],"study_design_scores_gemma":[0.0022744678,0.00028925695,0.16304314,0.00014248244,0.00020427846,0.000041599043,0.0005439208,0.8248772,0.007044983,0.00081054727,0.00050586753,0.00022223222],"about_ca_topic_score_codex":3.5775176e-7,"about_ca_topic_score_gemma":0.00007323835,"teacher_disagreement_score":0.53398424,"about_ca_system_score_codex":0.000069774615,"about_ca_system_score_gemma":0.000021467278,"threshold_uncertainty_score":0.44065467},"labels":[],"label_agreement":null},{"id":"W3191463346","doi":"10.1155/2021/9940126","title":"At the Traffic Intersection, Stopping, or Walking? Pedestrian Path Prediction Based on KPOF-GPDM for Driving Assistance","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pedestrian; Advanced driver assistance systems; Optical flow; Computer science; Computer vision; Intersection (aeronautics); Artificial intelligence; Pedestrian detection; Trajectory; Key (lock); Hotspot (geology); Simulation; Real-time computing; Engineering; Image (mathematics); Transport engineering; Computer security","score_opus":0.0065783638727779205,"score_gpt":0.2189207539255832,"score_spread":0.21234239005280528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191463346","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":[0.8384558,0.00024247877,0.15975985,0.0002990315,0.0008791056,0.0001519489,0.000024065976,0.00012359378,0.000064142805],"genre_scores_gemma":[0.9966346,0.000109320856,0.00296392,0.000043555992,0.00009673557,0.000015837148,0.000030622992,0.000023028646,0.00008235735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991303,0.000020734326,0.00043455994,0.000120418765,0.00015313266,0.00014083527],"domain_scores_gemma":[0.9993838,0.0001348856,0.00017797177,0.00013428267,0.00013420453,0.00003484659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021116166,0.00011574521,0.00017418494,0.00007770764,0.00015797123,0.0000114524455,0.000081363854,0.00009792547,0.000030374542],"category_scores_gemma":[0.000053569136,0.00008912862,0.00014995733,0.00018040703,0.000027364396,0.00019624048,8.8888675e-7,0.00026692206,7.9206296e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032005194,0.00003915817,0.00065371895,0.00006059531,0.000059680337,0.00001975533,0.0005642459,0.96187216,0.003655724,0.00006292169,0.00015633253,0.032535672],"study_design_scores_gemma":[0.012310394,0.0026404415,0.55825764,0.001819442,0.0007772237,0.00020075768,0.005916207,0.26187745,0.059698477,0.0009591055,0.09455273,0.0009901087],"about_ca_topic_score_codex":2.7839246e-7,"about_ca_topic_score_gemma":0.00025789152,"teacher_disagreement_score":0.6999947,"about_ca_system_score_codex":0.00020669821,"about_ca_system_score_gemma":0.00007154264,"threshold_uncertainty_score":0.36345598},"labels":[],"label_agreement":null},{"id":"W3191907322","doi":"10.1109/iccv48922.2021.01531","title":"Bifold and Semantic Reasoning for Pedestrian Behavior Prediction","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Pedestrian; Artificial intelligence; Modalities; Machine learning; Benchmark (surveying); Categorical variable; Trajectory; Representation (politics); Encoding (memory); Decoding methods; Parsing; Human–computer interaction","score_opus":0.02191209137745254,"score_gpt":0.26964996012751496,"score_spread":0.24773786875006243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191907322","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":[0.6803196,0.00014746368,0.30880815,0.0019386527,0.0043341825,0.00043737964,0.00016925673,0.00058941985,0.0032559023],"genre_scores_gemma":[0.99224293,0.000237096,0.0063222125,0.00010305466,0.0003441619,0.00005844114,0.0001218684,0.00002656322,0.0005436994],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988861,0.000026691161,0.00030340665,0.00038292998,0.00019411168,0.00020672966],"domain_scores_gemma":[0.9993626,0.000079160345,0.000054954304,0.00022216968,0.00020731549,0.00007379993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013650421,0.00019296912,0.00020330373,0.00014230605,0.00011685411,0.0001247461,0.00020243957,0.00018753731,0.00030183056],"category_scores_gemma":[0.000020539805,0.00020579866,0.000077273675,0.00010412058,0.00003828004,0.00016857755,0.00007437328,0.00026715352,0.000048140828],"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":[0.00024008288,0.0006631567,0.009706334,0.0002302772,0.00060669315,0.00042706743,0.0006456542,0.010853519,0.051541407,0.10666267,0.018742898,0.79968023],"study_design_scores_gemma":[0.00096146995,0.00023924871,0.012660005,0.0002658742,0.000040153,0.00010735377,0.000055718006,0.97075886,0.007559524,0.0010242008,0.006026237,0.00030135107],"about_ca_topic_score_codex":0.0000063929183,"about_ca_topic_score_gemma":0.000020000149,"teacher_disagreement_score":0.9599053,"about_ca_system_score_codex":0.00009146622,"about_ca_system_score_gemma":0.00005078973,"threshold_uncertainty_score":0.8392226},"labels":[],"label_agreement":null},{"id":"W3192815172","doi":"10.1109/iwcmc51323.2021.9498581","title":"Integration of Motion Prediction with End-to-end Latent RL for Self-Driving Vehicles","year":2021,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"End-to-end principle; Computer science; Reinforcement learning; Motion (physics); Artificial intelligence; Sensor fusion; Lidar; Artificial neural network; Simulation","score_opus":0.007642790252821269,"score_gpt":0.19447147269900192,"score_spread":0.18682868244618064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192815172","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":[0.58301616,0.000023512353,0.4153747,0.00016200553,0.0000678101,0.00013603277,0.000007750343,0.00056801277,0.0006439962],"genre_scores_gemma":[0.97461736,0.00001853742,0.025138319,0.000011175462,0.000020400736,0.000031548065,0.000028926239,0.000014300322,0.00011944886],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953914,0.000007994281,0.00015915262,0.0001192328,0.00006298894,0.0001114726],"domain_scores_gemma":[0.99972737,0.000027697331,0.000020866142,0.0001255304,0.00007424977,0.000024271609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000086657914,0.00007752288,0.00010447485,0.00006322888,0.00004004721,0.000007322321,0.000042717522,0.00009783652,0.000037672933],"category_scores_gemma":[0.0000130240005,0.00006792728,0.000028734054,0.00014057024,0.000012768106,0.0001068645,0.000012856697,0.00008337173,0.0000045919733],"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":[0.000059259706,0.00020903962,0.02556423,0.0003000281,0.00035554852,0.0000068294958,0.001601964,0.105224945,0.6022268,0.027426573,0.00073736033,0.23628742],"study_design_scores_gemma":[0.00039551334,0.0001621464,0.06911114,0.0000635542,0.000047028258,0.000013379573,0.00023221163,0.33258525,0.59607124,0.00046192342,0.0007215989,0.00013500307],"about_ca_topic_score_codex":0.0000055136575,"about_ca_topic_score_gemma":0.00008490632,"teacher_disagreement_score":0.39160118,"about_ca_system_score_codex":0.000058195903,"about_ca_system_score_gemma":0.000015486074,"threshold_uncertainty_score":0.2769994},"labels":[],"label_agreement":null},{"id":"W3194037986","doi":"10.1109/tits.2021.3102479","title":"LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic Vision Sensor","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Korea Creative Content Agency; National Research Foundation of Korea; Ministry of Culture, Sports and Tourism","keywords":"Artificial intelligence; Computer science; Computer vision; RGB color model; Frame (networking); Feature extraction; Advanced driver assistance systems; Encoder; Object detection; Segmentation","score_opus":0.015558495452928199,"score_gpt":0.23970431264002356,"score_spread":0.22414581718709536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194037986","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":[0.18249558,0.00017155068,0.8145105,0.000017892202,0.0013075925,0.00038439024,0.00010336723,0.00072661793,0.00028250364],"genre_scores_gemma":[0.9968061,0.000117204014,0.002552735,0.000022557399,0.000036822075,0.000092211616,0.00005258164,0.000075595824,0.0002442305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819046,0.00008628463,0.00064758747,0.00044203264,0.00029173456,0.00034188523],"domain_scores_gemma":[0.99928546,0.00007223267,0.00006513655,0.00035085474,0.00010485298,0.00012147922],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002429672,0.00029779045,0.0003331159,0.000377003,0.00023926745,0.000051473402,0.00012012189,0.00031968698,0.000093689734],"category_scores_gemma":[0.0000019052469,0.000337108,0.00016605061,0.0006588159,0.000038887792,0.00016013732,3.3589947e-7,0.00045830623,0.000086367996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042976004,0.000094669485,0.000024052193,0.00017375666,0.0001348692,0.000027868411,0.00073012075,0.90119755,0.06316475,0.000061020706,0.0000030405515,0.034345314],"study_design_scores_gemma":[0.00037152317,0.000099300196,0.0006166471,0.00020901236,0.00015525623,0.00013642783,0.001916281,0.7772832,0.21651803,0.000016968277,0.002104024,0.0005733591],"about_ca_topic_score_codex":0.0000992989,"about_ca_topic_score_gemma":0.00039223183,"teacher_disagreement_score":0.8143105,"about_ca_system_score_codex":0.00031007914,"about_ca_system_score_gemma":0.000037957074,"threshold_uncertainty_score":0.9999081},"labels":[],"label_agreement":null},{"id":"W3194149012","doi":"10.1109/tits.2021.3102432","title":"Hybrid Autonomous Driving Guidance Strategy Combining Deep Reinforcement Learning and Expert System","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Reinforcement learning; Artificial intelligence; Computer science; Engineering; Control engineering; Machine learning","score_opus":0.012372755036931884,"score_gpt":0.22770396638447862,"score_spread":0.21533121134754674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194149012","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":[0.08228674,0.0014049787,0.9126745,0.000017685425,0.0010485058,0.00028861253,0.000008709395,0.0013093842,0.00096091797],"genre_scores_gemma":[0.99842507,0.00060166005,0.00024251794,0.000012829624,0.000033505687,0.00017406716,0.000031206462,0.00006002801,0.0004190965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816614,0.000065430664,0.00080606795,0.0003811741,0.00022948644,0.0003516709],"domain_scores_gemma":[0.999344,0.000090945505,0.00009625308,0.00023978524,0.00010800522,0.000121048775],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019596012,0.0003050271,0.00037505728,0.0001705426,0.0003244769,0.00007364894,0.000111892936,0.0001812342,0.0000642576],"category_scores_gemma":[0.000001904264,0.00035127977,0.00011215882,0.00019856878,0.000056864916,0.00019200628,4.705556e-7,0.0004877441,0.000046007568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012261803,0.000028245027,0.000113121794,0.00023324558,0.00014981804,0.00007170189,0.0010215966,0.9841881,0.0024758456,0.0014421406,0.000009961904,0.010253966],"study_design_scores_gemma":[0.00051905174,0.0001394249,0.00023383259,0.0005665561,0.00007787721,0.00013698758,0.00713778,0.84898615,0.13913469,0.000012921999,0.002468856,0.00058586773],"about_ca_topic_score_codex":0.000053129195,"about_ca_topic_score_gemma":0.00007184532,"teacher_disagreement_score":0.91613835,"about_ca_system_score_codex":0.0002576314,"about_ca_system_score_gemma":0.000046057587,"threshold_uncertainty_score":0.9998939},"labels":[],"label_agreement":null},{"id":"W3194318374","doi":"10.17762/ijnpme.v8i04.79","title":"Neural Network Based Hurdle Avoidance System for Smart Vehicles","year":2019,"lang":"en","type":"article","venue":"International Journal of New Practices in Management and Engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Intuitive","keywords":"Sigmoid function; Controller (irrigation); Artificial neural network; Computer science; Control theory (sociology); Piecewise; MATLAB; Position (finance); Real-time computing; Control engineering; Simulation; Artificial intelligence; Engineering; Control (management); Mathematics","score_opus":0.01219658914830761,"score_gpt":0.24140435763995963,"score_spread":0.22920776849165203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194318374","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":[0.9191532,0.0019321619,0.072123654,0.0008858477,0.0036917618,0.0002946461,0.0000029735572,0.00018218833,0.0017335282],"genre_scores_gemma":[0.98807716,0.00011483098,0.011494581,0.000026925673,0.0001515887,0.0000043258674,0.000001422676,0.000015293153,0.000113868526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993769,0.0000068776135,0.0002712718,0.00008025899,0.00012662774,0.00013804578],"domain_scores_gemma":[0.9995762,0.00013221847,0.00016585473,0.00006947217,0.000030717383,0.000025534879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003264734,0.00009176359,0.00013283755,0.000169541,0.000013259272,0.00004146496,0.00023169602,0.000049218772,0.0000065114446],"category_scores_gemma":[0.000023574443,0.0000939611,0.000040782114,0.000072580944,0.00000584141,0.00036867007,0.000032063508,0.00018918791,0.0000035973392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007688717,0.00000915357,0.007777886,0.00019194845,0.00016469817,0.000041908443,0.000036718404,0.97569346,0.00036729264,0.0022431312,0.00030651595,0.013090375],"study_design_scores_gemma":[0.0012490964,0.000047004352,0.017730583,0.0003003067,0.000030237587,0.000041820338,0.000118092685,0.9495394,0.00025092255,0.00013868256,0.030412331,0.00014149712],"about_ca_topic_score_codex":0.000003060851,"about_ca_topic_score_gemma":0.0000017232112,"teacher_disagreement_score":0.06892393,"about_ca_system_score_codex":0.00007795176,"about_ca_system_score_gemma":0.0000066265975,"threshold_uncertainty_score":0.38316223},"labels":[],"label_agreement":null},{"id":"W3195565956","doi":"10.1155/2021/4438861","title":"Development of AI-Based Vehicle Detection and Tracking System for C-ITS Application","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Land, Infrastructure and Transport","keywords":"Intersection (aeronautics); Queue; Computer science; Vehicle tracking system; Artificial intelligence; Trajectory; Computer vision; Volume (thermodynamics); Image processing; Real-time computing; Image (mathematics); Engineering; Transport engineering; Kalman filter","score_opus":0.0066128772606449185,"score_gpt":0.21923718170698744,"score_spread":0.21262430444634253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195565956","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":[0.55720454,0.00017919767,0.44245735,0.00001537634,0.000053243333,0.00005909795,0.0000022345146,0.000026454501,0.0000025054921],"genre_scores_gemma":[0.9683653,0.000009478269,0.031579208,0.0000043029186,0.000013713152,0.000010697719,0.000007015703,0.0000096085205,6.5546305e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99943304,0.000004327887,0.00037632545,0.000057063688,0.000066870554,0.00006237723],"domain_scores_gemma":[0.9995969,0.000023042576,0.0001360921,0.00004032596,0.00018371311,0.000019904339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000105630665,0.000055127508,0.00012869645,0.000059608454,0.000049293278,0.0000033601636,0.000027444701,0.00006375752,5.7061544e-7],"category_scores_gemma":[0.0000055989126,0.00005911709,0.0000344342,0.00009239492,0.0000096604435,0.00014535245,3.6637988e-7,0.00009055412,1.2609935e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005560266,0.000013487509,0.00024858292,0.00032945984,0.000023796996,0.0000019245263,0.00037575897,0.16845824,0.695917,0.00031750806,1.1022567e-7,0.13425851],"study_design_scores_gemma":[0.00073440216,0.000032143664,0.031419355,0.000082212995,0.000030259438,0.000005567273,0.00037454406,0.015633976,0.951209,0.0000728452,0.00034666475,0.000059000897],"about_ca_topic_score_codex":9.8187506e-8,"about_ca_topic_score_gemma":0.000028699938,"teacher_disagreement_score":0.41116077,"about_ca_system_score_codex":0.000051680698,"about_ca_system_score_gemma":0.000048736067,"threshold_uncertainty_score":0.2410725},"labels":[],"label_agreement":null},{"id":"W3196361212","doi":"10.1109/lcn52139.2021.9524995","title":"Toward Driver Intention Prediction for Intelligent Vehicles: A Deep Learning Approach","year":2021,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"University of Ottawa","funders":"Canada Research Chairs","keywords":"Computer science; Focus (optics); Situation awareness; Trajectory; Inference; Deep learning; Transfer of learning; Artificial intelligence; Range (aeronautics); Convolutional neural network; Intelligent transportation system; Real-time computing; Human–computer interaction; Machine learning; Transport engineering; Engineering","score_opus":0.01723020214243095,"score_gpt":0.20966720437779693,"score_spread":0.19243700223536597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196361212","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":[0.08001758,0.00024263954,0.91142327,0.00005704992,0.00017775912,0.00014628042,0.0000021650492,0.0011785991,0.006754686],"genre_scores_gemma":[0.9869353,0.00015508206,0.011799931,0.000024883177,0.000037944774,0.000063660256,0.000061662075,0.00002182731,0.0008996849],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999399,0.000014439457,0.00017577998,0.00017299998,0.00005662141,0.00018115371],"domain_scores_gemma":[0.99975616,0.000023047405,0.000016125156,0.000113855065,0.0000593441,0.000031493066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011136027,0.000096905285,0.00011513189,0.000054143966,0.00007346208,0.000014883806,0.000070105445,0.0001680352,0.00006888211],"category_scores_gemma":[0.00003179084,0.00009961906,0.0000788202,0.00011595498,0.00002731867,0.00008873166,0.00003210623,0.00022787508,0.000030424364],"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":[0.00008142582,0.00026207732,0.014529552,0.00059772335,0.00056142436,0.00001709522,0.00386372,0.28942028,0.01855508,0.050571993,0.0012787643,0.62026083],"study_design_scores_gemma":[0.00031959213,0.000058553553,0.0024110712,0.000014281417,0.000032806005,0.000027644439,0.0019376932,0.9596714,0.023681996,0.0012533874,0.010427567,0.00016405154],"about_ca_topic_score_codex":0.0000018029127,"about_ca_topic_score_gemma":0.0000042091597,"teacher_disagreement_score":0.90691775,"about_ca_system_score_codex":0.000077718614,"about_ca_system_score_gemma":0.000009175858,"threshold_uncertainty_score":0.4062347},"labels":[],"label_agreement":null},{"id":"W3200750070","doi":"10.1109/icra46639.2022.9812041","title":"I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Robotics and Automation (ICRA)","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":"University of Waterloo","funders":"","keywords":"Computer science; Occlusion; Situational ethics; Situation awareness; Distraction; Artificial intelligence; Engineering; Psychology","score_opus":0.02763513750133222,"score_gpt":0.2618905035573005,"score_spread":0.23425536605596825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200750070","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":[0.93779314,0.00015453431,0.05454872,0.0025601175,0.0011563454,0.00057276187,0.0011708359,0.00054822996,0.0014952996],"genre_scores_gemma":[0.99718016,0.00010708116,0.0018625903,0.00004480347,0.000021885631,0.000030112356,0.0005189459,0.00002512246,0.00020927959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998902,0.000038113514,0.00039719994,0.00023574173,0.00025284087,0.00017406959],"domain_scores_gemma":[0.9994491,0.00006933352,0.00016484968,0.00015007691,0.00012739381,0.000039265862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021777327,0.00017479516,0.0002184872,0.00023301168,0.00028070025,0.000038140086,0.00024851033,0.00009906108,0.00025169837],"category_scores_gemma":[0.000015365546,0.0001971787,0.000060534712,0.00013148783,0.000060556093,0.00010249091,0.00010595205,0.00023122867,0.0000023087007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003932582,0.000049896724,0.0003153668,0.000045848486,0.000093991395,0.0000033269341,0.0002375772,0.9210749,0.009465983,0.06427782,0.000056385048,0.004339574],"study_design_scores_gemma":[0.0004562579,0.00010186243,0.0007842271,0.000031793046,0.000026862415,0.000015300157,0.0010652974,0.9900654,0.0017596363,0.005325631,0.00016559969,0.0002021366],"about_ca_topic_score_codex":0.000016398275,"about_ca_topic_score_gemma":0.00001451987,"teacher_disagreement_score":0.06899049,"about_ca_system_score_codex":0.00028491847,"about_ca_system_score_gemma":0.00012484792,"threshold_uncertainty_score":0.80407137},"labels":[],"label_agreement":null},{"id":"W3201284493","doi":"10.1155/2021/6251399","title":"Multitarget Vehicle Tracking and Motion State Estimation Using a Novel Driving Environment Perception System of Intelligent Vehicles","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Segmentation; Computer vision; Artificial neural network; Kalman filter; Vehicle tracking system; Motion (physics); Intelligent transportation system; Heading (navigation); Tracking (education); Perception; Feature (linguistics); Engineering","score_opus":0.009454884950537598,"score_gpt":0.2173810687479426,"score_spread":0.20792618379740502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201284493","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":[0.5739218,0.00017206991,0.42575815,0.000008808364,0.00006573614,0.00004331157,0.0000046747155,0.000023906994,0.00000154526],"genre_scores_gemma":[0.9288928,0.0002491262,0.070816755,0.0000018068042,0.000011953771,0.000001125791,0.000010569166,0.000014838491,0.0000010088992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991218,0.000015003057,0.000534001,0.00009047764,0.0001395723,0.00009918872],"domain_scores_gemma":[0.9995649,0.000026655754,0.00022810987,0.00006521021,0.00008095848,0.00003415626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001617626,0.00009543997,0.00019147818,0.00009805991,0.000047140482,0.0000071479326,0.00003596232,0.0000695124,0.0000049617784],"category_scores_gemma":[0.000009049504,0.00010295751,0.000054113796,0.00007995584,0.000028136305,0.00042045175,0.0000020256678,0.00015554288,4.2185437e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016276235,0.00002308201,0.0021725134,0.00010398024,0.000022060918,0.000005829935,0.0010611358,0.5988067,0.36660132,0.00004578727,5.496347e-8,0.031141298],"study_design_scores_gemma":[0.0007515604,0.00006881273,0.3974051,0.0003612605,0.00008677891,0.000054643046,0.002830769,0.4667899,0.13131708,0.00018780731,0.000010982658,0.00013529226],"about_ca_topic_score_codex":0.0000024064768,"about_ca_topic_score_gemma":0.00000829798,"teacher_disagreement_score":0.3952326,"about_ca_system_score_codex":0.00014864437,"about_ca_system_score_gemma":0.00001446071,"threshold_uncertainty_score":0.41984853},"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":"W3201917469","doi":"10.1109/iccv48922.2021.00753","title":"Where are you heading? Dynamic Trajectory Prediction with Expert Goal Examples","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Inference; Trajectory; Machine learning; Artificial intelligence; Key (lock); Code (set theory)","score_opus":0.014594022911133712,"score_gpt":0.24712295474667284,"score_spread":0.23252893183553913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201917469","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":[0.566689,0.0008002538,0.40710583,0.005954916,0.006477054,0.00041715044,0.00025951106,0.0016994003,0.010596845],"genre_scores_gemma":[0.9928981,0.0006593517,0.004740049,0.0002243575,0.00033578236,0.00003527425,0.00013031895,0.00004683826,0.0009299132],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983451,0.000057572364,0.00036483404,0.0005445269,0.0003950431,0.00029292342],"domain_scores_gemma":[0.9990929,0.00006689963,0.00008729608,0.00038298208,0.00027171875,0.00009821751],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000108990476,0.00031317055,0.00029625362,0.0001853591,0.00012558978,0.00013667248,0.00039360236,0.00024128058,0.00115879],"category_scores_gemma":[0.0000069445723,0.00029913554,0.000098219556,0.00017717291,0.00007716074,0.00027547948,0.00007916955,0.00051195873,0.00015536204],"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":[0.00089980825,0.0017412718,0.010943678,0.00037227126,0.002266982,0.0022548744,0.0032620132,0.15135859,0.116610296,0.032728136,0.08874165,0.58882046],"study_design_scores_gemma":[0.0010967441,0.00035641727,0.0138444,0.0008086986,0.000017963841,0.0001755643,0.0003277181,0.9552458,0.0055856477,0.00038411876,0.021604512,0.00055242225],"about_ca_topic_score_codex":0.0000129901155,"about_ca_topic_score_gemma":0.00020240966,"teacher_disagreement_score":0.8038872,"about_ca_system_score_codex":0.00029262435,"about_ca_system_score_gemma":0.000089108464,"threshold_uncertainty_score":0.99994606},"labels":[],"label_agreement":null},{"id":"W3202331112","doi":"10.1016/j.sysarc.2021.102292","title":"The price of schedulability in cyclic workloads: The history-vs.-response-time-vs.-accuracy trade-off","year":2021,"lang":"en","type":"article","venue":"Journal of Systems Architecture","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Army Research Office; Division of Computer and Network Systems; Office of Naval Research; General Motors of Canada; General Motors Corporation; National Science Foundation","keywords":"Computer science; Response time; Bounded function; Graph; Task (project management); Dilemma; Real-time computing; Algorithm; Parallel computing; Theoretical computer science; Operating system","score_opus":0.005533726568369445,"score_gpt":0.1984947860293631,"score_spread":0.19296105946099365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202331112","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":[0.9558214,0.036220662,0.0014488634,0.004892703,0.0010168455,0.0002486692,0.000005681796,0.00006882432,0.00027635504],"genre_scores_gemma":[0.9990801,0.00032060922,0.00020663942,0.00004466652,0.00013736963,0.000008965308,3.7864876e-7,0.000026364523,0.00017485576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977291,0.0005767263,0.0009453656,0.00014145735,0.0003007937,0.0003065335],"domain_scores_gemma":[0.9973493,0.0015489701,0.00039425396,0.0005613274,0.000081329985,0.00006481188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002116131,0.00019818167,0.0005004018,0.00013339227,0.00010983155,0.000021497677,0.00060293806,0.00024245563,0.00002655894],"category_scores_gemma":[0.0007707657,0.000116748946,0.00021094373,0.00038123407,0.00021739771,0.00007934334,0.000056885037,0.0014094958,0.000005329449],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005203594,0.0005469152,0.014028912,0.0012596379,0.0016371271,0.0007814986,0.020227106,0.5209269,0.26870385,0.002144824,0.010612378,0.15392724],"study_design_scores_gemma":[0.0029492674,0.00057796703,0.19784376,0.0016788976,0.00017486575,0.004494502,0.003160714,0.02250052,0.012393185,0.0017654407,0.7515733,0.0008875467],"about_ca_topic_score_codex":0.000006626522,"about_ca_topic_score_gemma":0.000023551018,"teacher_disagreement_score":0.74096096,"about_ca_system_score_codex":0.00039799136,"about_ca_system_score_gemma":0.00026293655,"threshold_uncertainty_score":0.61236364},"labels":[],"label_agreement":null},{"id":"W3203124669","doi":"10.1109/iros51168.2021.9636480","title":"Motion Planning for Autonomous Vehicles in the Presence of Uncertainty Using Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Autonomous Vehicle Technology and Safety","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":"Université de Sherbrooke; Huawei Technologies (Canada)","funders":"","keywords":"Reinforcement learning; Computer science; Range (aeronautics); Motion planning; Artificial intelligence; Computation; Field (mathematics); Motion (physics); Machine learning; Mathematical optimization; Algorithm; Engineering; Mathematics; Robot","score_opus":0.06493866033802373,"score_gpt":0.29827904322557747,"score_spread":0.23334038288755374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203124669","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":[0.6782049,0.00075388403,0.31447902,0.00028591347,0.001469585,0.00064002816,0.000023312732,0.00009054382,0.0040528215],"genre_scores_gemma":[0.9991484,0.00019857725,0.00014568621,0.00002654007,0.00008366517,0.000060155275,0.000032719545,0.000013671148,0.0002905624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987073,0.00007692297,0.0005165964,0.00024961247,0.00023574966,0.0002138197],"domain_scores_gemma":[0.9993083,0.00017924394,0.00013240759,0.00017596698,0.00017379565,0.000030320793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045765302,0.00017008047,0.0002505562,0.00013289484,0.00010235623,0.000080195554,0.00024824013,0.00013700392,0.000042521246],"category_scores_gemma":[0.00007745536,0.00014467434,0.00006381122,0.000117846466,0.00006835122,0.00010089687,0.000038919934,0.0003052367,0.0000037546292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023658175,0.000027225571,0.0013530493,0.0000799918,0.000058951802,0.000012290711,0.0011069304,0.9509395,0.0059467116,0.03632265,0.000029727391,0.0040993155],"study_design_scores_gemma":[0.00021587705,0.00006380489,0.00046927662,0.00038548256,0.000012391101,0.000024260655,0.002909354,0.9895965,0.0049998984,0.0004019893,0.0007727198,0.00014847031],"about_ca_topic_score_codex":0.000108960376,"about_ca_topic_score_gemma":0.000021004145,"teacher_disagreement_score":0.32094353,"about_ca_system_score_codex":0.00012818213,"about_ca_system_score_gemma":0.000055475644,"threshold_uncertainty_score":0.5899648},"labels":[],"label_agreement":null},{"id":"W3203848864","doi":"10.1109/iccv48922.2021.00375","title":"CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":318,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Benchmark (surveying); Convolution (computer science); Pipeline (software); Code (set theory); Line (geometry); Network topology; Process (computing); Source lines of code; Artificial intelligence; Algorithm; Pattern recognition (psychology); Data mining; Mathematics; Artificial neural network; Software; Programming language","score_opus":0.01336336142485659,"score_gpt":0.25839949022288133,"score_spread":0.24503612879802475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203848864","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":[0.098539256,0.000023684996,0.88457125,0.0046051363,0.004084326,0.00026002288,0.00018875719,0.0005448526,0.0071827206],"genre_scores_gemma":[0.9909519,0.000026292892,0.0060073645,0.0017208174,0.0005164338,0.000044298093,0.00032555964,0.000032348915,0.0003750143],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981961,0.000082082326,0.00039623815,0.00054162997,0.00048359114,0.0003003282],"domain_scores_gemma":[0.9988163,0.00022459283,0.00007703962,0.00039914213,0.0003447073,0.00013823407],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00016494449,0.00030045217,0.0002780273,0.00030688115,0.00014057069,0.00011946407,0.00035488582,0.0003458418,0.0030829562],"category_scores_gemma":[0.000046996327,0.000322189,0.00012026853,0.00025525386,0.000057081354,0.000122050784,0.00006676557,0.00065631897,0.0013019758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068443664,0.00088956696,0.0008987621,0.00008642813,0.0004803006,0.00058337394,0.00028764972,0.52803785,0.04735731,0.19761203,0.020782009,0.20230027],"study_design_scores_gemma":[0.0007268324,0.00041596612,0.0058281035,0.00027096062,0.000012454611,0.00004227212,0.000019420126,0.9553571,0.020760423,0.006203925,0.009935742,0.0004268531],"about_ca_topic_score_codex":0.000009843381,"about_ca_topic_score_gemma":0.000030027293,"teacher_disagreement_score":0.8924126,"about_ca_system_score_codex":0.0003125465,"about_ca_system_score_gemma":0.00010023656,"threshold_uncertainty_score":0.999923},"labels":[],"label_agreement":null},{"id":"W3204619263","doi":"10.1155/2021/5549111","title":"Applications of Deep Learning Techniques for Pedestrian Detection in Smart Environments: A Comprehensive Study","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pedestrian; Identification (biology); Field (mathematics); Intelligent transportation system; Computer science; Transport engineering; Deep learning; Smart city; Artificial intelligence; Data science; Human–computer interaction; Computer security; Engineering; Internet of Things","score_opus":0.00576459418424624,"score_gpt":0.23400970952916994,"score_spread":0.2282451153449237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204619263","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":[0.6152173,0.00021414808,0.38427538,0.000009490397,0.000027017337,0.00022164988,0.0000018460706,0.000025037629,0.00000810663],"genre_scores_gemma":[0.98887223,0.0002147429,0.010821325,0.000002512443,0.000012826202,0.00005284824,0.000008918995,0.000010997987,0.0000036281438],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993801,0.000014953264,0.00040263854,0.00006576526,0.00006721952,0.00006929411],"domain_scores_gemma":[0.9996697,0.00004482501,0.00015203068,0.000058372774,0.000059801136,0.0000152979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000066683606,0.00006271356,0.0001649916,0.000114729155,0.000028695766,0.000001854756,0.000042950775,0.000059742593,0.0000030197823],"category_scores_gemma":[0.000008036575,0.000070476555,0.000050128503,0.00015151876,0.000014849031,0.00013299541,8.8719787e-7,0.00019011622,1.9559079e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013572467,0.00026778702,0.01044053,0.00010609762,0.00008615163,0.00001633397,0.0019551655,0.25495264,0.28108665,0.000055769753,2.1909906e-7,0.45089692],"study_design_scores_gemma":[0.002685301,0.00082620594,0.5625364,0.00006850765,0.00012715417,0.000018179657,0.011181434,0.0015384624,0.41356495,0.0013860451,0.0058581247,0.00020923071],"about_ca_topic_score_codex":0.0000014450616,"about_ca_topic_score_gemma":0.00015799035,"teacher_disagreement_score":0.5520959,"about_ca_system_score_codex":0.00004930557,"about_ca_system_score_gemma":0.000009183366,"threshold_uncertainty_score":0.28739506},"labels":[],"label_agreement":null},{"id":"W3205492849","doi":"10.1109/icra48506.2021.9561107","title":"Graph-SIM: A Graph-based Spatiotemporal Interaction Modelling for Pedestrian Action Prediction","year":2021,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Pedestrian; Computer science; Weighting; Cluster analysis; Minimum bounding box; Graph; Bounding overwatch; Benchmark (surveying); Data mining; Machine learning; Artificial intelligence; Theoretical computer science; Transport engineering; Geography","score_opus":0.03743080217815473,"score_gpt":0.24849945382218785,"score_spread":0.21106865164403313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205492849","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":[0.17179665,0.000058765167,0.8251924,0.00020611889,0.00051868346,0.0001690972,0.000016702214,0.0011560054,0.0008855611],"genre_scores_gemma":[0.98840535,0.00006215044,0.011064951,0.000034548262,0.00007923634,0.000063885156,0.000158045,0.000026159434,0.00010565368],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931157,0.000015225715,0.00023086731,0.00019414147,0.000069279195,0.00017889334],"domain_scores_gemma":[0.99963564,0.000046728288,0.000035586112,0.00017917257,0.00006526397,0.000037589092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000987195,0.00012519615,0.00012633007,0.00015536715,0.00011869712,0.000022237075,0.000052001087,0.00021390403,0.000056404588],"category_scores_gemma":[0.000011547677,0.000139285,0.000104661354,0.0002357383,0.000020699717,0.00022852303,0.0000067991873,0.0002053802,0.0000077734485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006083658,0.000052309373,0.0010418141,0.000097165204,0.00006566236,0.0000031552531,0.000050208808,0.9753348,0.0036030796,0.001401408,0.00058418716,0.017705344],"study_design_scores_gemma":[0.0005095175,0.00004615716,0.00025156207,0.000020126849,0.000027821972,0.0000068773143,0.00012674404,0.93792,0.054610487,0.0029357963,0.0034131606,0.00013174956],"about_ca_topic_score_codex":0.000028880295,"about_ca_topic_score_gemma":0.00013191382,"teacher_disagreement_score":0.8166087,"about_ca_system_score_codex":0.0000780897,"about_ca_system_score_gemma":0.00003571868,"threshold_uncertainty_score":0.56798774},"labels":[],"label_agreement":null},{"id":"W3206041734","doi":"10.3390/pr9101786","title":"Efficient Video-based Vehicle Queue Length Estimation using Computer Vision and Deep Learning for an Urban Traffic Scenario","year":2021,"lang":"en","type":"article","venue":"Processes","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Queue; Real-time computing; Computer science; Simulation; Computer vision; Artificial intelligence; Computer network","score_opus":0.01056465997462269,"score_gpt":0.23823155924928063,"score_spread":0.22766689927465794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206041734","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":[0.6594342,0.0012929279,0.3386668,0.00004050154,0.000041815005,0.0000966522,0.0000010177353,0.00041452856,0.000011583527],"genre_scores_gemma":[0.97308904,0.000020120693,0.026761154,0.000028928192,0.00003677867,0.000012717542,0.000020169158,0.000026864363,0.0000042529214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935234,0.000017712751,0.00015435329,0.00021974892,0.00006647898,0.00018935291],"domain_scores_gemma":[0.99963325,0.0000962974,0.000032748354,0.00009895347,0.00009541802,0.000043315777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011396297,0.0001245301,0.00014379024,0.00006212338,0.00020922502,0.000050572013,0.000061101186,0.00011530484,0.0000042839474],"category_scores_gemma":[0.000060559913,0.00012823923,0.000020468187,0.00018323865,0.00003995167,0.000101524514,0.000018269788,0.00014891801,0.0000020951238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013040557,0.000042577118,0.0003112664,0.0005591512,0.000009183835,0.0000027225135,0.00032904575,0.92605823,0.0013566875,0.000024132709,0.000004416793,0.07128953],"study_design_scores_gemma":[0.00037450547,0.00010306103,0.00043430738,0.00007384577,0.000020960426,0.000010345344,0.000058049573,0.9891542,0.009224927,0.00003154721,0.000359665,0.00015456372],"about_ca_topic_score_codex":0.0000018858607,"about_ca_topic_score_gemma":0.00002208713,"teacher_disagreement_score":0.31365484,"about_ca_system_score_codex":0.00004452803,"about_ca_system_score_gemma":0.000060811686,"threshold_uncertainty_score":0.5229444},"labels":[],"label_agreement":null},{"id":"W3206683817","doi":"10.1109/access.2021.3119915","title":"Safe Adaptive Deep Reinforcement Learning for Autonomous Driving in Urban Environments. Additional Filter? How and Where?","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Toyota Motor Corporation","keywords":"Reinforcement learning; Computer science; Context (archaeology); Bounded function; Artificial intelligence; Filter (signal processing); Heuristic; Machine learning; Automotive industry; Deep learning; Mathematics; Engineering; Computer vision","score_opus":0.012361737537294753,"score_gpt":0.21657649529392303,"score_spread":0.20421475775662828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206683817","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":[0.32489407,0.0015269159,0.665193,0.0008112674,0.0005680136,0.0007878419,0.00008686286,0.00061412103,0.0055179014],"genre_scores_gemma":[0.99751824,0.00012423645,0.0011980239,0.000045550933,0.00006201251,0.00015932757,0.00009571834,0.0000267347,0.00077017443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992103,0.000018877916,0.00017787908,0.00023516477,0.00008039899,0.00027737435],"domain_scores_gemma":[0.9996274,0.00013259325,0.000043587166,0.00013892713,0.000011639819,0.000045817615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000769924,0.00015010899,0.00017784665,0.00007717308,0.00010852028,0.00004892806,0.00015080805,0.00014539204,0.0004892569],"category_scores_gemma":[0.000026900447,0.00017668192,0.00003891889,0.000087528046,0.000051234674,0.0003351057,0.0000763963,0.00027500425,0.000009209557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004864345,0.00007268693,0.016095543,0.0001395239,0.0002566629,0.000119143806,0.00096090103,0.89609075,0.0046184734,0.0021970053,0.0072732666,0.072127394],"study_design_scores_gemma":[0.0014520511,0.000106681015,0.0374243,0.00013981592,0.00003461306,0.000030986692,0.00033192954,0.8491428,0.019797698,0.0009429238,0.089940116,0.00065607723],"about_ca_topic_score_codex":0.0000038761027,"about_ca_topic_score_gemma":0.00009808747,"teacher_disagreement_score":0.6726242,"about_ca_system_score_codex":0.00014587767,"about_ca_system_score_gemma":0.000021451475,"threshold_uncertainty_score":0.72048795},"labels":[],"label_agreement":null},{"id":"W3206774503","doi":"10.1109/icra48506.2021.9560790","title":"Prediction-Based Reachability for Collision Avoidance in Autonomous Driving","year":2021,"lang":"en","type":"article","venue":"","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":"Simon Fraser University","funders":"","keywords":"Reachability; Collision avoidance; Computer science; Collision; Distributed computing; Computer security; Algorithm","score_opus":0.010016602239691063,"score_gpt":0.20818649339763784,"score_spread":0.19816989115794678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206774503","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":[0.79247034,0.00014640484,0.20085332,0.0005292689,0.0002481266,0.00027851496,0.00002286539,0.0012448159,0.004206326],"genre_scores_gemma":[0.9900493,0.000008686275,0.00949529,0.000052047217,0.000019964129,0.00008482214,0.000019472704,0.000015851065,0.000254589],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993085,0.000015801319,0.00022895013,0.00020092863,0.000046882324,0.0001989133],"domain_scores_gemma":[0.99948514,0.00016922256,0.0000137998495,0.0002588714,0.000043893775,0.000029073784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021383367,0.000093231385,0.00014483512,0.000053311058,0.00005816465,0.000008227217,0.00008091125,0.00016756651,0.00004601001],"category_scores_gemma":[0.00011875616,0.000101785416,0.00004171211,0.00020763495,0.00003129985,0.000065750144,0.000016890432,0.00016787153,0.000008087333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000417346,0.0003085606,0.18894677,0.0003092519,0.00004929621,0.000032866323,0.00027007333,0.71614563,0.023387866,0.012677087,0.0017556896,0.056075193],"study_design_scores_gemma":[0.0005833919,0.00002898934,0.058316812,0.000029516637,0.0000051177476,0.0000029626494,0.000054698212,0.87149215,0.0612965,0.001254482,0.006789989,0.00014536156],"about_ca_topic_score_codex":0.000009599991,"about_ca_topic_score_gemma":0.00031323932,"teacher_disagreement_score":0.19757892,"about_ca_system_score_codex":0.00017605897,"about_ca_system_score_gemma":0.00008007114,"threshold_uncertainty_score":0.41506886},"labels":[],"label_agreement":null},{"id":"W3206880326","doi":"10.1109/icra48506.2021.9561450","title":"Learning robust driving policies without online exploration","year":2021,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Huawei Technologies (Canada)","funders":"","keywords":"Reinforcement learning; Offline learning; Computer science; Artificial intelligence; Representation (politics); Machine learning; Online learning; Online and offline; Training set; Robot; Scale (ratio)","score_opus":0.01796128547711093,"score_gpt":0.22501420062719749,"score_spread":0.20705291515008656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206880326","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":[0.866243,0.00011649952,0.11746321,0.0007065094,0.000105858475,0.00003528446,7.9872666e-7,0.0020828263,0.013246001],"genre_scores_gemma":[0.99135727,0.000114945506,0.006262571,0.00004771939,0.000052514446,0.0000016061055,0.000022038597,0.000018669634,0.0021226902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995771,0.000012817971,0.00011792433,0.00009480177,0.00004698295,0.00015034621],"domain_scores_gemma":[0.99980307,0.000017996428,0.000011522127,0.00011758226,0.000026443528,0.000023363475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000045848356,0.000078081954,0.00009778882,0.00004282668,0.0000791648,0.0000155364,0.00005356329,0.00009238133,0.00012405796],"category_scores_gemma":[0.000033366265,0.00008060308,0.00002420783,0.00013866198,0.000020711104,0.00016113471,0.000033077955,0.0002314807,0.000048097918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018559602,0.00004167946,0.038509943,0.000027806695,0.00006204143,0.000021477237,0.0010948713,0.8850209,0.026141211,0.008655663,0.00042870519,0.039993852],"study_design_scores_gemma":[0.00040031716,0.000046583347,0.0264611,0.00004782438,0.000024334253,0.000050203835,0.005301389,0.8514354,0.09678057,0.0010511314,0.01790716,0.00049397815],"about_ca_topic_score_codex":0.000006023157,"about_ca_topic_score_gemma":0.00019005497,"teacher_disagreement_score":0.12511423,"about_ca_system_score_codex":0.000029654306,"about_ca_system_score_gemma":0.000013502418,"threshold_uncertainty_score":0.3286898},"labels":[],"label_agreement":null},{"id":"W3209562871","doi":"10.1109/tvt.2021.3121985","title":"Driving Tasks Transfer Using Deep Reinforcement Learning for Decision-Making of Autonomous Vehicles in Unsignalized Intersection","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":105,"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":"Intersection (aeronautics); Reinforcement learning; Task (project management); Transfer of learning; Computer science; Engineering; Artificial intelligence; Transport engineering; Systems engineering","score_opus":0.009138235964359212,"score_gpt":0.23880260395101477,"score_spread":0.22966436798665557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209562871","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":[0.42437717,0.00015949729,0.5745441,0.000044768196,0.00019046117,0.00020773099,0.0000014786192,0.00044434483,0.00003048356],"genre_scores_gemma":[0.99147373,0.00007262832,0.008243633,0.000021486068,0.000010085442,0.00010302416,0.0000031420182,0.000059211856,0.00001303035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838585,0.0000418043,0.0006504362,0.0003722163,0.00013284542,0.00041685347],"domain_scores_gemma":[0.9992761,0.00019175878,0.000055108005,0.0003446545,0.00010359531,0.000028789162],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021629137,0.00026212333,0.00046094035,0.00093721633,0.00018077761,0.000012558204,0.0002015805,0.0006678236,0.0000710359],"category_scores_gemma":[0.000038090686,0.00031000364,0.00020425396,0.0008860273,0.00013499848,0.00012612267,0.000004455746,0.00081661734,0.000004466026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005063226,0.00006117587,0.00008475172,0.000051613675,0.00011078825,0.000027401788,0.00019474607,0.82594633,0.09549447,0.00037171142,0.0000010568787,0.077605315],"study_design_scores_gemma":[0.0007663199,0.00013077115,0.000042935437,0.00032126057,0.000065690256,0.000073088675,0.0005111307,0.68776214,0.3089426,0.0009506832,0.00018665381,0.00024676486],"about_ca_topic_score_codex":0.0000074565314,"about_ca_topic_score_gemma":0.00020482256,"teacher_disagreement_score":0.5670966,"about_ca_system_score_codex":0.00035126353,"about_ca_system_score_gemma":0.000066984874,"threshold_uncertainty_score":0.9999352},"labels":[],"label_agreement":null},{"id":"W3210339426","doi":"10.11159/jmids.2021.003","title":"On-line Situational Awareness for Autonomous Driving at Roundabouts using Artificial Intelligence","year":2021,"lang":"en","type":"article","venue":"Journal of Machine Intelligence and Data Science","topic":"Autonomous Vehicle Technology and Safety","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Line (geometry); Situation awareness; Situational ethics; Artificial intelligence; Computer science; Psychology; Aeronautics; Engineering; Social psychology; Aerospace engineering; Mathematics","score_opus":0.08824265311485818,"score_gpt":0.35411481101886155,"score_spread":0.26587215790400337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210339426","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":[0.22191587,0.0005494174,0.7765278,0.00029467986,0.00049973774,0.000062509294,0.000058024234,0.00003549268,0.00005640941],"genre_scores_gemma":[0.9789773,0.0002659756,0.020511718,0.00007505237,0.0001271666,0.000001186451,0.000018198787,0.000012448831,0.000010914687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838537,0.00001871545,0.0006167911,0.0003395757,0.0003330558,0.00030649846],"domain_scores_gemma":[0.998568,0.00035285534,0.00021591851,0.00045212172,0.00028762626,0.00012350889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013681876,0.000160871,0.00025416154,0.00021810652,0.00045592047,0.000115014474,0.0009206105,0.00008198071,0.00005444996],"category_scores_gemma":[0.00091334083,0.000143892,0.000048100002,0.00047865734,0.00040489135,0.00089569425,0.00037660092,0.00030162686,0.000007024192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007461398,0.00014107875,0.0011372884,0.000052818392,0.00005467001,0.00008373873,0.00025547645,0.5751578,0.017374404,0.07340071,0.000044732933,0.33222267],"study_design_scores_gemma":[0.000038845083,0.00010689372,0.00022567957,0.0000695161,0.000023924671,0.0004410688,0.00012306732,0.88984585,0.082511574,0.02601392,0.0004256811,0.000173968],"about_ca_topic_score_codex":0.000008371446,"about_ca_topic_score_gemma":0.00007312001,"teacher_disagreement_score":0.7570615,"about_ca_system_score_codex":0.00016934192,"about_ca_system_score_gemma":0.0005542955,"threshold_uncertainty_score":0.5867746},"labels":[],"label_agreement":null},{"id":"W3210844925","doi":"10.32920/ryerson.14660697.v1","title":"Deep vision pipeline for self-driving cars based on machine learning methods","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Toronto Metropolitan University","funders":"","keywords":"Pipeline (software); Computer science; Architecture; Code (set theory); Artificial intelligence; Deep learning; Source code","score_opus":0.00967020365272043,"score_gpt":0.2846368351736561,"score_spread":0.2749666315209357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210844925","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":[0.0041237194,0.0006476783,0.9856908,0.00024910585,0.00068313617,0.0004140821,0.0000057486905,0.0039652213,0.0042205406],"genre_scores_gemma":[0.5027813,0.00015869009,0.49633658,0.00008689588,0.000070340495,0.00008899463,0.00018976803,0.00009894452,0.00018850721],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983805,0.00013604129,0.00042970417,0.0005474278,0.00012496341,0.0003813801],"domain_scores_gemma":[0.99862444,0.00055096817,0.000082733364,0.0005961453,0.00007276253,0.00007297555],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00082927983,0.0004132884,0.0005645784,0.00023223554,0.00014334127,0.000049997943,0.0003217,0.00088374026,0.0001923328],"category_scores_gemma":[0.00019652746,0.0004169676,0.00027196086,0.00013361801,0.000023640809,0.000042412186,0.00029253343,0.0017314757,0.000009855062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008504752,0.00003967867,0.00027176956,0.00023587157,0.00007646915,0.00000811923,0.000091848706,0.8798296,0.00066277554,0.0002028286,0.00007662479,0.11849594],"study_design_scores_gemma":[0.00035245443,0.000049462054,0.00020818286,0.00013314355,0.00007183728,0.0000029362518,0.00004136387,0.9868095,0.007047177,0.00025639613,0.0046393783,0.00038818998],"about_ca_topic_score_codex":0.000018953579,"about_ca_topic_score_gemma":0.000068762914,"teacher_disagreement_score":0.49865755,"about_ca_system_score_codex":0.00020916785,"about_ca_system_score_gemma":0.000057030316,"threshold_uncertainty_score":0.9998282},"labels":[],"label_agreement":null},{"id":"W3212671907","doi":"10.1109/wf-iot51360.2021.9595955","title":"A Novel Lane Overtaking Approach for Connected Vehicles Using Cooperative Perception","year":2021,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Ontario Tech University","funders":"","keywords":"Overtaking; Visibility; Perception; Relay; Computer science; Position (finance); Simulation; Real-time computing; Transport engineering; Engineering; Geography","score_opus":0.028132096908480217,"score_gpt":0.24176971845156914,"score_spread":0.2136376215430889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212671907","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":[0.3696993,0.000052681607,0.6269884,0.000027480944,0.000042322063,0.00011138698,0.000011767961,0.00044677444,0.0026198914],"genre_scores_gemma":[0.8656117,0.000006948231,0.13403405,0.00006731292,0.00003624282,0.000016795177,0.000053350515,0.000021068205,0.00015254617],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994643,0.0000088389415,0.00013531266,0.00016556526,0.000043232532,0.00018275778],"domain_scores_gemma":[0.99972594,0.000042738953,0.000014347481,0.00012448807,0.000068952075,0.000023528157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000636104,0.000107796055,0.00014974241,0.000036969308,0.000093670584,0.000017863595,0.000057235393,0.00015574486,0.00007595797],"category_scores_gemma":[0.000029107236,0.00010740664,0.00004132206,0.000139348,0.000029115987,0.000083017636,0.000021284444,0.00012854631,0.0000045526663],"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":[0.000013496754,0.000060617484,0.00033818156,0.000059419293,0.000090522364,0.000002841863,0.00037091368,0.07021339,0.9114535,0.012946981,0.0001097614,0.004340371],"study_design_scores_gemma":[0.00054045033,0.000015791587,0.0016792081,0.000009945565,0.000019546345,0.000046215195,0.00081346417,0.9632348,0.033080373,0.00008751425,0.00029476712,0.00017792163],"about_ca_topic_score_codex":0.000010199843,"about_ca_topic_score_gemma":0.000020711417,"teacher_disagreement_score":0.8930214,"about_ca_system_score_codex":0.000069927606,"about_ca_system_score_gemma":0.000026729236,"threshold_uncertainty_score":0.43799156},"labels":[],"label_agreement":null},{"id":"W3214895017","doi":"10.3389/ffutr.2021.759125","title":"A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles","year":2021,"lang":"en","type":"review","venue":"Frontiers in Future Transportation","topic":"Autonomous Vehicle Technology and Safety","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 Manitoba","funders":"","keywords":"Obstacle; Computer science; Disengagement theory; Perception; Advanced driver assistance systems; Transport engineering; Data science; Human–computer interaction; Risk analysis (engineering); Artificial intelligence; Machine learning; Data mining; Engineering","score_opus":0.010410651493049601,"score_gpt":0.2484224591444637,"score_spread":0.2380118076514141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214895017","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":[0.00006142699,0.99409145,0.0028239647,0.000007714837,0.0013692287,0.0010425413,0.000303748,0.00026373693,0.00003615975],"genre_scores_gemma":[0.00048033026,0.99468267,0.0031281542,0.0000051667707,0.0000671242,0.00018724235,0.0013851281,0.00005361691,0.000010562982],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99827665,0.000048744194,0.0010818136,0.00024048235,0.0001623756,0.00018995929],"domain_scores_gemma":[0.9991482,0.000054333897,0.00035643962,0.00029601887,0.00011963012,0.000025405641],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032376096,0.00032324632,0.0019089059,0.00052903796,0.000022810467,0.0000054214534,0.00022903921,0.00071809697,0.0000016705911],"category_scores_gemma":[0.000023334449,0.00029284038,0.00034587612,0.001390316,0.000030661886,0.000088337714,0.0000021138424,0.0004708399,3.0158557e-7],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004564799,0.000021579597,0.00007589435,0.42379585,0.00019040101,0.000006612909,0.00011212771,0.00076905935,3.769411e-7,0.00025091114,0.0016999472,0.5730727],"study_design_scores_gemma":[0.00024243229,0.00007801735,0.00022882456,0.41300198,0.0006956352,0.000004641902,0.00006222656,0.0086571835,0.00001636396,0.000003831791,0.57664573,0.00036314776],"about_ca_topic_score_codex":7.634096e-7,"about_ca_topic_score_gemma":0.0000019673673,"teacher_disagreement_score":0.57494575,"about_ca_system_score_codex":0.00010628798,"about_ca_system_score_gemma":0.000081434315,"threshold_uncertainty_score":0.9999524},"labels":[],"label_agreement":null},{"id":"W3216996634","doi":"10.1016/j.aap.2021.106500","title":"A proactive lane-changing risk prediction framework considering driving intention recognition and different lane-changing patterns","year":2021,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":117,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Bridge (graph theory); Computer science; Advanced driver assistance systems; Artificial neural network; Artificial intelligence; Machine learning; Feature (linguistics); Driving simulator","score_opus":0.01271813641231024,"score_gpt":0.2226007970779327,"score_spread":0.20988266066562247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216996634","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":[0.6130858,0.00022524464,0.38599816,0.000025031142,0.00010386337,0.00014098502,0.0000035564344,0.00036945645,0.0000479183],"genre_scores_gemma":[0.9967909,0.0009884065,0.0014727731,0.000010839643,0.00008408133,0.000073570496,0.00045138368,0.00003107459,0.00009698024],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984373,0.00014899812,0.00044259292,0.00041422062,0.0001855953,0.0003713273],"domain_scores_gemma":[0.9992744,0.00013385908,0.00018130829,0.00026550196,0.000081748294,0.00006318455],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041294043,0.00023864282,0.0003574272,0.0008283681,0.00027995318,0.00009334541,0.0000745328,0.00025600436,0.0002122118],"category_scores_gemma":[0.00014052549,0.0002635648,0.0002455188,0.0008494749,0.000021804233,0.000375352,0.00012327367,0.00047068208,0.000016469145],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009934842,0.000057302128,0.92675936,0.000023997733,0.0022920424,0.000019611361,0.001321002,0.0022660599,0.0011678324,0.00013136773,0.0000046499104,0.065946825],"study_design_scores_gemma":[0.0005844463,0.00006383483,0.8105728,0.0006066818,0.0043021752,0.00004264994,0.00438666,0.14692074,0.013715471,0.018179413,0.000025280011,0.00059984776],"about_ca_topic_score_codex":0.000027810298,"about_ca_topic_score_gemma":0.0011887699,"teacher_disagreement_score":0.3845254,"about_ca_system_score_codex":0.00016593112,"about_ca_system_score_gemma":0.000008480538,"threshold_uncertainty_score":0.99998164},"labels":[],"label_agreement":null},{"id":"W4200005854","doi":"10.1155/2021/8453726","title":"Real-Time Vehicle Trajectory Prediction for Traffic Conflict Detection at Unsignalized Intersections","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Hunan Province; Scientific Research Foundation of Hunan Provincial Education Department; National Natural Science Foundation of China","keywords":"Trajectory; Heading (navigation); Intersection (aeronautics); Computer science; Artificial neural network; Artificial intelligence; Task (project management); Computer vision; Simulation; Engineering; Transport engineering","score_opus":0.007076844936989569,"score_gpt":0.21715407294318673,"score_spread":0.21007722800619716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200005854","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":[0.96387583,0.00021900372,0.0347418,0.00003968704,0.00063783117,0.00014096058,0.000037765596,0.00023846679,0.00006866746],"genre_scores_gemma":[0.99683356,0.00042606398,0.0024460244,0.0000079667225,0.00009169798,0.000016389768,0.0000523445,0.00002795023,0.00009802853],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991429,0.000018664268,0.0004859915,0.0001099819,0.00010407164,0.0001383856],"domain_scores_gemma":[0.999466,0.00006511606,0.00014501753,0.000084746374,0.00018857513,0.000050527993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011765582,0.000111665264,0.00020983802,0.00012401082,0.00010213294,0.000006133935,0.000049709448,0.00014111419,0.000052275038],"category_scores_gemma":[0.000013628582,0.00012215321,0.00016690667,0.00017647175,0.000028385673,0.0002823941,8.286429e-7,0.00019147861,0.0000036032097],"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":[0.00020230525,0.000025578673,0.000048860118,0.00003301643,0.00007649899,0.000010431946,0.0006673879,0.40859884,0.5802576,0.000015776764,0.000040332867,0.0100233825],"study_design_scores_gemma":[0.0064843176,0.00086063385,0.09480474,0.00016189851,0.00044081782,0.00020418748,0.0014117365,0.05152609,0.8336675,0.0003473082,0.009670618,0.0004201643],"about_ca_topic_score_codex":8.8169986e-7,"about_ca_topic_score_gemma":0.000099289355,"teacher_disagreement_score":0.35707274,"about_ca_system_score_codex":0.00019557527,"about_ca_system_score_gemma":0.000035438956,"threshold_uncertainty_score":0.49812636},"labels":[],"label_agreement":null},{"id":"W4200142290","doi":"10.1155/2021/5948971","title":"A Decision-Making Model for Self-Driving Vehicles Based on Overtaking Frequency","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Overtaking; Self driving; Driving simulator; State (computer science); Moment (physics); Driving cycle; Simulation; Collision; Automotive engineering; Computer science; Component (thermodynamics); Engineering; Decision model; Control theory (sociology); Control (management); Transport engineering; Artificial intelligence; Algorithm; Computer security; Power (physics)","score_opus":0.006637777995290989,"score_gpt":0.24008029543660672,"score_spread":0.23344251744131572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200142290","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":[0.47226894,0.00014296577,0.5271721,0.000045231634,0.00016561711,0.000054520686,0.0000061573855,0.00009384852,0.000050619554],"genre_scores_gemma":[0.7434426,0.00004543749,0.2563985,0.000050753213,0.000029751438,0.0000046824075,0.0000044256444,0.000022053833,0.000001785039],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990969,0.000007044558,0.00046063776,0.00011168544,0.00016246575,0.00016127572],"domain_scores_gemma":[0.9992911,0.00023691167,0.00015874475,0.00011486238,0.00016287058,0.000035481193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001345163,0.00011924707,0.00020682428,0.00012862156,0.00007795143,0.000010505328,0.00009423538,0.00010985214,0.000007587992],"category_scores_gemma":[0.000056893456,0.00012239163,0.00014235433,0.00014742996,0.000009733033,0.00028129737,8.681722e-7,0.00023922708,6.466235e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005257659,0.000036596077,0.00095502293,0.00004251396,0.000030465824,0.000039113624,0.00036300815,0.95607775,0.008772533,0.0009401635,0.000007671343,0.032682605],"study_design_scores_gemma":[0.0010415865,0.000089966554,0.02452043,0.000495811,0.000063110165,0.000009957123,0.00012320386,0.9530419,0.0053371014,0.015001538,0.00009993285,0.00017543683],"about_ca_topic_score_codex":9.741852e-8,"about_ca_topic_score_gemma":0.000025256344,"teacher_disagreement_score":0.2711737,"about_ca_system_score_codex":0.00009907606,"about_ca_system_score_gemma":0.00008296631,"threshold_uncertainty_score":0.4990986},"labels":[],"label_agreement":null},{"id":"W4200277452","doi":"10.1109/vtc2021-fall52928.2021.9625310","title":"SA-SGAN: A Vehicle Trajectory Prediction Model Based on Generative Adversarial Networks","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Autonomous Vehicle Technology and Safety","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":"Queen's University","funders":"Science and Technology Department, Henan Province; National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Sequence (biology); Generative grammar; Task (project management); Artificial intelligence; Displacement (psychology); Generative model; Adversarial system; Machine learning; Motion (physics); Engineering","score_opus":0.009742288950092161,"score_gpt":0.2003179927223472,"score_spread":0.19057570377225505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200277452","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":[0.47379985,0.0010655911,0.511881,0.00301244,0.001734109,0.0007398142,0.00014784669,0.003527772,0.004091583],"genre_scores_gemma":[0.99342406,0.0003745035,0.0045886855,0.00043041896,0.00026846977,0.00027003113,0.00017156925,0.000122114,0.00035017583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99641407,0.00016651045,0.000756388,0.0011769735,0.00041987366,0.0010662122],"domain_scores_gemma":[0.99778736,0.00010436144,0.00015159574,0.0014160426,0.000356796,0.00018385093],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0003411703,0.000738967,0.0008498718,0.000550795,0.0004048684,0.000071132876,0.0007349569,0.0021964002,0.00025208667],"category_scores_gemma":[0.0001122208,0.0008284362,0.00029253948,0.0012590034,0.00046294212,0.00024033777,0.00014584884,0.0023261595,0.00014453846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072027244,0.00023212109,0.0010710001,0.000043790547,0.00032063393,0.00044357532,0.00012838162,0.9312931,0.035102855,0.0095989,0.0011717809,0.02052187],"study_design_scores_gemma":[0.0016230121,0.00018320799,0.0003946475,0.00011366607,0.00013214406,0.000042117445,0.00021590765,0.941527,0.051632557,0.0016147227,0.0018155485,0.00070543715],"about_ca_topic_score_codex":0.00001527114,"about_ca_topic_score_gemma":0.00028904967,"teacher_disagreement_score":0.5196242,"about_ca_system_score_codex":0.0004063352,"about_ca_system_score_gemma":0.0005928245,"threshold_uncertainty_score":0.9999755},"labels":[],"label_agreement":null},{"id":"W4200304283","doi":"10.1109/iros51168.2021.9635900","title":"A Multimodal and Hybrid Framework for Human Navigational Intent Inference","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Autonomous Vehicle Technology and Safety","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":"Simon Fraser University","funders":"","keywords":"Computer science; Trajectory; Artificial intelligence; Reachability; Inference; Set (abstract data type); Position (finance); Perspective (graphical); Motion (physics); Machine learning; Orientation (vector space); Perception; Computer vision; Motion planning; Motion capture; Robot; Algorithm; Mathematics","score_opus":0.04790299826362454,"score_gpt":0.3108265899219895,"score_spread":0.262923591658365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200304283","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":[0.42750853,0.00085507595,0.5630239,0.0009628043,0.0031360262,0.00064013444,0.00029374947,0.0002585844,0.003321166],"genre_scores_gemma":[0.9964632,0.00058665825,0.0014782143,0.00008958303,0.0002443203,0.00016538844,0.00014638636,0.000029308809,0.0007969746],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841064,0.00003907623,0.0005293149,0.00046819155,0.00026734325,0.00028545756],"domain_scores_gemma":[0.998961,0.00021117687,0.00010410774,0.00024053846,0.00035573475,0.00012744337],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020503238,0.0002871114,0.0003472881,0.00012548424,0.0001877711,0.00020839693,0.0002419728,0.00021769883,0.0002962912],"category_scores_gemma":[0.0001144037,0.0002839592,0.00008443969,0.00007763331,0.00014483952,0.00012788465,0.000082038176,0.00044637633,0.00003373672],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003713203,0.00010614098,0.0023953333,0.00014930696,0.0002693803,0.000042551408,0.00040619937,0.01146585,0.006994718,0.9629923,0.0003016793,0.0148394145],"study_design_scores_gemma":[0.0009012879,0.0002674068,0.0032546744,0.0014688417,0.000059700888,0.00019685845,0.0014731913,0.90410656,0.021951953,0.059755564,0.005579522,0.0009844385],"about_ca_topic_score_codex":0.000042942298,"about_ca_topic_score_gemma":0.000026918779,"teacher_disagreement_score":0.90323675,"about_ca_system_score_codex":0.00011417286,"about_ca_system_score_gemma":0.00006038595,"threshold_uncertainty_score":0.99996126},"labels":[],"label_agreement":null},{"id":"W4200366202","doi":"10.1109/iros51168.2021.9636741","title":"On Fault Classification in Connected Autonomous Vehicles Using Supervised Machine Learning","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quadratic classifier; Artificial intelligence; Support vector machine; Machine learning; Classifier (UML); Computer science; Naive Bayes classifier; Margin classifier; Platoon; Discriminant; Bayes classifier; Pattern recognition (psychology)","score_opus":0.06005271555710401,"score_gpt":0.2761365916842959,"score_spread":0.21608387612719188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200366202","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":[0.9692709,0.00058885204,0.019901054,0.0005488234,0.0018544149,0.00032597518,0.000052105926,0.00028507793,0.0071727973],"genre_scores_gemma":[0.99797773,0.0007494708,0.00012968188,0.00006223464,0.00009385857,0.000038525115,0.00012222551,0.000035911817,0.0007903658],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982024,0.00013191726,0.00061242684,0.00047077108,0.00028337786,0.00029912638],"domain_scores_gemma":[0.99920875,0.00014282767,0.0001112824,0.0002496356,0.00019917835,0.0000883007],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026310835,0.00029264973,0.00037259958,0.00029272458,0.00012879347,0.00015650915,0.00024233086,0.00027983013,0.00037244006],"category_scores_gemma":[0.00009066591,0.00029609998,0.000070736234,0.00021251748,0.00007263946,0.00012547015,0.000046024034,0.00066425634,0.000088611734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001127665,0.00026192152,0.007822223,0.00013090015,0.00026344528,0.00019211345,0.00092722283,0.6638559,0.09234012,0.21299538,0.00014620928,0.020951789],"study_design_scores_gemma":[0.00041055353,0.00006910379,0.0017008577,0.00033353196,0.000012106003,0.00004120888,0.00072614016,0.98768127,0.0073180287,0.00044124867,0.000957922,0.000308058],"about_ca_topic_score_codex":0.00015974027,"about_ca_topic_score_gemma":0.00011125903,"teacher_disagreement_score":0.32382533,"about_ca_system_score_codex":0.00031354165,"about_ca_system_score_gemma":0.00008040797,"threshold_uncertainty_score":0.9999491},"labels":[],"label_agreement":null},{"id":"W4200530308","doi":"10.1117/12.2619787","title":"Towards a robust object classifier for autonomous vehicles by feature synthesis","year":2021,"lang":"en","type":"article","venue":"Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021)","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":"Queen's University","funders":"","keywords":"Artificial intelligence; Computer science; Classifier (UML); Pattern recognition (psychology); Feature extraction; Computer vision","score_opus":0.01550343547309657,"score_gpt":0.21098365192830842,"score_spread":0.19548021645521185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200530308","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":[0.85288113,0.0015786388,0.1277059,0.0028188098,0.0029441593,0.00079733087,0.0032614635,0.0028893885,0.005123186],"genre_scores_gemma":[0.9967805,0.00015803435,0.0019848368,0.00001857233,0.000083349965,0.000192297,0.00041873596,0.000046020377,0.00031761255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885654,0.000014085191,0.0003371941,0.00035384644,0.00019026276,0.00024804575],"domain_scores_gemma":[0.999447,0.00010006425,0.000052438583,0.00016247449,0.00015004011,0.000087997556],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000115155744,0.00026800117,0.00030383808,0.00012692096,0.00007904048,0.00007170477,0.00016479816,0.0002732173,0.00006863954],"category_scores_gemma":[0.000026170239,0.00028621516,0.00010280225,0.00013036074,0.000026171989,0.00011526699,0.000004103893,0.00026957452,0.0000100588995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015447411,0.00018482201,0.00034633474,0.0017446065,0.0013387933,0.0001985308,0.0023964269,0.77007097,0.0143340165,0.11895456,0.003323525,0.08695291],"study_design_scores_gemma":[0.0008923834,0.000064785956,0.004679573,0.0005824197,0.00009437858,0.00003913556,0.00074695103,0.9620855,0.010358939,0.000026719326,0.019814136,0.0006150897],"about_ca_topic_score_codex":0.000003536744,"about_ca_topic_score_gemma":0.000018627881,"teacher_disagreement_score":0.1920145,"about_ca_system_score_codex":0.000109448316,"about_ca_system_score_gemma":0.00006986682,"threshold_uncertainty_score":0.999959},"labels":[],"label_agreement":null},{"id":"W4200631182","doi":"10.1016/j.trc.2022.103926","title":"A generative car-following model conditioned on driving styles","year":2022,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Generative grammar; Trajectory; Generative model; Driving simulation; Driving simulator; Process (computing); Artificial intelligence; Variable (mathematics); Component (thermodynamics); Calibration; Simulation; Machine learning; Mathematics; Statistics","score_opus":0.04430271926385397,"score_gpt":0.31539412511613013,"score_spread":0.27109140585227615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200631182","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":[0.9798221,0.00030470057,0.008887139,0.0021135854,0.00013582005,0.00044032562,0.00010710319,0.0069304653,0.0012587565],"genre_scores_gemma":[0.99713004,0.00015239316,0.00142275,0.000023979654,0.000011549671,0.0008881403,0.0001248613,0.000045953195,0.0002003099],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980522,0.00007291053,0.00033663266,0.00036803918,0.0005863093,0.0005839166],"domain_scores_gemma":[0.99932706,0.00013022799,0.00003870004,0.00041081008,0.000057748937,0.000035434103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006357612,0.00019867133,0.00022841145,0.00066630007,0.0011736306,0.00002270967,0.00049149175,0.00015984583,0.00008938348],"category_scores_gemma":[0.0000575772,0.00022174933,0.0001013788,0.0008533174,0.00020981974,0.00016225524,0.000045384168,0.0013280443,0.000017793116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000251199,0.00007376628,0.0022348254,0.000037355123,0.00012290767,0.000101698446,0.0012828534,0.8843611,0.014106702,0.08486469,0.00721669,0.0055722897],"study_design_scores_gemma":[0.0018011797,0.0006336407,0.0036345108,0.00013577483,0.000069153684,0.0000049210657,0.040711667,0.70810205,0.16239022,0.055825748,0.025196731,0.0014943783],"about_ca_topic_score_codex":0.000013347152,"about_ca_topic_score_gemma":0.00007446837,"teacher_disagreement_score":0.17625903,"about_ca_system_score_codex":0.00022644732,"about_ca_system_score_gemma":0.00005546353,"threshold_uncertainty_score":0.90426755},"labels":[],"label_agreement":null},{"id":"W4205436213","doi":"10.3390/s22010247","title":"A Survey of Localization Methods for Autonomous Vehicles in Highway Scenarios","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Agence Nationale de la Recherche","keywords":"Overtaking; Component (thermodynamics); Fuse (electrical); Context (archaeology); Position (finance); Autonomous system (mathematics); Intelligent transportation system","score_opus":0.02173849097151072,"score_gpt":0.2884758166117836,"score_spread":0.2667373256402729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205436213","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":[0.85447603,0.00073138496,0.14349522,0.00008917036,0.000196108,0.00019172253,0.000031686493,0.00034819177,0.0004405028],"genre_scores_gemma":[0.98166937,0.000042112628,0.01809891,0.000019125295,0.000007731575,0.0000106857115,0.000034612163,0.000023411267,0.000094071824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992677,0.00011209133,0.0002662075,0.00014316091,0.00003508469,0.0001757584],"domain_scores_gemma":[0.9994253,0.00025332734,0.0000311443,0.000189658,0.000078405006,0.000022193135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004674346,0.000092514136,0.00022278147,0.00008832282,0.000025436922,0.0000042407955,0.000075080425,0.00017907005,0.000013929805],"category_scores_gemma":[0.00021933122,0.00010312942,0.00003811545,0.00032464368,0.000043674772,0.000029495384,0.000019032788,0.00011095721,0.0000041227104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004329161,0.00009728664,0.02857112,0.00024321234,0.000103510916,0.000017779383,0.0008389744,0.7576769,0.017994666,0.0032807027,0.00021643464,0.1909161],"study_design_scores_gemma":[0.0003813935,0.000019926903,0.060704276,0.00002041671,0.000008610224,0.0000041037815,0.00006534599,0.8240857,0.11189708,0.0005347509,0.0021341816,0.0001441674],"about_ca_topic_score_codex":0.000082643186,"about_ca_topic_score_gemma":0.00043705045,"teacher_disagreement_score":0.19077194,"about_ca_system_score_codex":0.00005417761,"about_ca_system_score_gemma":0.00004449228,"threshold_uncertainty_score":0.42054957},"labels":[],"label_agreement":null},{"id":"W4206545034","doi":"10.1109/bigdata52589.2021.9671392","title":"Drivable Area Detection Using Deep Learning Models for Autonomous Driving","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Big Data (Big Data)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Pyramid (geometry); Artificial intelligence; Segmentation; Backbone network; Computer vision; Pixel; Deep learning; Feature (linguistics); Pooling; Architecture; Image segmentation; Advanced driver assistance systems; Object detection; Pattern recognition (psychology); Geography","score_opus":0.2613181683505157,"score_gpt":0.31131480188750965,"score_spread":0.04999663353699396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206545034","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":[0.04400438,0.00011263716,0.9462437,0.0002490388,0.0038100202,0.00019004574,0.0015506468,0.00038711552,0.0034524247],"genre_scores_gemma":[0.98665345,0.0003639468,0.0052024615,0.000050701532,0.0006363998,0.000024630615,0.0067060688,0.000051879895,0.00031047128],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803734,0.00004520365,0.00041651746,0.0008295024,0.00028532994,0.00038610364],"domain_scores_gemma":[0.9978955,0.00013606405,0.00011648886,0.001541022,0.00022907078,0.0000818479],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036586498,0.0002723825,0.00028263347,0.00018595724,0.0002449399,0.0002001288,0.0016815981,0.0002463152,0.00015796417],"category_scores_gemma":[0.0002438156,0.00032157995,0.00004982661,0.0001931403,0.00006469251,0.0009230607,0.0007220279,0.0005791914,0.000050116654],"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":[0.00004766592,0.00010343676,0.00023786581,0.000051136234,0.00041433627,0.00007028727,0.000097645825,0.2120551,0.047012996,0.004416725,0.00057426293,0.73491853],"study_design_scores_gemma":[0.0003885691,0.000025440571,0.000072614945,0.0000964505,0.000041644627,0.00004552808,0.00014194401,0.97064084,0.010891575,0.0016943023,0.015631415,0.0003296836],"about_ca_topic_score_codex":0.00004643182,"about_ca_topic_score_gemma":0.0008036772,"teacher_disagreement_score":0.94264907,"about_ca_system_score_codex":0.00021288819,"about_ca_system_score_gemma":0.00018595511,"threshold_uncertainty_score":0.99992365},"labels":[],"label_agreement":null},{"id":"W4206661064","doi":"10.1155/2022/4736623","title":"Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":3,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Guangxi University; Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China","keywords":"Point cloud; Computer science; Artificial intelligence; Computer vision; Lidar; Ranging; Motion (physics); Feature (linguistics); Feature extraction; Convolution (computer science); Pattern recognition (psychology); Artificial neural network; Remote sensing","score_opus":0.002638806445665586,"score_gpt":0.21236796491709306,"score_spread":0.20972915847142748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206661064","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":[0.7698226,0.00031332378,0.2286304,0.0002312807,0.00047533988,0.0002069688,0.000113289,0.00016178824,0.00004500909],"genre_scores_gemma":[0.9607003,0.00015354354,0.038971975,0.000024706187,0.000027602377,0.000013824599,0.00008290371,0.000012389988,0.000012763156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99917096,0.000051555562,0.0004646653,0.000066748755,0.00014310912,0.000102979684],"domain_scores_gemma":[0.99949867,0.00005059548,0.00026302613,0.000053316922,0.00010247712,0.000031918444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032099758,0.00010841611,0.0001760979,0.00015973048,0.00013338226,0.000012614152,0.000058117534,0.000044397562,0.000023045437],"category_scores_gemma":[0.000025919357,0.000106922154,0.000051684063,0.0001392045,0.000018933077,0.0019255821,0.0000044153626,0.00049122405,0.0000011719754],"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":[0.00034477617,0.00002909662,0.000526499,0.000065975895,0.00008294175,0.000008337628,0.013225328,0.8132533,0.02063938,0.0001886749,0.00007199035,0.15156373],"study_design_scores_gemma":[0.00684858,0.0022857473,0.6605254,0.00036680215,0.00031300346,0.00061816105,0.031605188,0.2022408,0.07411717,0.010797411,0.009351214,0.00093053555],"about_ca_topic_score_codex":0.000005274633,"about_ca_topic_score_gemma":0.000014317445,"teacher_disagreement_score":0.6599989,"about_ca_system_score_codex":0.00012768712,"about_ca_system_score_gemma":0.000014053473,"threshold_uncertainty_score":0.4360159},"labels":[],"label_agreement":null},{"id":"W4206746675","doi":"10.1109/access.2021.3136226","title":"Survey of RPAS Autonomous Control Systems Using Artificial Intelligence","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; British Columbia Institute of Technology","funders":"University of Mumbai; British Columbia Institute of Technology; Northeastern University","keywords":"Computer science; Field (mathematics); Autonomy; Control (management); Control system; Systems engineering; Artificial intelligence; Engineering; Electrical engineering","score_opus":0.0674397583161026,"score_gpt":0.2996011143758428,"score_spread":0.2321613560597402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206746675","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":[0.66112924,0.0008121913,0.33614817,0.00001638239,0.0010538036,0.00011012912,0.000047770453,0.00026985712,0.0004124924],"genre_scores_gemma":[0.9997424,0.000022530145,0.0001185966,0.000014684216,0.00005380797,0.0000072399735,0.000006175623,0.000022710103,0.000011841076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901646,0.00007044432,0.00041557266,0.00017398299,0.000093263625,0.0002302664],"domain_scores_gemma":[0.9993038,0.00013615245,0.00006862068,0.00031483648,0.00013769636,0.000038900707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029906462,0.00012682364,0.00030569755,0.0000733966,0.000053455162,0.000043355638,0.00032741483,0.00018134763,0.000035383968],"category_scores_gemma":[0.000056312972,0.00013897957,0.000043257853,0.00034519774,0.00007396813,0.00016296566,0.00003556399,0.00019384542,0.000018024894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020692883,0.000062169296,0.009886732,0.00013582576,0.00015964662,0.00006623616,0.00007107092,0.9503109,0.01580646,0.004329564,0.00004928935,0.01910141],"study_design_scores_gemma":[0.00009097224,0.000013749428,0.011886707,0.000037779108,0.00002850789,0.000023485414,0.00004018705,0.8379641,0.14888038,0.0007400508,0.000068522335,0.0002255779],"about_ca_topic_score_codex":0.0004471011,"about_ca_topic_score_gemma":0.0002459511,"teacher_disagreement_score":0.3386132,"about_ca_system_score_codex":0.000063784035,"about_ca_system_score_gemma":0.000087149805,"threshold_uncertainty_score":0.56674224},"labels":[],"label_agreement":null},{"id":"W4206832203","doi":"10.1155/2022/7907435","title":"Traffic Risk Modelling and Analysis under Airport-Like Simulation Environment","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Transport engineering; Collision; Task (project management); Separation (statistics); Computer science; Air traffic control; State (computer science); Simulation; Automotive engineering; Engineering; Computer security; Systems engineering; Aerospace engineering","score_opus":0.006598116740977125,"score_gpt":0.19857172350066107,"score_spread":0.19197360675968395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206832203","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":[0.6146722,0.00034550694,0.38483936,0.00001584302,0.000049362243,0.000034252676,0.000010125788,0.000030137086,0.0000032571133],"genre_scores_gemma":[0.9935598,0.0005085737,0.0058663953,0.000008811624,0.0000105763975,0.000003247683,0.000024717143,0.000011727232,0.0000061977266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993104,0.000016245373,0.0003520089,0.000080846716,0.00015382195,0.00008669697],"domain_scores_gemma":[0.9996697,0.00003338405,0.00018289362,0.00006794021,0.000014252193,0.000031883606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016106517,0.0000782007,0.00016574009,0.00020107323,0.000114650524,0.000003243315,0.000048736067,0.000038675556,0.000042011274],"category_scores_gemma":[7.781228e-7,0.000084414576,0.00008888363,0.000185371,0.000016861317,0.00017038178,0.0000014541343,0.00028286158,3.7150525e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000389076,0.000018741177,0.0015369058,0.000005030158,0.00020436346,0.000007435531,0.0006302445,0.9880185,0.00012456946,0.000037570288,6.59108e-7,0.009377033],"study_design_scores_gemma":[0.00043312233,0.000074877615,0.06872705,0.0000022451284,0.00046006768,0.0000031514348,0.0005239743,0.9285199,0.000054698274,0.0006342952,0.00047135266,0.000095277544],"about_ca_topic_score_codex":8.426259e-7,"about_ca_topic_score_gemma":0.000005396661,"teacher_disagreement_score":0.37897295,"about_ca_system_score_codex":0.000075005126,"about_ca_system_score_gemma":0.000007151908,"threshold_uncertainty_score":0.34423265},"labels":[],"label_agreement":null},{"id":"W4207000465","doi":"10.32920/16834393","title":"Motion Boundaries Formation For Autonomous Vehicles","year":2021,"lang":"en","type":"preprint","venue":"","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":"Toronto Metropolitan University","funders":"","keywords":"Curvilinear coordinates; Nonlinear system; Trajectory; Control theory (sociology); Model predictive control; Motion (physics); Computer science; Nonlinear model; Horizon; Time horizon; Mathematical optimization; Control (management); Mathematics; Artificial intelligence; Physics","score_opus":0.013275257583367499,"score_gpt":0.22006610832946627,"score_spread":0.20679085074609876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207000465","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":[0.17229973,0.00083156995,0.8176887,0.0004372308,0.0010813663,0.0005185112,0.0000469191,0.0028956626,0.004200305],"genre_scores_gemma":[0.9845512,0.00008708374,0.014309832,0.000045359247,0.000081793805,0.00021084228,0.00043312792,0.000039492097,0.00024127626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915653,0.000010770599,0.00031599755,0.00021798024,0.00006725273,0.00023145002],"domain_scores_gemma":[0.99947256,0.000028747807,0.000053166972,0.000344055,0.00007171592,0.000029766543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013494275,0.00021514439,0.00026821913,0.00009724498,0.00018103528,0.00020401484,0.00017798055,0.0006553713,0.000053369287],"category_scores_gemma":[0.000022000833,0.00023543317,0.00013364211,0.000051248844,0.000081647515,0.0001812725,0.00015021613,0.00041577936,0.000016622707],"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":[0.000043926277,0.00013514255,0.00040446292,0.004669088,0.0007050419,0.000017444312,0.004021271,0.2712375,0.004554563,0.08063765,0.012521715,0.6210522],"study_design_scores_gemma":[0.0004046821,0.000031016472,0.0013295158,0.0000942676,0.00008474548,0.000018353312,0.00043459496,0.88384044,0.05611474,0.02292262,0.034043077,0.00068195414],"about_ca_topic_score_codex":0.000011311846,"about_ca_topic_score_gemma":0.00006661755,"teacher_disagreement_score":0.81225145,"about_ca_system_score_codex":0.00019658345,"about_ca_system_score_gemma":0.00008692239,"threshold_uncertainty_score":0.9600686},"labels":[],"label_agreement":null},{"id":"W4207078074","doi":"10.32920/16834393.v1","title":"Motion Boundaries Formation For Autonomous Vehicles","year":2021,"lang":"en","type":"preprint","venue":"","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":"Toronto Metropolitan University","funders":"","keywords":"Curvilinear coordinates; Nonlinear system; Control theory (sociology); Trajectory; Motion (physics); Model predictive control; Computer science; Nonlinear model; Horizon; Time horizon; Sensitivity (control systems); Mathematical optimization; Mathematics; Control (management); Engineering; Artificial intelligence; Physics","score_opus":0.013275257583367499,"score_gpt":0.22006610832946627,"score_spread":0.20679085074609876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207078074","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":[0.17229973,0.00083156995,0.8176887,0.0004372308,0.0010813663,0.0005185112,0.0000469191,0.0028956626,0.004200305],"genre_scores_gemma":[0.9845512,0.00008708374,0.014309832,0.000045359247,0.000081793805,0.00021084228,0.00043312792,0.000039492097,0.00024127626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915653,0.000010770599,0.00031599755,0.00021798024,0.00006725273,0.00023145002],"domain_scores_gemma":[0.99947256,0.000028747807,0.000053166972,0.000344055,0.00007171592,0.000029766543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013494275,0.00021514439,0.00026821913,0.00009724498,0.00018103528,0.00020401484,0.00017798055,0.0006553713,0.000053369287],"category_scores_gemma":[0.000022000833,0.00023543317,0.00013364211,0.000051248844,0.000081647515,0.0001812725,0.00015021613,0.00041577936,0.000016622707],"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":[0.000043926277,0.00013514255,0.00040446292,0.004669088,0.0007050419,0.000017444312,0.004021271,0.2712375,0.004554563,0.08063765,0.012521715,0.6210522],"study_design_scores_gemma":[0.0004046821,0.000031016472,0.0013295158,0.0000942676,0.00008474548,0.000018353312,0.00043459496,0.88384044,0.05611474,0.02292262,0.034043077,0.00068195414],"about_ca_topic_score_codex":0.000011311846,"about_ca_topic_score_gemma":0.00006661755,"teacher_disagreement_score":0.81225145,"about_ca_system_score_codex":0.00019658345,"about_ca_system_score_gemma":0.00008692239,"threshold_uncertainty_score":0.9600686},"labels":[],"label_agreement":null},{"id":"W4212791280","doi":"10.1109/tits.2022.3151264","title":"Siamese Temporal Convolutional Networks for Driver Identification Using Driver Steering Behavior Analysis","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Identification (biology); Computer science; Personalization; Telematics; Intelligent transportation system; Variety (cybernetics); Advanced driver assistance systems; Function (biology); Artificial intelligence; Machine learning; Real-time computing; Engineering; Transport engineering; Telecommunications","score_opus":0.02243233130397846,"score_gpt":0.24773011701469239,"score_spread":0.22529778571071393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212791280","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":[0.28806156,0.00010039834,0.70905614,0.000006576576,0.0012482863,0.0006700523,0.00044937243,0.00040106886,0.000006510569],"genre_scores_gemma":[0.997744,0.000023634164,0.00044196023,0.000010438347,0.000038179318,0.0011545168,0.00031461517,0.000051576513,0.00022106654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823374,0.000052370455,0.0007675369,0.00035401215,0.00030246834,0.0002898963],"domain_scores_gemma":[0.99934465,0.000067002446,0.00013416843,0.00027783946,0.0001032798,0.00007307528],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028070656,0.0002347503,0.00032400302,0.0005711829,0.0005590891,0.000033504046,0.00019921926,0.0001581154,0.00020900984],"category_scores_gemma":[5.9433154e-7,0.0002910497,0.00034852102,0.000833577,0.000059670467,0.00018482712,4.735377e-7,0.00036928267,0.000008762661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041989806,0.00010889187,0.0020363452,0.00003509432,0.0005647157,0.0000046310342,0.00043205306,0.993855,0.0014865581,0.00037066735,0.000020832736,0.0010432093],"study_design_scores_gemma":[0.00031704712,0.000058269972,0.005606419,0.000011652657,0.0009770271,0.0000070318383,0.0008977528,0.9880126,0.0030034904,0.000009824815,0.0007661626,0.00033269878],"about_ca_topic_score_codex":0.00010718455,"about_ca_topic_score_gemma":0.00012582171,"teacher_disagreement_score":0.70968246,"about_ca_system_score_codex":0.00042682205,"about_ca_system_score_gemma":0.00003355688,"threshold_uncertainty_score":0.99995416},"labels":[],"label_agreement":null},{"id":"W4213069740","doi":"10.1109/tits.2022.3151263","title":"A Novel Multimodal Vehicle Path Prediction Method Based on Temporal Convolutional Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Path (computing); Computer network","score_opus":0.014723105070724593,"score_gpt":0.22661348451262794,"score_spread":0.21189037944190336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213069740","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":[0.018454935,0.00005948894,0.9758196,0.00005928601,0.0024331831,0.00066591747,0.0011862982,0.0011867508,0.0001345543],"genre_scores_gemma":[0.9970123,0.000013657051,0.0015668335,0.00008399742,0.000059312515,0.00081575074,0.00024653864,0.00006440444,0.00013721669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803245,0.00010402489,0.00068095396,0.00039445714,0.00045924558,0.00032888725],"domain_scores_gemma":[0.9992806,0.00015758853,0.00009251898,0.00030236875,0.00006460683,0.00010232754],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041906693,0.00028945482,0.00028642843,0.00033156312,0.0005073169,0.000020768126,0.0001956619,0.00021275572,0.00028546617],"category_scores_gemma":[0.0000010885212,0.00033084975,0.00020388195,0.0004608564,0.000050931187,0.00011197462,3.4684254e-7,0.0008396827,0.000028466517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018161087,0.00032265054,0.0002623166,0.000037613474,0.000095864816,0.000007519855,0.000242128,0.99426943,0.0008771048,0.00047755265,0.000102784026,0.0031233972],"study_design_scores_gemma":[0.00079209433,0.00029223802,0.0013611121,0.000036295416,0.000055749122,0.000011091369,0.00040150952,0.9922361,0.0024842601,0.00001079553,0.0020410207,0.00027777872],"about_ca_topic_score_codex":0.00014870777,"about_ca_topic_score_gemma":0.000043210566,"teacher_disagreement_score":0.97855735,"about_ca_system_score_codex":0.00040654643,"about_ca_system_score_gemma":0.000059471477,"threshold_uncertainty_score":0.99991435},"labels":[],"label_agreement":null},{"id":"W4214496626","doi":"10.1109/tvt.2022.3151651","title":"Highway Decision-Making and Motion Planning for Autonomous Driving via Soft Actor-Critic","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":130,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Chongqing; National Natural Science Foundation of China","keywords":"Reinforcement learning; Motion planning; Cruise control; Action (physics); Engineering; Optimal control; Motion (physics); Controller (irrigation); Vehicle dynamics; Control (management); Computer science; Simulation; Control engineering; Artificial intelligence; Automotive engineering; Robot","score_opus":0.00642917467920485,"score_gpt":0.22526974420586768,"score_spread":0.21884056952666284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214496626","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":[0.29253387,0.000292443,0.7038337,0.00029282385,0.00048068218,0.00030980565,0.000023004879,0.0022067034,0.000026937707],"genre_scores_gemma":[0.99243194,0.000017857317,0.006873925,0.000060999177,0.000019500925,0.0004943376,0.000004414426,0.0000744763,0.00002252537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859303,0.000026674674,0.00034331315,0.00042953668,0.00014184912,0.00046559164],"domain_scores_gemma":[0.9992097,0.0002478714,0.0000515814,0.000402972,0.000041172738,0.000046672205],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020990166,0.00026451424,0.00031305014,0.0007876026,0.0009076399,0.000019629975,0.0003029844,0.0004061287,0.000061482635],"category_scores_gemma":[0.000019081044,0.0003182426,0.00009901691,0.0005619489,0.00014754597,0.0001215091,0.000011278539,0.0009699686,0.000009597015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000359651,0.00009213988,0.000115741466,0.0000407275,0.00012739225,0.000047341036,0.00015453516,0.5174728,0.013085723,0.0008927816,0.000051590567,0.46788326],"study_design_scores_gemma":[0.0012935753,0.0005564114,0.00037090393,0.000114080016,0.00015963061,0.00068016304,0.0006109292,0.9320643,0.038521085,0.016370956,0.008378214,0.0008797612],"about_ca_topic_score_codex":0.0000017199025,"about_ca_topic_score_gemma":0.000008503066,"teacher_disagreement_score":0.69989806,"about_ca_system_score_codex":0.00028628335,"about_ca_system_score_gemma":0.000026232352,"threshold_uncertainty_score":0.999927},"labels":[],"label_agreement":null},{"id":"W4214672496","doi":"10.1109/icssit53264.2022.9716433","title":"An Overview of Deep Learning Techniques for Autonomous Driving Vehicles","year":2022,"lang":"en","type":"article","venue":"2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT)","topic":"Autonomous Vehicle Technology and Safety","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":"Horizon College and Seminary","funders":"","keywords":"Computer science; Deep learning; Quality (philosophy); Artificial intelligence; Data science; Risk analysis (engineering)","score_opus":0.03758714809743346,"score_gpt":0.28747814720101694,"score_spread":0.24989099910358348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214672496","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":[0.9518999,0.0041858815,0.027109787,0.0011211715,0.0015567887,0.0014212065,0.0002121893,0.0029876935,0.009505379],"genre_scores_gemma":[0.9980216,0.00037820262,0.0006522666,0.00003157991,0.000033809683,0.0005182561,0.00005667618,0.000033306234,0.00027431242],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986923,0.00007053409,0.00045026452,0.00034680785,0.00019187483,0.00024825367],"domain_scores_gemma":[0.99930567,0.000049744696,0.00020314584,0.00025307058,0.0001472971,0.000041085383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000467292,0.0002078901,0.00036125057,0.0005603048,0.0003138646,0.000028169265,0.00051644107,0.00022070734,0.00013478294],"category_scores_gemma":[0.00003461676,0.00022888716,0.000072896284,0.00022907212,0.00019135159,0.00014001312,0.0002150333,0.0006224208,0.0000034330735],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043218948,0.00011865175,0.016578697,0.00012487122,0.00025699232,0.000011244952,0.0002895851,0.0014156671,0.022921357,0.91318184,0.00015674064,0.044901107],"study_design_scores_gemma":[0.0010450653,0.0020544743,0.0034677854,0.0002856073,0.000071002265,0.00012514117,0.006334584,0.8626839,0.026847307,0.03330807,0.06289047,0.00088659785],"about_ca_topic_score_codex":0.000027070011,"about_ca_topic_score_gemma":0.000018259389,"teacher_disagreement_score":0.8798738,"about_ca_system_score_codex":0.00018263886,"about_ca_system_score_gemma":0.00003677144,"threshold_uncertainty_score":0.93337476},"labels":[],"label_agreement":null},{"id":"W4220713727","doi":"10.1109/icais53314.2022.9742978","title":"Review On Lane and Object Detection for Accident Prevention in Automated Cars","year":2022,"lang":"en","type":"article","venue":"2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS)","topic":"Autonomous Vehicle Technology and Safety","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":"Horizon College and Seminary","funders":"","keywords":"Computer science; Object detection; Accident (philosophy); Object (grammar); Artificial intelligence; Computer vision; Pattern recognition (psychology)","score_opus":0.042105067697007316,"score_gpt":0.2903408185714225,"score_spread":0.2482357508744152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220713727","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":[0.95817685,0.004856802,0.014719396,0.0027374898,0.0026591078,0.0011368624,0.00018120627,0.0011451711,0.014387134],"genre_scores_gemma":[0.9953299,0.0035121574,0.000036744063,0.00047021353,0.000032150627,0.00028347864,0.000073029805,0.0000143000025,0.0002479915],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901116,0.00005495007,0.00035217145,0.00028196015,0.00013392203,0.00016584632],"domain_scores_gemma":[0.99966866,0.00006811804,0.000062893734,0.0001199656,0.000043037777,0.00003730297],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00029053542,0.00014612803,0.00018792071,0.0002140576,0.00014303855,0.00003132623,0.00016283634,0.000073191615,0.0009796616],"category_scores_gemma":[0.000035471152,0.0001582386,0.000046190988,0.00014072422,0.000042309923,0.000099739664,0.00006276961,0.00026208794,0.0000081253165],"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":[0.00024685226,0.00018156452,0.000186876,0.00011585327,0.00011117728,0.00001496064,0.00025566824,0.0066477736,0.007964745,0.26256987,0.0008778832,0.72082675],"study_design_scores_gemma":[0.0002735949,0.0012396581,0.0020565588,0.00042113126,0.000032761363,0.00004982148,0.0007563606,0.85162854,0.048012085,0.051018655,0.04389806,0.0006127522],"about_ca_topic_score_codex":0.00004883497,"about_ca_topic_score_gemma":0.0012079328,"teacher_disagreement_score":0.8449808,"about_ca_system_score_codex":0.00011573698,"about_ca_system_score_gemma":0.000025952198,"threshold_uncertainty_score":0.9999336},"labels":[],"label_agreement":null},{"id":"W4220755522","doi":"10.1155/2022/5134437","title":"Efficient Lane Detection Technique Based on Lightweight Attention Deep Neural Network","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Exploit; Artificial intelligence; Deep learning; Feature (linguistics); Benchmark (surveying); Computation; Task (project management); Artificial neural network; Segmentation; Pixel; Object detection; Machine learning; Pattern recognition (psychology); Algorithm; Engineering","score_opus":0.003104492658410055,"score_gpt":0.18934465662059247,"score_spread":0.1862401639621824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220755522","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":[0.7411954,0.000122069185,0.25753435,0.00010132735,0.00062872353,0.00016919154,0.0000047950043,0.00017155918,0.00007260323],"genre_scores_gemma":[0.99571764,0.000010739084,0.004100465,0.000032464253,0.00006740916,0.00003144526,0.000016499933,0.000020376174,0.000002937713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917394,0.000031141568,0.00036131463,0.00008664865,0.00020120332,0.00014575208],"domain_scores_gemma":[0.99964017,0.000029657003,0.00016610205,0.00009011141,0.000041771338,0.000032215325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021698583,0.00010257145,0.0001447401,0.0001631645,0.00015246427,0.0000036098472,0.00008996637,0.000061392566,0.000034020548],"category_scores_gemma":[0.0000032587554,0.000103936414,0.00009767472,0.000275875,0.000013364871,0.00007046579,0.0000013626233,0.00048328162,0.0000013056901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016544884,0.000040296858,0.00031232557,0.000013415201,0.000012237192,0.000031704716,0.00005519936,0.9665087,0.01811543,0.00012647813,0.0000045136853,0.014614292],"study_design_scores_gemma":[0.0020403923,0.0014499022,0.17828615,0.000059617927,0.000105394014,0.00008693747,0.0001729719,0.79108655,0.022682315,0.00084967725,0.0027916087,0.0003884651],"about_ca_topic_score_codex":6.324369e-7,"about_ca_topic_score_gemma":0.000010938295,"teacher_disagreement_score":0.2545223,"about_ca_system_score_codex":0.00014288393,"about_ca_system_score_gemma":0.000009578003,"threshold_uncertainty_score":0.4238404},"labels":[],"label_agreement":null},{"id":"W4220897160","doi":"10.1155/2022/2116814","title":"Development Status and Hotspot Visualized Analysis of Autonomous Vehicles Based on CiteSpace","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Hotspot (geology); Visualization; China; Field (mathematics); Data science; Computer science; Web of science; Citation; Systems engineering; Operations research; Engineering; Geography; Artificial intelligence; Political science; World Wide Web","score_opus":0.006279323589595438,"score_gpt":0.23687164218048778,"score_spread":0.23059231859089235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220897160","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":[0.9914225,0.00023507106,0.008060589,0.000044386255,0.000072331626,0.00006388749,0.000021980224,0.00005030199,0.000028969685],"genre_scores_gemma":[0.98824126,0.00006359602,0.011612173,0.000025620906,0.0000039404777,0.0000064461165,0.000028821965,0.000012716007,0.0000054286743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.999098,0.000019615905,0.00046838672,0.00008485167,0.00020261525,0.00012655686],"domain_scores_gemma":[0.99953264,0.00006213325,0.0002306289,0.000078611745,0.000050057584,0.0000459537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019349772,0.000097739074,0.00029465696,0.0005624927,0.00007326029,0.0000030378878,0.00007098236,0.000039384766,0.000045211036],"category_scores_gemma":[0.000006313135,0.00010141658,0.00008508897,0.0005495155,0.00002483928,0.00009064304,0.0000019726615,0.00021315787,1.6340857e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028083392,0.00005570172,0.007452899,0.00002267705,0.00029827404,0.000012829274,0.0015322147,0.96472454,0.0089641055,0.00022340892,0.0000021205626,0.016430413],"study_design_scores_gemma":[0.0017104725,0.0003799075,0.94161063,0.000021643475,0.00044509306,0.000001990666,0.001163885,0.03842484,0.0128546795,0.000099153454,0.003098292,0.00018938683],"about_ca_topic_score_codex":0.0000016060972,"about_ca_topic_score_gemma":0.000021438454,"teacher_disagreement_score":0.9341577,"about_ca_system_score_codex":0.00011133323,"about_ca_system_score_gemma":0.0000617597,"threshold_uncertainty_score":0.4135648},"labels":[],"label_agreement":null},{"id":"W4220933764","doi":"10.1155/2022/2266706","title":"Research on the Prediction of the Operational Risk Field of Intelligent Vehicles Based on Dual Multiline LiDAR","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Guangxi University; Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China","keywords":"Point cloud; Computer science; Field (mathematics); Dual (grammatical number); Artificial intelligence; Computer vision; Cloud computing; Real-time computing; Lidar; Sequence (biology); Feature (linguistics); Simulation; Remote sensing","score_opus":0.018932524671738296,"score_gpt":0.2787639994913567,"score_spread":0.25983147481961844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220933764","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":[0.9953122,0.000050912044,0.003396326,0.00073674193,0.000207488,0.00014588052,0.0000848104,0.000012901475,0.000052715397],"genre_scores_gemma":[0.99944663,0.00006938802,0.000387484,0.00003653639,0.000027536353,0.000010057245,0.000007142297,0.000007746896,0.0000074979794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99896485,0.00009551722,0.0004021604,0.000056859793,0.0004057814,0.00007483297],"domain_scores_gemma":[0.9991785,0.00038080703,0.00015905648,0.00013210317,0.00013621029,0.000013298021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006272663,0.00005625872,0.00010189126,0.00011918585,0.00015072725,0.0000018815263,0.00014899253,0.000042998887,0.00006080118],"category_scores_gemma":[0.00005834021,0.00003694145,0.00008013926,0.00022290401,0.000050065722,0.000054418986,0.0000031366073,0.0007142276,3.2237728e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003738881,0.00008675277,0.0031314003,0.000013842481,0.000024095432,0.0000016622065,0.0005150902,0.9821887,0.008120773,0.001000685,0.000048212834,0.004494884],"study_design_scores_gemma":[0.0011805949,0.0021344956,0.47508708,0.00014225791,0.00004890447,0.0000039974825,0.0029001222,0.0961299,0.41982946,0.0013735475,0.0010810462,0.00008858107],"about_ca_topic_score_codex":0.000004466704,"about_ca_topic_score_gemma":0.000013412933,"teacher_disagreement_score":0.8860588,"about_ca_system_score_codex":0.000054358326,"about_ca_system_score_gemma":0.00004702298,"threshold_uncertainty_score":0.31030032},"labels":[],"label_agreement":null},{"id":"W4221082595","doi":"10.1016/j.trc.2022.103625","title":"Optimal roadside units location for path flow reconstruction in a connected vehicle environment","year":2022,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Path (computing); Transport engineering; Flow (mathematics); Computer science; Traffic flow (computer networking); Engineering; Simulation; Mathematics; Computer security; Computer network; Geometry","score_opus":0.03698980274093822,"score_gpt":0.27145439328940746,"score_spread":0.23446459054846924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221082595","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":[0.9805901,0.00042797616,0.014578808,0.0010849241,0.000118532625,0.00079791516,0.00007880557,0.0022770038,0.000045899607],"genre_scores_gemma":[0.9928535,0.00030389123,0.004848071,0.000005225639,0.000008977225,0.0017055076,0.0002173233,0.00003028974,0.000027173082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852,0.00006284345,0.00037314667,0.00030621258,0.00027146182,0.0004663053],"domain_scores_gemma":[0.9994423,0.00012205973,0.00004291144,0.00028896506,0.00008034146,0.00002346486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071566965,0.00013921094,0.00017364397,0.00056463416,0.00039484387,0.000010634432,0.0002894944,0.00016323141,0.00008134271],"category_scores_gemma":[0.00008544927,0.0001679687,0.000031214655,0.0011720514,0.0001923214,0.00014672447,0.000026044054,0.0007518686,0.000008080211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000733932,0.00004786612,0.0042192894,0.00005579071,0.000031443084,0.000012799821,0.0005511725,0.9225181,0.002509239,0.006693435,0.000552893,0.06273458],"study_design_scores_gemma":[0.0017191502,0.00040159802,0.0110577615,0.000070161004,0.000018453658,0.00000669099,0.01860675,0.90232223,0.028925288,0.007988552,0.028350957,0.0005323914],"about_ca_topic_score_codex":0.000026461768,"about_ca_topic_score_gemma":0.000095824485,"teacher_disagreement_score":0.06220219,"about_ca_system_score_codex":0.00027896234,"about_ca_system_score_gemma":0.000054983386,"threshold_uncertainty_score":0.6849565},"labels":[],"label_agreement":null},{"id":"W4223986562","doi":"10.1155/2022/1410296","title":"Extracting Overtaking Segments by Unsupervised Clustering and Predicting Nonmotorized Vehicle’s Trajectory","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Overtaking; Trajectory; Computer science; Cluster analysis; Hierarchical Dirichlet process; Latent Dirichlet allocation; Artificial intelligence; Pattern recognition (psychology); Topic model; Engineering","score_opus":0.005081276326540273,"score_gpt":0.20263687915095002,"score_spread":0.19755560282440976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223986562","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":[0.9871423,0.0011237849,0.010996413,0.000032981203,0.00041729817,0.0001028308,0.000020368461,0.0001204774,0.00004351096],"genre_scores_gemma":[0.99631,0.00015109687,0.0034197657,0.00002171114,0.0000390126,0.000008582701,0.000009769693,0.000028955654,0.00001108999],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9990251,0.000023123403,0.00047822946,0.00010494666,0.0001998429,0.00016876732],"domain_scores_gemma":[0.9995944,0.000053396812,0.00020991913,0.00006496538,0.000028551027,0.00004876445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002396177,0.00011828116,0.00020457181,0.000107148044,0.00020303593,0.000008840268,0.000093273,0.000053888314,0.000030020094],"category_scores_gemma":[0.000009231518,0.00013567532,0.00005601814,0.00012353617,0.000015702037,0.00040900082,0.0000048374723,0.0005146009,2.0096851e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015164205,0.000035831523,0.011222254,0.00007480712,0.00008681473,0.000045705023,0.0029554148,0.57022643,0.36561552,0.000010192509,0.0000123999325,0.04956298],"study_design_scores_gemma":[0.018386004,0.001388868,0.68866533,0.0005004461,0.00049104245,0.00046896018,0.025575534,0.20645848,0.045068815,0.0004833619,0.010840662,0.0016725266],"about_ca_topic_score_codex":0.0000043249665,"about_ca_topic_score_gemma":0.0000123463415,"teacher_disagreement_score":0.677443,"about_ca_system_score_codex":0.00012163198,"about_ca_system_score_gemma":0.00001519075,"threshold_uncertainty_score":0.5532679},"labels":[],"label_agreement":null},{"id":"W4224214644","doi":"10.1155/2022/2286147","title":"Traffic Incident Detection Based on mmWave Radar and Improvement Using Fusion with Camera","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Radar; Computer science; Robustness (evolution); Extremely high frequency; Sensor fusion; Real-time computing; Intelligent transportation system; Artificial intelligence; Engineering; Transport engineering; Telecommunications","score_opus":0.0039667466362023306,"score_gpt":0.19108485713733944,"score_spread":0.1871181105011371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224214644","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":[0.9647038,0.00008263122,0.034857355,0.000042036307,0.00014471961,0.00011026316,0.000005112225,0.000048480815,0.000005588293],"genre_scores_gemma":[0.99610746,0.000035037745,0.003778796,0.000035085188,0.000016316624,0.0000050581934,0.0000049080104,0.000015755819,0.0000015593761],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993885,0.000010909004,0.00024965624,0.00007633896,0.00018293463,0.000091638416],"domain_scores_gemma":[0.999725,0.0000144156265,0.00014256552,0.00005610839,0.00003163977,0.000030283421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011830318,0.00008688341,0.00012174782,0.00015025513,0.00012862256,0.0000039077613,0.00003907089,0.000031346026,0.000014755622],"category_scores_gemma":[0.0000015482639,0.000080812824,0.000031568645,0.00012787103,0.000016199105,0.00012849318,0.0000011074983,0.00028729808,8.925798e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027654984,0.000025060714,0.00015047073,0.00001541978,0.000013994721,0.000027581846,0.00029631448,0.8680181,0.052761924,0.0000056323843,4.7125314e-7,0.07840851],"study_design_scores_gemma":[0.009595299,0.008229084,0.20993665,0.00021335295,0.00032446178,0.00020606857,0.0048727295,0.64076406,0.12364308,0.0002154602,0.0012386256,0.000761115],"about_ca_topic_score_codex":0.0000022203715,"about_ca_topic_score_gemma":0.000028271475,"teacher_disagreement_score":0.22725399,"about_ca_system_score_codex":0.00017211861,"about_ca_system_score_gemma":0.00002418676,"threshold_uncertainty_score":0.32954514},"labels":[],"label_agreement":null},{"id":"W4224227227","doi":"10.1155/2022/1188089","title":"A Systematic Review of Autonomous Emergency Braking System: Impact Factor, Technology, and Performance Evaluation","year":2022,"lang":"en","type":"review","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Shaanxi Province Postdoctoral Science Foundation; Chang'an University; National Natural Science Foundation of China","keywords":"Key (lock); Field (mathematics); Test (biology); Computer science; Systems engineering; Risk analysis (engineering); Engineering; Transport engineering; Computer security; Business","score_opus":0.02141343540304674,"score_gpt":0.3070732518913197,"score_spread":0.28565981648827293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224227227","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":[0.0026522493,0.9950821,0.00044477577,0.0000035038158,0.0003197087,0.0012988903,0.000049913007,0.0001153284,0.00003355228],"genre_scores_gemma":[0.029273825,0.9701711,0.00032209198,0.0000010392396,0.00001706013,0.000117423064,0.00004777489,0.00004699267,0.0000027133328],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9967899,0.00009304283,0.0024216692,0.00015507347,0.000366261,0.00017410774],"domain_scores_gemma":[0.99726605,0.00006211152,0.0021892698,0.0002540229,0.00018607991,0.000042440868],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008442515,0.00033282294,0.0022692834,0.0007754458,0.00006805015,0.0000033261588,0.0002696986,0.00025724474,0.0000734948],"category_scores_gemma":[0.000057432087,0.00026956154,0.00043677283,0.0007922532,0.000031551677,0.00029493426,0.0000055269743,0.0006775032,0.000001156741],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033458502,0.0000094744,0.000011494472,0.7041068,0.0003278921,0.000006025043,0.000076878496,0.0017714546,0.000002493815,0.00004763598,0.0000014329124,0.29363507],"study_design_scores_gemma":[0.00040309932,0.0004195251,0.00040938807,0.9829674,0.007510825,0.0005080615,0.00022598343,0.0010451229,0.000008847024,0.000037295827,0.0059725777,0.00049187936],"about_ca_topic_score_codex":5.350284e-7,"about_ca_topic_score_gemma":0.0000012220466,"teacher_disagreement_score":0.29314318,"about_ca_system_score_codex":0.00051529205,"about_ca_system_score_gemma":0.00019123087,"threshold_uncertainty_score":0.9999757},"labels":[],"label_agreement":null},{"id":"W4224280689","doi":"10.3390/s22083040","title":"Survey of Cooperative Advanced Driver Assistance Systems: From a Holistic and Systemic Vision","year":2022,"lang":"en","type":"review","venue":"Sensors","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":"Concordia University","funders":"Comisión Nacional de Investigación Científica y Tecnológica; Agencia Nacional de Investigación y Desarrollo","keywords":"Advanced driver assistance systems; Computer science; Principal (computer security); Architecture; Systems engineering; The Internet; Human–computer interaction; Work (physics); State (computer science); Engineering; Artificial intelligence; Computer security; World Wide Web","score_opus":0.033728685314047886,"score_gpt":0.28465542878017613,"score_spread":0.25092674346612825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224280689","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":[0.007473301,0.98983985,0.00013417882,5.214245e-7,0.0004238576,0.0005235252,0.0011461579,0.00024621031,0.00021241636],"genre_scores_gemma":[0.06841611,0.9311069,0.000034520366,8.9266894e-7,0.00001379318,0.0000530507,0.00019992363,0.000059662078,0.000115135306],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998426,0.00035079723,0.00057410204,0.00033696025,0.00012610723,0.00018598443],"domain_scores_gemma":[0.9986634,0.00065029523,0.00020761132,0.00039679446,0.00004073328,0.000041129842],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025929688,0.00032536977,0.0015354266,0.00012207289,0.00007230239,0.000011453287,0.00019832251,0.0003298178,0.000032382213],"category_scores_gemma":[0.000102853155,0.00029599198,0.00008425717,0.00029508641,0.00009834588,0.00003777821,0.00007397896,0.0005090708,0.000016542317],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028487093,0.00003824409,0.000096194475,0.03358555,0.0010196043,0.00011995611,0.00037654655,0.025192903,0.000014405051,0.0006060813,0.00019271157,0.93872935],"study_design_scores_gemma":[0.0007286135,0.0001701199,0.0017898052,0.02604691,0.00091424666,0.00013434337,0.00044503753,0.02713331,0.000005615819,0.000016498683,0.9410133,0.0016022138],"about_ca_topic_score_codex":0.00014385574,"about_ca_topic_score_gemma":0.000051867886,"teacher_disagreement_score":0.9408206,"about_ca_system_score_codex":0.00020606689,"about_ca_system_score_gemma":0.00005624355,"threshold_uncertainty_score":0.9999492},"labels":[],"label_agreement":null},{"id":"W4225001138","doi":"10.4271/12-06-01-0006","title":"Validation on Safety of the Intended Functionality of Automated Vehicles: Concept Development","year":2022,"lang":"en","type":"article","venue":"SAE International Journal of Connected and Automated 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":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"","keywords":"Systems engineering; Computer science; Engineering; Transport engineering; Software engineering","score_opus":0.008847804137560296,"score_gpt":0.22828632373345653,"score_spread":0.21943851959589625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225001138","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":[0.9974219,0.00021960722,0.00017456058,0.0005382633,0.00070675917,0.00011198895,0.00009193679,0.00043810095,0.00029684952],"genre_scores_gemma":[0.9994775,0.000034139623,0.00031659097,0.00007604191,0.00003118253,0.0000051426473,0.000026648124,0.000016072772,0.000016653661],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99821967,0.00013404785,0.00087644125,0.00011769773,0.0005176559,0.00013450287],"domain_scores_gemma":[0.99868613,0.00021401588,0.0005213917,0.00012955653,0.00041095793,0.000037930524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000448726,0.00015017735,0.00028715277,0.00023644812,0.00014205869,0.000011911359,0.00044975002,0.00009351104,0.00011478683],"category_scores_gemma":[0.00011405886,0.00012150008,0.00010533161,0.00026029276,0.0001641044,0.000114896124,0.00012754505,0.00038109173,0.0000013345738],"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":[0.0021423476,0.001123283,0.020799723,0.00019987233,0.0047846655,0.00007232917,0.006243103,0.38035423,0.43071425,0.025933253,0.005325324,0.122307636],"study_design_scores_gemma":[0.0038306916,0.00039131942,0.4682625,0.0003764695,0.0000974275,0.00030979278,0.0016801214,0.13218375,0.3843882,0.0018383085,0.0062628333,0.0003785821],"about_ca_topic_score_codex":0.00000731688,"about_ca_topic_score_gemma":0.0000026388504,"teacher_disagreement_score":0.44746277,"about_ca_system_score_codex":0.00018085161,"about_ca_system_score_gemma":0.00015136562,"threshold_uncertainty_score":0.49546292},"labels":[],"label_agreement":null},{"id":"W4225384347","doi":"10.32473/flairs.v35i.130629","title":"Estimating Automobile Crash Characteristics from Images using Deep Learning","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Crash; Collision; Artificial intelligence; Deep learning; Computer science; Machine learning; Simulation; Engineering; Computer security","score_opus":0.06565464327735158,"score_gpt":0.3190429977632977,"score_spread":0.2533883544859461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225384347","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":[0.9708507,0.000070548325,0.02474676,0.00060210517,0.0012254512,0.00028511666,0.00007153165,0.00029772724,0.0018500265],"genre_scores_gemma":[0.9917303,0.000060057962,0.0076605985,0.0000193327,0.00028926958,0.000075107666,0.00001086853,0.000030881547,0.00012362399],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771506,0.000030235067,0.00052890764,0.00033473974,0.00094617496,0.0004449075],"domain_scores_gemma":[0.99861765,0.0002705334,0.00016514635,0.00014275181,0.0007437642,0.000060132283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013583223,0.00017687138,0.0002237132,0.000102674356,0.0008931436,0.00015714603,0.0016208509,0.000105873056,0.0006304391],"category_scores_gemma":[0.0005178555,0.00017350123,0.00017101232,0.0005210078,0.00043078788,0.00027458352,0.0010864602,0.0016860861,0.000017553137],"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":[0.000072146475,0.00016510539,0.013286453,0.00013819693,0.00041649566,0.000004101414,0.008513815,0.1083799,0.70783895,0.05435707,0.0008288834,0.10599886],"study_design_scores_gemma":[0.000023465687,0.00003231558,0.00050016184,0.000049573588,0.00001078913,0.0000051274287,0.004534036,0.85359675,0.114210956,0.026458152,0.0004216828,0.0001569735],"about_ca_topic_score_codex":0.0001160867,"about_ca_topic_score_gemma":0.0000035656078,"teacher_disagreement_score":0.74521685,"about_ca_system_score_codex":0.00047368385,"about_ca_system_score_gemma":0.000105743966,"threshold_uncertainty_score":0.7325299},"labels":[],"label_agreement":null},{"id":"W4225410237","doi":"10.1007/s10489-022-03468-6","title":"Improving environmental awareness for autonomous vehicles","year":2022,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Human–computer interaction","score_opus":0.009911229993568398,"score_gpt":0.20315985684936008,"score_spread":0.1932486268557917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225410237","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":[0.4744235,0.00057909644,0.51922184,0.000068956964,0.00047093953,0.0007692244,0.000119448785,0.0015650338,0.0027819774],"genre_scores_gemma":[0.9968267,0.000019903715,0.0022340834,0.000079739875,0.000032818087,0.00064341805,0.000029275036,0.000039649414,0.00009442561],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914503,0.0000062062763,0.00021496495,0.00024040333,0.00009305015,0.00030034638],"domain_scores_gemma":[0.9996129,0.00006467057,0.000034296783,0.00024862372,0.0000027849587,0.000036705056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001498946,0.00014530515,0.00014103149,0.0000623013,0.0003601026,0.000010015498,0.00035993205,0.000070049646,0.0002112425],"category_scores_gemma":[0.000003297467,0.00017445323,0.000050842173,0.00009003473,0.00007339408,0.000040559313,0.00015065707,0.00026183765,0.00005309916],"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":[0.000042355947,0.00007690245,0.00038281461,0.000049163507,0.000052877538,0.000005661353,0.00057359395,0.16289955,0.078604415,0.034221906,0.00030017493,0.7227906],"study_design_scores_gemma":[0.000278816,0.00015859872,0.00054737885,0.0000031833165,0.000035089404,0.000034276607,0.0022970017,0.47033152,0.46407956,0.012954308,0.048467644,0.00081261824],"about_ca_topic_score_codex":0.000006909583,"about_ca_topic_score_gemma":0.0000017170878,"teacher_disagreement_score":0.72197795,"about_ca_system_score_codex":0.00019653703,"about_ca_system_score_gemma":0.000021756387,"threshold_uncertainty_score":0.7113997},"labels":[],"label_agreement":null},{"id":"W4226144994","doi":"10.1109/iv51971.2022.9827450","title":"Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Intelligent Vehicles Symposium (IV)","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":"","keywords":"Computer science; Discriminative model; Artificial intelligence; Generator (circuit theory); Domain (mathematical analysis); Domain adaptation; Machine learning; Consistency (knowledge bases); Class (philosophy); Labeled data; Adversarial system; Synthetic data; Generative grammar; Baseline (sea); Pattern recognition (psychology); Adaptation (eye); Data mining; Power (physics)","score_opus":0.014741755755094517,"score_gpt":0.22797299879361904,"score_spread":0.21323124303852453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226144994","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":[0.91455346,0.00009853992,0.08223729,0.00058294705,0.0005778065,0.00081263954,0.00003365597,0.0005656725,0.000537972],"genre_scores_gemma":[0.99767226,0.00013603823,0.0010144498,0.0000625237,0.000073689735,0.0007539859,0.000035841273,0.000057569898,0.0001936138],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985183,0.00008129916,0.00045820727,0.00039598698,0.00017217948,0.00037404386],"domain_scores_gemma":[0.99942094,0.00014540207,0.00007469041,0.00025105462,0.00002717457,0.000080753816],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00053549826,0.00021506057,0.00023385076,0.0003820171,0.00029640275,0.00003125632,0.00021902347,0.00014789922,0.00002872925],"category_scores_gemma":[0.000012826698,0.0002673774,0.0000638956,0.000371033,0.000042149826,0.00011999097,0.000076239485,0.00041144193,0.000023426697],"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":[0.00020218248,0.00012227848,0.0059421565,0.00008522612,0.00006412865,0.000011350088,0.0041236617,0.3373656,0.58125347,0.00254999,0.00017810323,0.06810188],"study_design_scores_gemma":[0.0005582582,0.00044081957,0.014947559,0.000019458099,0.000030605384,0.000032485957,0.0023572992,0.93654287,0.02983929,0.0025821796,0.012136107,0.00051305734],"about_ca_topic_score_codex":0.00009024552,"about_ca_topic_score_gemma":0.0007314525,"teacher_disagreement_score":0.5991773,"about_ca_system_score_codex":0.0006690452,"about_ca_system_score_gemma":0.000031217125,"threshold_uncertainty_score":0.9999778},"labels":[],"label_agreement":null},{"id":"W4226188445","doi":"10.1109/robio54168.2021.9739455","title":"Robot Navigation with Interaction-based Deep Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Robotics and Biomimetics (ROBIO)","topic":"Autonomous Vehicle Technology and Safety","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":"Artificial Intelligence in Medicine (Canada)","funders":"Research and Development; Fundamental Research Funds for the Central Universities; Graduate Research and Innovation Projects of Jiangsu Province; National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Robot; Artificial intelligence; Representation (politics); Mobile robot navigation; Human–computer interaction; Robot learning; Collision avoidance; Motion planning; Mobile robot; Computer vision; Collision; Robot control; Computer security","score_opus":0.021020171947378388,"score_gpt":0.25149805970115996,"score_spread":0.23047788775378159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226188445","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":[0.09844632,0.00011185294,0.8766818,0.0038117485,0.001868863,0.00026712412,0.000017421287,0.00038468276,0.018410204],"genre_scores_gemma":[0.9933289,0.00016558608,0.005622365,0.00009490806,0.000058396254,0.000009998675,0.00020626944,0.000020975689,0.00049254287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991205,0.000022463399,0.00025313455,0.0002449138,0.00017765845,0.00018138527],"domain_scores_gemma":[0.99941087,0.000058979364,0.00007169912,0.00015755791,0.00024138107,0.000059498365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008349853,0.00018667693,0.00016380243,0.00011993644,0.000101473495,0.00010257163,0.00013325835,0.00013681017,0.00032188097],"category_scores_gemma":[0.00001685635,0.00017788743,0.000042591495,0.00014260477,0.00008411513,0.0000745213,0.000030231973,0.00036581792,0.00004612114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032726985,0.00004441047,0.0003468112,0.000020845375,0.00012621842,0.000035449404,0.000051686395,0.950176,0.024409175,0.01945799,0.000030871346,0.005267828],"study_design_scores_gemma":[0.0005109598,0.00013277707,0.00015795787,0.0001413258,0.000029479279,0.000024928717,0.00016867724,0.9397562,0.0579406,0.00032207288,0.00057327293,0.00024173995],"about_ca_topic_score_codex":0.0000061432493,"about_ca_topic_score_gemma":0.000021282349,"teacher_disagreement_score":0.8948826,"about_ca_system_score_codex":0.00010090182,"about_ca_system_score_gemma":0.000060817805,"threshold_uncertainty_score":0.72540385},"labels":[],"label_agreement":null},{"id":"W4226198919","doi":"10.1109/tgcn.2022.3162698","title":"Green-Tech CAV: Next Generation Computing for Traffic Sign and Obstacle Detection in Connected and Autonomous Vehicles","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Green Communications and Networking","topic":"Autonomous Vehicle Technology and Safety","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":"University of New Brunswick","funders":"","keywords":"Obstacle; Computer science; Traffic sign recognition; Real-time computing; Traffic sign; Intelligent transportation system; Advanced driver assistance systems; Sign (mathematics); Artificial intelligence; Transport engineering; Engineering","score_opus":0.03874289677801138,"score_gpt":0.23357302534354762,"score_spread":0.19483012856553625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226198919","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":[0.7877269,0.0026189885,0.20845905,0.0003319946,0.00012273052,0.00039087998,0.000017034705,0.00031298917,0.000019440413],"genre_scores_gemma":[0.9972382,0.0010027095,0.0014535565,0.000058977548,0.000032208056,0.00016047162,0.000012244294,0.000028853654,0.000012746321],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916506,0.0000954169,0.0002725705,0.00021033092,0.00005351955,0.0002031147],"domain_scores_gemma":[0.9992946,0.0002850819,0.000049089118,0.00031274764,0.000018848736,0.000039658815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000278898,0.0001444404,0.00017459567,0.00019405653,0.0012260618,0.000037509053,0.00014821647,0.00010857605,0.000003170223],"category_scores_gemma":[4.941639e-7,0.00018146564,0.000028853574,0.00028446576,0.0001035718,0.0001156986,0.00001431048,0.00048288188,2.5449881e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017592838,0.000039707367,0.00007231682,0.000018159853,0.0000311025,6.7664837e-7,0.0006533539,0.03821333,0.0038672904,0.00003945149,0.0000036811005,0.95704335],"study_design_scores_gemma":[0.0005536644,0.00015319108,0.00031890546,0.000017551736,0.000029956336,0.000036859357,0.0002890815,0.99644965,0.0006062731,0.0001176221,0.0012408713,0.00018638579],"about_ca_topic_score_codex":0.00019846819,"about_ca_topic_score_gemma":0.0023373268,"teacher_disagreement_score":0.95823634,"about_ca_system_score_codex":0.00009850603,"about_ca_system_score_gemma":0.000013150795,"threshold_uncertainty_score":0.943},"labels":[],"label_agreement":null},{"id":"W4226323855","doi":"10.1109/tits.2022.3154926","title":"Tracking Dependent Extended Targets Using Multi-Output Spatiotemporal Gaussian Processes","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nipissing University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Clutter; Artificial intelligence; Computer science; Gaussian process; Pattern recognition (psychology); Kalman filter; Gaussian; Cluster analysis; Kernel (algebra); Bayesian probability; Kinematics; Computer vision; Mathematics; Radar","score_opus":0.036191997668830016,"score_gpt":0.2537266786318408,"score_spread":0.21753468096301076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226323855","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":[0.13111249,0.00051030447,0.8646916,0.00002196789,0.0016865722,0.0006300682,0.00028761875,0.0010054571,0.0000539052],"genre_scores_gemma":[0.99837506,0.00008710883,0.00075301994,0.000017648874,0.000033033426,0.000348253,0.00005633159,0.00008482368,0.00024469526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979556,0.00007370406,0.0007798663,0.00039360457,0.00041879434,0.00037842945],"domain_scores_gemma":[0.9993527,0.000049406062,0.00012450066,0.0002797398,0.00009295657,0.00010072216],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024723983,0.00032004353,0.0003301139,0.00039643352,0.0005986649,0.00004088488,0.00025174717,0.00016513132,0.00020012421],"category_scores_gemma":[0.0000017334866,0.0003671305,0.0001363787,0.00051514746,0.00005084288,0.0002486515,4.9489324e-7,0.00063575397,0.0000289381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004917337,0.00022956386,0.00021213735,0.00023104995,0.00013512619,0.000037241913,0.002044079,0.98942095,0.0016182917,0.00007921093,0.000008767108,0.005934422],"study_design_scores_gemma":[0.0018131046,0.00041608946,0.002543188,0.00024541392,0.0003416606,0.0001647784,0.010931569,0.71659905,0.26091617,0.00008107162,0.0041725948,0.0017753082],"about_ca_topic_score_codex":0.00019127029,"about_ca_topic_score_gemma":0.0003340066,"teacher_disagreement_score":0.8672626,"about_ca_system_score_codex":0.00036680757,"about_ca_system_score_gemma":0.00009850265,"threshold_uncertainty_score":0.99987805},"labels":[],"label_agreement":null},{"id":"W4229455978","doi":"10.18280/ts.390207","title":"A Novel Fast Responding Driver Assistance Technique with Efficient Lane Detection and Collision Avoidance Using Dynamic Feature Extraction in Any Environment","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Savitribai Phule Pune University","keywords":"Collision; Computer science; Collision avoidance; Feature (linguistics); Lane departure warning system; Real-time computing; ALARM; Collision avoidance system; Simulation; Artificial intelligence; Computer vision; Computer security; Engineering","score_opus":0.004869281634551205,"score_gpt":0.19432894517608426,"score_spread":0.18945966354153307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229455978","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":[0.6264693,0.00009355234,0.37293035,0.000033103297,0.000026912876,0.00029400593,0.000014659516,0.000111468835,0.00002666513],"genre_scores_gemma":[0.99315673,0.000017461616,0.0066188076,0.000009344016,0.000008467861,0.00013418216,0.00000619341,0.000022437465,0.000026380045],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916714,0.00003693782,0.00016108988,0.00024461673,0.00018380187,0.00020638263],"domain_scores_gemma":[0.99977744,0.000025710586,0.000057989862,0.00010588744,0.0000053482336,0.000027620325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030051533,0.000146401,0.00013135895,0.00024146236,0.00023827606,0.0000120878585,0.000071069466,0.00008281754,0.000032849108],"category_scores_gemma":[0.0000015204878,0.00015267868,0.000018402132,0.00027210833,0.00004362403,0.00008799939,0.00003918892,0.000444975,7.1350456e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013080926,0.000056029618,0.00048308593,0.000015661497,0.000009923635,0.00001759429,0.0001415139,0.45915794,0.5375547,0.000054925855,0.0000010120551,0.0023768274],"study_design_scores_gemma":[0.0010044301,0.00021752866,0.055339232,0.000064798885,0.000021099326,0.00018554406,0.00030371334,0.9127015,0.028856799,0.000015657091,0.0010054938,0.00028424425],"about_ca_topic_score_codex":0.000009266372,"about_ca_topic_score_gemma":0.000039263894,"teacher_disagreement_score":0.50869787,"about_ca_system_score_codex":0.0007891188,"about_ca_system_score_gemma":0.000011519023,"threshold_uncertainty_score":0.6226056},"labels":[],"label_agreement":null},{"id":"W4230657418","doi":"10.32920/ryerson.14647812.v1","title":"Development Of Framework For In-Vehicle Rear-End Collision Warning System Considering Driver Characteristics","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Collision; Acceleration; Simulation; Advanced driver assistance systems; Automotive engineering; Driving simulator; Warning system; Kinematics; Computer science; Population; Engineering; Computer security; Aerospace engineering; Physics","score_opus":0.017830277955134633,"score_gpt":0.23475084584296885,"score_spread":0.2169205678878342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230657418","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":[0.74016887,0.0001460067,0.25800115,0.000017425908,0.00043444592,0.00034173072,0.000014677807,0.00044578977,0.0004298979],"genre_scores_gemma":[0.7515398,0.00003743508,0.2482094,0.0000057613565,0.000025009069,0.00007869064,0.000044355304,0.00003945243,0.000020052243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984694,0.000019878049,0.0007700389,0.00032887133,0.000121211604,0.00029058955],"domain_scores_gemma":[0.9991433,0.00019467609,0.00015536022,0.00037934782,0.00008405407,0.000043254156],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003152954,0.00025895296,0.00062758644,0.00015225941,0.00007018283,0.000024920108,0.00021079865,0.0008793375,0.000030076124],"category_scores_gemma":[0.0000845884,0.00028980259,0.00008486026,0.00011396218,0.00004166044,0.000041625957,0.0003845352,0.00081099715,0.000004878618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003440628,0.0006037705,0.072644666,0.056027833,0.003244736,0.0006634012,0.05315881,0.2919778,0.08646442,0.14858958,0.00024230524,0.28603864],"study_design_scores_gemma":[0.0013654844,0.00004989269,0.069092155,0.013543521,0.00015014215,0.00003540552,0.008671869,0.59424514,0.3054339,0.0015995726,0.003543065,0.002269883],"about_ca_topic_score_codex":0.0000074568043,"about_ca_topic_score_gemma":0.000026944788,"teacher_disagreement_score":0.30226734,"about_ca_system_score_codex":0.000333452,"about_ca_system_score_gemma":0.00017710362,"threshold_uncertainty_score":0.9999554},"labels":[],"label_agreement":null},{"id":"W4231504151","doi":"10.1109/tvt.2016.2524138","title":"IEEE Transactions on Vehicular Technology publication information","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fiat Chrysler Automobiles; Universidade Estadual de Campinas; Universidade Nova de Lisboa; Xidian University; Beijing Institute of Technology; Indiana University-Purdue University Indianapolis; Yonsei University; National Institute of Standards and Technology; King's College London; McMaster University; Futurewei Technologies; McGill University; Heriot-Watt University; Dartmouth College; Chinese Academy of Sciences; Queen Mary University of London; Syracuse University","keywords":"Computer science; Engineering; Telecommunications; Computer network","score_opus":0.005588814665482827,"score_gpt":0.19395296009305188,"score_spread":0.18836414542756905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231504151","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":[0.096596405,0.00008280529,0.8847186,0.009055055,0.0008104099,0.00064207765,0.00010111786,0.007446423,0.00054708327],"genre_scores_gemma":[0.996774,0.0003723218,0.0014427932,0.00026794226,0.000023298577,0.0007178942,0.000008536561,0.00008730392,0.00030589863],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974398,0.000047151505,0.00079809915,0.0005615989,0.0003255264,0.00082783884],"domain_scores_gemma":[0.9980234,0.000089630914,0.0001489607,0.0013611108,0.00025213885,0.0001247546],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023985215,0.00056055776,0.0005086199,0.0030283108,0.0004861313,0.000038395938,0.0007498359,0.0021346358,0.00028693373],"category_scores_gemma":[0.000019728004,0.0004943141,0.00024644582,0.0021062638,0.0005645721,0.0007720114,0.0000019804752,0.0014190592,0.002067605],"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":[0.000085334344,0.00037820885,0.000026496726,0.00006048376,0.00045194282,0.00002759027,0.00008940073,0.05657523,0.055432554,0.0062176185,0.0005345745,0.8801206],"study_design_scores_gemma":[0.001946701,0.00058846903,0.000051825533,0.0001394475,0.00014560146,0.00023605014,0.00016049288,0.020299358,0.9334031,0.0035506357,0.03853366,0.0009446804],"about_ca_topic_score_codex":0.0000064324204,"about_ca_topic_score_gemma":0.000027358448,"teacher_disagreement_score":0.9001776,"about_ca_system_score_codex":0.00051026256,"about_ca_system_score_gemma":0.000076818644,"threshold_uncertainty_score":0.99975085},"labels":[],"label_agreement":null},{"id":"W4232104377","doi":"10.1504/ijehv.2019.102877","title":"A review of autonomous vehicle technology landscape","year":2019,"lang":"en","type":"review","venue":"International Journal of Electric and Hybrid Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Automotive industry; Distraction; Field (mathematics); Engineering; Autonomous system (mathematics); Computer science; Transport engineering; Artificial intelligence","score_opus":0.013624331062789598,"score_gpt":0.2730713613215522,"score_spread":0.2594470302587626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232104377","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":[0.0003954456,0.99823576,0.00015480975,0.00015387763,0.00036318472,0.00018057063,0.000025663341,0.00007560294,0.0004151024],"genre_scores_gemma":[0.0038075917,0.9957555,0.00014149334,0.00006443447,0.00013547916,0.000006636849,0.00000952511,0.00003895644,0.000040367762],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99826676,0.000036840745,0.0011102677,0.00015885851,0.0002203501,0.00020691403],"domain_scores_gemma":[0.99864566,0.00014351,0.00070200453,0.00018908217,0.00027088873,0.000048829803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000329273,0.00027548592,0.001462747,0.000896066,0.000021159141,0.000013670691,0.00073079125,0.00024388585,0.000040104936],"category_scores_gemma":[0.00007842488,0.00022696318,0.00034746152,0.00033723284,0.000065234715,0.00010432692,0.00008089944,0.00078680634,0.000020404092],"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":[0.0000057501725,0.000027466658,0.000024819938,0.01216654,0.0006499625,0.000060774757,0.0000038135445,0.000007217139,0.000013146859,0.00045425186,0.001937087,0.9846492],"study_design_scores_gemma":[0.00028714456,0.00014784478,0.000010763128,0.035182606,0.0005748587,0.0024923128,0.000003594941,0.00044059884,0.00013829932,0.00042898493,0.9600409,0.00025206007],"about_ca_topic_score_codex":0.0000010121253,"about_ca_topic_score_gemma":1.8415706e-7,"teacher_disagreement_score":0.9843971,"about_ca_system_score_codex":0.00010280374,"about_ca_system_score_gemma":0.00024046624,"threshold_uncertainty_score":0.925529},"labels":[],"label_agreement":null},{"id":"W4233934963","doi":"10.32920/ryerson.14644536.v1","title":"Development of framework for in-vehicle collision warning system considering driver characteristics","year":2021,"lang":"en","type":"preprint","venue":"","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":"Toronto Metropolitan University","funders":"","keywords":"Collision; Computer science; Simulation; Reliability (semiconductor); Data collection; Monte Carlo method; Global Positioning System; Acceleration; Warning system; Real-time computing; Computer security; Telecommunications","score_opus":0.017391487902569988,"score_gpt":0.23418545370394897,"score_spread":0.216793965801379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233934963","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":[0.66566545,0.00012933102,0.3328903,0.000012269852,0.00039503953,0.0002826776,0.000009409514,0.0003836583,0.00023189718],"genre_scores_gemma":[0.73381513,0.000030529547,0.26597208,0.0000055321675,0.000020884565,0.00007866124,0.000032356336,0.000032684533,0.000012128215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864334,0.000015115425,0.00070484035,0.0002813609,0.00009875418,0.0002566077],"domain_scores_gemma":[0.9992324,0.00019802873,0.00013386822,0.00032481377,0.00007478262,0.00003608097],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002499531,0.00023131944,0.00057255797,0.00013694259,0.000058360052,0.000019969993,0.00019364279,0.0007961273,0.000017532653],"category_scores_gemma":[0.00008303972,0.00025939697,0.000078071025,0.0000986315,0.00003165878,0.000034204448,0.00034353716,0.00069206476,0.0000039021743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022637084,0.0004906891,0.06259598,0.053162776,0.0025831119,0.00041001433,0.04664457,0.38868207,0.06411779,0.13438529,0.00016891558,0.24653241],"study_design_scores_gemma":[0.0010199087,0.00003168242,0.037012216,0.010358867,0.00010402205,0.000018601522,0.006836915,0.67168546,0.26730898,0.0011740439,0.002751108,0.0016981981],"about_ca_topic_score_codex":0.0000039384545,"about_ca_topic_score_gemma":0.000018846757,"teacher_disagreement_score":0.2830034,"about_ca_system_score_codex":0.0002965277,"about_ca_system_score_gemma":0.0001534788,"threshold_uncertainty_score":0.9999858},"labels":[],"label_agreement":null},{"id":"W4234297828","doi":"10.1504/ijvas.2019.099831","title":"Path planning and re-planning of lane change manoeuvres in dynamic traffic environments","year":2019,"lang":"en","type":"article","venue":"International Journal of Vehicle Autonomous Systems","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":"University of Alberta","funders":"","keywords":"Motion planning; Acceleration; Path (computing); Engineering; Quintic function; Control theory (sociology); Tangent; Simulation; Computer science; Control engineering; Mathematics; Nonlinear system; Artificial intelligence; Robot; Control (management)","score_opus":0.01214548992483486,"score_gpt":0.23807075575431416,"score_spread":0.2259252658294793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234297828","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":[0.99426174,0.0039387927,0.00039911646,0.000074211806,0.0007881427,0.00014515939,0.000013307646,0.00004092697,0.0003386225],"genre_scores_gemma":[0.9995937,0.000104708735,0.00012968517,0.000015344549,0.00007127844,0.000005606435,0.0000045309594,0.00002262299,0.000052521722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879515,0.000034503206,0.0006295576,0.00012161903,0.00023999922,0.00017915605],"domain_scores_gemma":[0.99946487,0.000070410955,0.00027903815,0.00011367032,0.000028725124,0.000043311586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032738346,0.00013908795,0.00033011948,0.00033809445,0.00001587894,0.000020026868,0.00030893352,0.00014284137,0.000014677244],"category_scores_gemma":[0.0000081524395,0.00013722191,0.000052704567,0.00006311389,0.000038260234,0.00023307772,0.00004272268,0.00030301732,0.000010566592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023478022,0.0002234945,0.37342763,0.0003473195,0.0007103687,0.00085676694,0.009015193,0.5485997,0.036315158,0.00059168396,0.00008053947,0.029597363],"study_design_scores_gemma":[0.0028105304,0.00041118,0.41051024,0.0016312973,0.00003211115,0.0006832899,0.0018561002,0.57701904,0.0013044738,0.00010873096,0.0032057564,0.000427257],"about_ca_topic_score_codex":0.000027183163,"about_ca_topic_score_gemma":0.0000037821183,"teacher_disagreement_score":0.037082627,"about_ca_system_score_codex":0.00019442565,"about_ca_system_score_gemma":0.000017930959,"threshold_uncertainty_score":0.5595747},"labels":[],"label_agreement":null},{"id":"W4234502368","doi":"10.32920/ryerson.14660670","title":"Development Of In-Vehicle Collision Warning System For Intersections","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Federal Highway Administration; U.S. Department of Transportation","keywords":"Intersection (aeronautics); Collision; Acceleration; Warning system; Computer science; Lane departure warning system; Simulation; Reliability (semiconductor); Real-time computing; Engineering; Computer security; Artificial intelligence; Transport engineering; Telecommunications","score_opus":0.014679561408735253,"score_gpt":0.2271598311676616,"score_spread":0.21248026975892637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234502368","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":[0.7947199,0.00012556755,0.20230265,0.000011474112,0.0004353744,0.00025427033,0.000004655413,0.00044338254,0.0017027499],"genre_scores_gemma":[0.9566863,0.000011677179,0.043038417,0.0000018130393,0.000011268023,0.0001440397,0.000027411128,0.000020542077,0.00005850581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919105,0.000009192365,0.00042495265,0.00018055626,0.00005084939,0.00014341409],"domain_scores_gemma":[0.99963814,0.000047040037,0.00005140436,0.00019959066,0.000045323355,0.000018530636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017826523,0.00012853956,0.00028530898,0.00015443095,0.000041174302,0.000008148474,0.00013975924,0.0003638582,0.000011222418],"category_scores_gemma":[0.000015459564,0.00013895222,0.00006726728,0.00010096965,0.0000145195345,0.000025663838,0.00020254785,0.00033747175,0.0000026312928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004781558,0.00017131817,0.0035596546,0.008515889,0.00061420654,0.000019879053,0.013683539,0.8619482,0.035170496,0.0043517128,0.00018998534,0.07172733],"study_design_scores_gemma":[0.00067502115,0.000021122558,0.005169109,0.0018991131,0.000035058583,0.000008427262,0.012187558,0.6154675,0.36179224,0.0000790264,0.002115581,0.0005502489],"about_ca_topic_score_codex":0.00001678728,"about_ca_topic_score_gemma":0.00033097842,"teacher_disagreement_score":0.32662177,"about_ca_system_score_codex":0.00031300867,"about_ca_system_score_gemma":0.00009403379,"threshold_uncertainty_score":0.5666307},"labels":[],"label_agreement":null},{"id":"W4237651012","doi":"10.1007/978-3-319-12529-9_4","title":"Curve-Fitting","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Curve fitting; Nonparametric statistics; Population; Randomness; Parametric equation; Learning curve; Parametric statistics; Mathematics; Statistics; Computer science; Geometry","score_opus":0.007900682242455437,"score_gpt":0.1763286103683044,"score_spread":0.16842792812584897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237651012","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000023577142,0.00026043344,0.013902176,0.000055962773,0.00020795198,0.00006368032,0.0000033690915,0.0023291297,0.9831537],"genre_scores_gemma":[0.04380496,0.00012805314,0.0008295709,0.000081355865,0.00016279033,0.0000033090887,0.00001356529,0.00009833685,0.95487803],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99942917,0.0000016431721,0.0001953317,0.00015012965,0.00005816828,0.00016555941],"domain_scores_gemma":[0.99959815,0.00003160258,0.000028342185,0.00029736696,0.000012476569,0.000032070675],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000084563566,0.00021089552,0.0002412751,0.00007735729,0.00003624597,0.0000061968,0.00016428721,0.0006301703,0.0013008994],"category_scores_gemma":[0.0000042543716,0.00021082391,0.000079240635,0.000007760862,0.00004499455,0.000015772895,0.000040475577,0.0004967161,0.0018323751],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.7939226e-7,7.123257e-7,0.000010782045,0.00004478653,0.0000689072,0.000007452597,0.000009809686,0.00025778974,0.000016775817,0.90086627,0.009850058,0.08886599],"study_design_scores_gemma":[0.00006834136,0.000011228418,0.000015666783,0.000040630664,0.000018026458,0.000009550872,0.0000011629168,0.004502971,0.00014578943,0.021303209,0.9735583,0.000325123],"about_ca_topic_score_codex":9.977173e-7,"about_ca_topic_score_gemma":0.000008467371,"teacher_disagreement_score":0.9637082,"about_ca_system_score_codex":0.000038611706,"about_ca_system_score_gemma":0.000007016885,"threshold_uncertainty_score":0.99961203},"labels":[],"label_agreement":null},{"id":"W4239602482","doi":"10.32920/ryerson.14656848.v1","title":"Modelling of Three-Dimensional Intersection Sight Distance","year":2021,"lang":"en","type":"preprint","venue":"","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":"Toronto Metropolitan University","funders":"","keywords":"Intersection (aeronautics); Sight; Tangent; Object (grammar); Computer science; Mathematics; Geometry; Computer vision; Artificial intelligence; Engineering; Transport engineering; Physics","score_opus":0.018819078750919732,"score_gpt":0.19843358595304444,"score_spread":0.1796145072021247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239602482","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":[0.33147863,0.000569025,0.664383,0.000022887874,0.00046316112,0.00006151666,0.0000059256813,0.0003921609,0.0026236887],"genre_scores_gemma":[0.98963594,0.00004050097,0.010128953,0.0000058312453,0.000028806671,0.0000112420485,0.000028892779,0.000022492935,0.00009732235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931735,0.000006943593,0.00026357573,0.00020753889,0.000082572544,0.000122024794],"domain_scores_gemma":[0.99956155,0.00002129103,0.00004435424,0.00030677504,0.00004615711,0.000019903051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006302851,0.00015434895,0.00025135404,0.00006556942,0.000022437125,0.00000699465,0.00013677105,0.00039659487,0.0001533338],"category_scores_gemma":[0.0000026877067,0.00015868644,0.000101505655,0.00006222404,0.000044668996,0.00003873988,0.0001804341,0.00058749976,0.000005708857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046526216,0.000012505259,0.00011631925,0.00011797497,0.00007476917,0.0000044394974,0.00005025456,0.99659884,0.00040912468,0.0014661722,0.00003681511,0.0011081529],"study_design_scores_gemma":[0.00005628962,0.0000054389493,0.00008835702,0.0001208583,0.000015792739,0.0000032269074,0.000021566397,0.9872272,0.009031286,0.0031913402,0.00008639778,0.00015222625],"about_ca_topic_score_codex":0.000058845355,"about_ca_topic_score_gemma":0.00012871748,"teacher_disagreement_score":0.65815735,"about_ca_system_score_codex":0.00008153751,"about_ca_system_score_gemma":0.000029792289,"threshold_uncertainty_score":0.6471045},"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":"W4244433422","doi":"10.1109/cavs.2019.8887839","title":"CAVS 2019 Panel","year":2019,"lang":"en","type":"article","venue":"","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":"Carleton University","funders":"National Institute of Standards and Technology","keywords":"Standardization; Interoperability; Session (web analytics); Reliability (semiconductor); Computer science; Energy consumption; Safety standards; Test (biology); Efficient energy use; Risk analysis (engineering); Engineering management; Reliability engineering; Business; Engineering","score_opus":0.00509107935922335,"score_gpt":0.1635986914692898,"score_spread":0.15850761211006645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244433422","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":[0.7272527,0.00013528048,0.0018884627,0.00016189493,0.0002608288,0.00007693737,0.0000014611429,0.0015810961,0.2686413],"genre_scores_gemma":[0.9902618,0.000019214793,0.00047523426,0.00005972651,0.000011288838,0.0000017805655,0.0000018946563,0.000010180396,0.009158882],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997283,0.0000018351626,0.00006105004,0.000062684456,0.000026455975,0.00011966317],"domain_scores_gemma":[0.9998005,0.000009785489,0.000003746642,0.00016577191,0.0000041529174,0.000016013162],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000030079473,0.000051535586,0.00006534422,0.000024812054,0.000010487274,0.0000027220512,0.00008253749,0.000087882945,0.0009711181],"category_scores_gemma":[0.0000011133109,0.000046422778,0.00001840369,0.000043775904,0.000009966597,0.000040450494,0.000014626836,0.000098139346,0.005458243],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026666788,0.00009764277,0.19620888,0.00019060532,0.00033905078,0.000039764163,0.00062021957,0.044988032,0.089569,0.30582264,0.13570447,0.22639301],"study_design_scores_gemma":[0.0016482732,0.00015425768,0.24626112,0.000025707373,0.000026149708,0.00006765395,0.00025208975,0.26660448,0.07323854,0.0076273456,0.4027623,0.0013320737],"about_ca_topic_score_codex":0.000007268132,"about_ca_topic_score_gemma":0.000003472307,"teacher_disagreement_score":0.2981953,"about_ca_system_score_codex":0.000014254086,"about_ca_system_score_gemma":0.0000035932603,"threshold_uncertainty_score":0.9999421},"labels":[],"label_agreement":null},{"id":"W4246632638","doi":"10.32920/ryerson.14644536","title":"Development of framework for in-vehicle collision warning system considering driver characteristics","year":2021,"lang":"en","type":"preprint","venue":"","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":"Toronto Metropolitan University","funders":"","keywords":"Collision; Computer science; Simulation; Reliability (semiconductor); Data collection; Global Positioning System; Monte Carlo method; Acceleration; Warning system; Real-time computing; Computer security; Telecommunications","score_opus":0.017391487902569988,"score_gpt":0.23418545370394897,"score_spread":0.216793965801379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246632638","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":[0.66566545,0.00012933102,0.3328903,0.000012269852,0.00039503953,0.0002826776,0.000009409514,0.0003836583,0.00023189718],"genre_scores_gemma":[0.73381513,0.000030529547,0.26597208,0.0000055321675,0.000020884565,0.00007866124,0.000032356336,0.000032684533,0.000012128215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864334,0.000015115425,0.00070484035,0.0002813609,0.00009875418,0.0002566077],"domain_scores_gemma":[0.9992324,0.00019802873,0.00013386822,0.00032481377,0.00007478262,0.00003608097],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002499531,0.00023131944,0.00057255797,0.00013694259,0.000058360052,0.000019969993,0.00019364279,0.0007961273,0.000017532653],"category_scores_gemma":[0.00008303972,0.00025939697,0.000078071025,0.0000986315,0.00003165878,0.000034204448,0.00034353716,0.00069206476,0.0000039021743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022637084,0.0004906891,0.06259598,0.053162776,0.0025831119,0.00041001433,0.04664457,0.38868207,0.06411779,0.13438529,0.00016891558,0.24653241],"study_design_scores_gemma":[0.0010199087,0.00003168242,0.037012216,0.010358867,0.00010402205,0.000018601522,0.006836915,0.67168546,0.26730898,0.0011740439,0.002751108,0.0016981981],"about_ca_topic_score_codex":0.0000039384545,"about_ca_topic_score_gemma":0.000018846757,"teacher_disagreement_score":0.2830034,"about_ca_system_score_codex":0.0002965277,"about_ca_system_score_gemma":0.0001534788,"threshold_uncertainty_score":0.9999858},"labels":[],"label_agreement":null},{"id":"W4248759410","doi":"10.32920/ryerson.14660697","title":"Deep vision pipeline for self-driving cars based on machine learning methods","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pipeline (software); Computer science; Code (set theory); Architecture; Artificial intelligence; Deep learning; Source code","score_opus":0.00967020365272043,"score_gpt":0.2846368351736561,"score_spread":0.2749666315209357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248759410","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":[0.0041237194,0.0006476783,0.9856908,0.00024910585,0.00068313617,0.0004140821,0.0000057486905,0.0039652213,0.0042205406],"genre_scores_gemma":[0.5027813,0.00015869009,0.49633658,0.00008689588,0.000070340495,0.00008899463,0.00018976803,0.00009894452,0.00018850721],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983805,0.00013604129,0.00042970417,0.0005474278,0.00012496341,0.0003813801],"domain_scores_gemma":[0.99862444,0.00055096817,0.000082733364,0.0005961453,0.00007276253,0.00007297555],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00082927983,0.0004132884,0.0005645784,0.00023223554,0.00014334127,0.000049997943,0.0003217,0.00088374026,0.0001923328],"category_scores_gemma":[0.00019652746,0.0004169676,0.00027196086,0.00013361801,0.000023640809,0.000042412186,0.00029253343,0.0017314757,0.000009855062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008504752,0.00003967867,0.00027176956,0.00023587157,0.00007646915,0.00000811923,0.000091848706,0.8798296,0.00066277554,0.0002028286,0.00007662479,0.11849594],"study_design_scores_gemma":[0.00035245443,0.000049462054,0.00020818286,0.00013314355,0.00007183728,0.0000029362518,0.00004136387,0.9868095,0.007047177,0.00025639613,0.0046393783,0.00038818998],"about_ca_topic_score_codex":0.000018953579,"about_ca_topic_score_gemma":0.000068762914,"teacher_disagreement_score":0.49865755,"about_ca_system_score_codex":0.00020916785,"about_ca_system_score_gemma":0.000057030316,"threshold_uncertainty_score":0.9998282},"labels":[],"label_agreement":null},{"id":"W4249948109","doi":"10.1108/oxan-db230720","title":"US fatality could slow down self-driving car testing","year":2018,"lang":"en","type":"other","venue":"Emerald expert briefings","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Self driving; Permission; Pedestrian; State (computer science); Engineering; Computer security; Aeronautics; Transport engineering; Business; Political science; Computer science; Law","score_opus":0.010198949045848333,"score_gpt":0.22024856677019516,"score_spread":0.21004961772434683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249948109","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031825642,0.0083548,0.0068531763,0.013368786,0.0022935704,0.0015199323,0.00013462751,0.05403617,0.9102564],"genre_scores_gemma":[0.020352574,0.0031784247,0.1296884,0.09521485,0.010388863,0.00068947533,0.0006269514,0.009426553,0.73043394],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979828,0.000028436889,0.00043479804,0.0006019907,0.00023047392,0.00072152],"domain_scores_gemma":[0.9988208,0.000058915386,0.00014388877,0.00080533075,0.000044389562,0.00012672317],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00015720367,0.0006256174,0.00063608,0.00018152045,0.00018037013,0.00004877791,0.0005532444,0.0011174282,0.0016516709],"category_scores_gemma":[0.000072574316,0.0006810981,0.00013886207,0.00027933528,0.00018570003,0.00010479013,0.00016777059,0.00066498906,0.00079546985],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014439483,0.000020321319,0.00014485215,0.00009177088,0.00018522392,0.000029380499,0.00024811924,0.00002610255,0.0007258523,0.000088153705,0.9956881,0.002750724],"study_design_scores_gemma":[0.00023688811,0.000029057222,0.00027786885,0.0002506528,0.000029348397,0.000034151442,0.0000033464037,0.0037428327,0.00081418274,0.000064519474,0.99374,0.00077714224],"about_ca_topic_score_codex":0.0114389695,"about_ca_topic_score_gemma":0.00024138344,"teacher_disagreement_score":0.17982246,"about_ca_system_score_codex":0.00017282218,"about_ca_system_score_gemma":0.000046589314,"threshold_uncertainty_score":0.99998254},"labels":[],"label_agreement":null},{"id":"W4250118282","doi":"10.32920/ryerson.14656848","title":"Modelling of Three-Dimensional Intersection Sight Distance","year":2021,"lang":"en","type":"preprint","venue":"","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":"Toronto Metropolitan University","funders":"","keywords":"Intersection (aeronautics); Sight; Tangent; Object (grammar); Computer science; Geometry; Mathematics; Computer vision; Artificial intelligence; Engineering; Physics; Transport engineering; Optics","score_opus":0.018819078750919732,"score_gpt":0.19843358595304444,"score_spread":0.1796145072021247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250118282","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":[0.33147863,0.000569025,0.664383,0.000022887874,0.00046316112,0.00006151666,0.0000059256813,0.0003921609,0.0026236887],"genre_scores_gemma":[0.98963594,0.00004050097,0.010128953,0.0000058312453,0.000028806671,0.0000112420485,0.000028892779,0.000022492935,0.00009732235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931735,0.000006943593,0.00026357573,0.00020753889,0.000082572544,0.000122024794],"domain_scores_gemma":[0.99956155,0.00002129103,0.00004435424,0.00030677504,0.00004615711,0.000019903051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006302851,0.00015434895,0.00025135404,0.00006556942,0.000022437125,0.00000699465,0.00013677105,0.00039659487,0.0001533338],"category_scores_gemma":[0.0000026877067,0.00015868644,0.000101505655,0.00006222404,0.000044668996,0.00003873988,0.0001804341,0.00058749976,0.000005708857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046526216,0.000012505259,0.00011631925,0.00011797497,0.00007476917,0.0000044394974,0.00005025456,0.99659884,0.00040912468,0.0014661722,0.00003681511,0.0011081529],"study_design_scores_gemma":[0.00005628962,0.0000054389493,0.00008835702,0.0001208583,0.000015792739,0.0000032269074,0.000021566397,0.9872272,0.009031286,0.0031913402,0.00008639778,0.00015222625],"about_ca_topic_score_codex":0.000058845355,"about_ca_topic_score_gemma":0.00012871748,"teacher_disagreement_score":0.65815735,"about_ca_system_score_codex":0.00008153751,"about_ca_system_score_gemma":0.000029792289,"threshold_uncertainty_score":0.6471045},"labels":[],"label_agreement":null},{"id":"W4251772480","doi":"10.32920/ryerson.14647812","title":"Development Of Framework For In-Vehicle Rear-End Collision Warning System Considering Driver Characteristics","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Toronto Metropolitan University","funders":"","keywords":"Collision; Acceleration; Simulation; Advanced driver assistance systems; Automotive engineering; Driving simulator; Kinematics; Warning system; Engineering; Computer science; Population; Aeronautics; Computer security; Aerospace engineering; Physics","score_opus":0.017830277955134633,"score_gpt":0.23475084584296885,"score_spread":0.2169205678878342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251772480","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":[0.74016887,0.0001460067,0.25800115,0.000017425908,0.00043444592,0.00034173072,0.000014677807,0.00044578977,0.0004298979],"genre_scores_gemma":[0.7515398,0.00003743508,0.2482094,0.0000057613565,0.000025009069,0.00007869064,0.000044355304,0.00003945243,0.000020052243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984694,0.000019878049,0.0007700389,0.00032887133,0.000121211604,0.00029058955],"domain_scores_gemma":[0.9991433,0.00019467609,0.00015536022,0.00037934782,0.00008405407,0.000043254156],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003152954,0.00025895296,0.00062758644,0.00015225941,0.00007018283,0.000024920108,0.00021079865,0.0008793375,0.000030076124],"category_scores_gemma":[0.0000845884,0.00028980259,0.00008486026,0.00011396218,0.00004166044,0.000041625957,0.0003845352,0.00081099715,0.000004878618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003440628,0.0006037705,0.072644666,0.056027833,0.003244736,0.0006634012,0.05315881,0.2919778,0.08646442,0.14858958,0.00024230524,0.28603864],"study_design_scores_gemma":[0.0013654844,0.00004989269,0.069092155,0.013543521,0.00015014215,0.00003540552,0.008671869,0.59424514,0.3054339,0.0015995726,0.003543065,0.002269883],"about_ca_topic_score_codex":0.0000074568043,"about_ca_topic_score_gemma":0.000026944788,"teacher_disagreement_score":0.30226734,"about_ca_system_score_codex":0.000333452,"about_ca_system_score_gemma":0.00017710362,"threshold_uncertainty_score":0.9999554},"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":"W4280531543","doi":"10.1155/2022/8655514","title":"Heterogeneous Driver Modeling and Corner Scenarios Sampling for Automated Vehicles Testing","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Hyperparameter; Computer science; Sampling (signal processing); Test case; Sample (material); Simulation; Driving simulator; Scenario testing; Machine learning; Artificial intelligence; Computer vision","score_opus":0.017978973929983957,"score_gpt":0.23849954504076631,"score_spread":0.22052057111078235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280531543","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":[0.9078599,0.00040929587,0.0911814,0.00002824624,0.00014383272,0.00010603799,0.000015816315,0.0002521146,0.0000033361155],"genre_scores_gemma":[0.9489634,0.000033549313,0.050916273,0.000018396915,0.000022817927,0.000011299941,0.000010459924,0.000022273181,0.0000014986113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933785,0.0000073054407,0.00035028515,0.00008104414,0.000092367954,0.00013115528],"domain_scores_gemma":[0.9996718,0.000062192805,0.00011087741,0.000048168175,0.000074692725,0.00003224387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013390639,0.000086676664,0.00015427961,0.00009557743,0.00017179217,0.0000062496756,0.00006768709,0.000041914966,0.0000034673806],"category_scores_gemma":[0.000013436283,0.00009328601,0.00004647859,0.00010144722,0.000014383892,0.00015716378,0.000002665738,0.0002113994,1.2492423e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053082378,0.000010644862,0.00059948926,0.00003062209,0.00003052319,0.000011455021,0.0004780268,0.9610231,0.023845326,0.00004218466,0.0000023835987,0.01387319],"study_design_scores_gemma":[0.0007900132,0.00015362716,0.0050881975,0.000027855029,0.000044895358,0.000068158246,0.000329025,0.9912425,0.0010109731,0.0009684763,0.00015586246,0.00012037674],"about_ca_topic_score_codex":0.0000011200752,"about_ca_topic_score_gemma":0.000004290323,"teacher_disagreement_score":0.041103512,"about_ca_system_score_codex":0.000056509045,"about_ca_system_score_gemma":0.000018469527,"threshold_uncertainty_score":0.38040933},"labels":[],"label_agreement":null},{"id":"W4280645238","doi":"10.3390/s22103611","title":"Safety Monitoring System of CAVs Considering the Trade-Off between Sampling Interval and Data Reliability","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Reliability engineering; Reliability (semiconductor); Interval (graph theory); Sampling (signal processing); Computer science; Statistics; Data mining; Real-time computing; Engineering; Mathematics; Telecommunications; Physics","score_opus":0.03468217509482023,"score_gpt":0.2581740796031003,"score_spread":0.22349190450828005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280645238","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":[0.99736583,0.00058169826,0.0005414303,0.00023026168,0.00027579113,0.00015578738,0.00017301134,0.00040187594,0.00027433192],"genre_scores_gemma":[0.9990971,0.000036837115,0.0007599953,0.0000027193369,0.000059137095,0.000005573183,0.0000111816225,0.000021444173,0.0000059841095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.999033,0.00008119322,0.00034238695,0.0002292911,0.00011719405,0.00019694987],"domain_scores_gemma":[0.9988778,0.0003262106,0.000053456817,0.00070399186,0.000007584988,0.000030945062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007385356,0.00011763119,0.00024370382,0.000043282515,0.00026478057,0.000007650403,0.00037771382,0.00006631798,0.0000062538707],"category_scores_gemma":[0.000048358062,0.00010578384,0.00003189223,0.00012136607,0.000114281196,0.000055984157,0.00045799007,0.00047840926,0.0000010136196],"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":[0.00011231593,0.000042785374,0.34763205,0.0011940622,0.0006833534,0.000050245577,0.0068007526,0.5060539,0.0045061638,0.0021245726,0.00015243697,0.13064733],"study_design_scores_gemma":[0.0014779074,0.00018297827,0.48438615,0.00032308442,0.00036732928,0.0003380954,0.037422918,0.4243595,0.012753515,0.00050218496,0.036752146,0.0011341785],"about_ca_topic_score_codex":0.000045704957,"about_ca_topic_score_gemma":0.000004066517,"teacher_disagreement_score":0.13675411,"about_ca_system_score_codex":0.000109365414,"about_ca_system_score_gemma":0.00001329173,"threshold_uncertainty_score":0.43137398},"labels":[],"label_agreement":null},{"id":"W4281255456","doi":"10.1155/2022/6766455","title":"Road-Type Detection Based on Traffic Sign and Lane Data","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Research, Development and Innovation Office; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal","keywords":"Computer science; Heuristics; Global Positioning System; Real-time computing; Speed limit; Software; Heuristic; Data type; Vehicle type; Simulation; Data mining; Artificial intelligence; Engineering; Transport engineering","score_opus":0.009475658858297282,"score_gpt":0.2170280278812289,"score_spread":0.20755236902293162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281255456","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":[0.98970044,0.0002264112,0.0094933985,0.00008680072,0.00030196778,0.00006009453,0.000020929314,0.0000833172,0.00002663288],"genre_scores_gemma":[0.9983744,0.00006351676,0.0014512057,0.000025983847,0.000022753888,0.0000017116307,0.000044301996,0.000012081334,0.000004071174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995045,0.000015034488,0.00021188334,0.000077309785,0.000118499695,0.00007276989],"domain_scores_gemma":[0.9997297,0.00002613829,0.00007969551,0.00011855642,0.000021615078,0.000024310131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001631419,0.000063679756,0.000104965686,0.000109948276,0.0000755216,0.0000030597391,0.00010630548,0.00003446081,0.00002642415],"category_scores_gemma":[0.0000068004815,0.00006583352,0.000018885421,0.00013938887,0.000012211034,0.00016394566,0.0000016830859,0.00029655045,6.418802e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001560858,0.000019525773,0.00007908764,0.000010644435,0.000012453528,0.000019722327,0.0001085283,0.8577084,0.0051809386,0.000012616229,0.000011468106,0.13668056],"study_design_scores_gemma":[0.0051876386,0.0031964707,0.35803887,0.00006643888,0.00022747289,0.000122094,0.0013792921,0.6030312,0.010340862,0.0005884304,0.0172343,0.0005869067],"about_ca_topic_score_codex":6.580855e-7,"about_ca_topic_score_gemma":0.000017528406,"teacher_disagreement_score":0.35795978,"about_ca_system_score_codex":0.000039377835,"about_ca_system_score_gemma":0.000015787913,"threshold_uncertainty_score":0.26846132},"labels":[],"label_agreement":null},{"id":"W4282928664","doi":"10.1109/noms54207.2022.9789929","title":"Convolutional and Recurrent Neural Networks for Driver Identification: An Empirical Study","year":2022,"lang":"en","type":"article","venue":"NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Canada Research Chairs","keywords":"Computer science; Convolutional neural network; Identification (biology); Benchmark (surveying); Personalization; Machine learning; Variety (cybernetics); Artificial intelligence; Task (project management); Deep learning; Convolution (computer science); Recurrent neural network; Artificial neural network; Data mining; Engineering","score_opus":0.011775483008613231,"score_gpt":0.24967397509253622,"score_spread":0.237898492083923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282928664","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":[0.82810396,0.0026154104,0.1528447,0.0026047996,0.0058990903,0.005821783,0.00019007023,0.001098178,0.0008220156],"genre_scores_gemma":[0.99465585,0.0006017354,0.00060001854,0.00026761007,0.00037036397,0.0021282816,0.00032559803,0.000043607113,0.0010069405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983218,0.0001230918,0.0004311623,0.0005435052,0.00019326538,0.00038719305],"domain_scores_gemma":[0.99939996,0.000044705932,0.00004421512,0.0003732091,0.00003726807,0.000100645884],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005563482,0.00024316362,0.00023630775,0.00013151582,0.0016075495,0.000119832686,0.00024069512,0.00007738743,0.000118635115],"category_scores_gemma":[0.0000017127528,0.00027572917,0.000053789132,0.0003566226,0.00009376079,0.00024191549,0.00024355995,0.0003619074,0.0000024169678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043041236,0.00027498623,0.007051984,0.000026524294,0.00023609004,0.000010263663,0.00046098835,0.9660371,0.000057240875,0.006367838,0.014602647,0.004831298],"study_design_scores_gemma":[0.0008575868,0.00032186531,0.015606754,0.0000036247388,0.00015891723,0.000013758342,0.0008625002,0.95981807,0.000002496478,0.00014427635,0.021883061,0.00032706503],"about_ca_topic_score_codex":0.000005890284,"about_ca_topic_score_gemma":0.00007460379,"teacher_disagreement_score":0.16655189,"about_ca_system_score_codex":0.00012291173,"about_ca_system_score_gemma":0.000012527772,"threshold_uncertainty_score":0.9999695},"labels":[],"label_agreement":null},{"id":"W4283072071","doi":"10.1155/2022/4509071","title":"Heterogeneity in the Driver Behavior: An Exploratory Study Using Real-Time Driving Data","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Science and Engineering Research Board; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Context (archaeology); Acceleration; Driving simulator; Aggressive driving; Computer science; Poison control; Simulation; Cluster analysis; Human factors and ergonomics; Artificial intelligence","score_opus":0.028388825215401644,"score_gpt":0.28366683344829,"score_spread":0.2552780082328884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283072071","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":[0.9988421,0.00005621196,0.00070051,0.000010220839,0.00013762215,0.00018142714,0.00001877216,0.000048245132,0.0000048832844],"genre_scores_gemma":[0.9984636,0.00003155778,0.0014141352,0.000007719191,0.000023449267,0.0000105078025,0.000032109863,0.000016454613,4.988513e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9990771,0.00009264833,0.00039063435,0.00011100518,0.00021253485,0.00011606859],"domain_scores_gemma":[0.99949604,0.00002354194,0.00013149518,0.00030106833,0.000024535297,0.000023309038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058636215,0.00008684761,0.00015253227,0.000118031465,0.0001236035,0.000006836883,0.00042723326,0.000029956396,0.00001768938],"category_scores_gemma":[0.0000029258217,0.000079459045,0.000030218993,0.00019294054,0.000017679726,0.0007368868,0.00000932226,0.00038306278,4.412189e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044970584,0.00039315983,0.0940015,0.0000059011522,0.0000331833,0.0003114467,0.01270353,0.84955454,0.038114447,0.000014675019,0.0000049197893,0.004817719],"study_design_scores_gemma":[0.0009793714,0.00035593647,0.971323,0.000010863076,0.000116299525,0.00004416184,0.019281523,0.007024688,0.00052180863,0.00007158158,0.00010611287,0.00016462727],"about_ca_topic_score_codex":0.00000456035,"about_ca_topic_score_gemma":0.00034273736,"teacher_disagreement_score":0.87732154,"about_ca_system_score_codex":0.00008887022,"about_ca_system_score_gemma":0.000026655985,"threshold_uncertainty_score":0.3240246},"labels":[],"label_agreement":null},{"id":"W4283075535","doi":"10.35940/ijitee.g9996.0711822","title":"Review Paper on E-Traffic Police IoT Based Auto-Detection of Traffic Rule Violation","year":2022,"lang":"en","type":"article","venue":"International Journal of Innovative Technology and Exploring Engineering","topic":"Autonomous Vehicle Technology and Safety","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":"Horizon College and Seminary","funders":"","keywords":"Computer security; Jump; Internet of Things; Focus (optics); SIGNAL (programming language); Government (linguistics); Transport engineering; Computer science; Engineering","score_opus":0.014088403091205789,"score_gpt":0.22952630848929179,"score_spread":0.215437905398086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283075535","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":[0.9897284,0.0017442588,0.006751486,0.0006305602,0.0006960182,0.00008368856,0.000011357231,0.00031709878,0.00003713726],"genre_scores_gemma":[0.9984873,0.0005826231,0.00072040374,0.000099339355,0.000044839144,0.00003663601,0.0000036434355,0.000022890832,0.0000023515754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905854,0.000014470791,0.0004910243,0.00010743147,0.00019140524,0.00013711365],"domain_scores_gemma":[0.99940485,0.00006298773,0.00018660042,0.00010730288,0.00021916094,0.000019104184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033053465,0.00014072425,0.00024611075,0.0012918296,0.000057652618,0.0000045059837,0.00027699576,0.00008834685,0.000022316495],"category_scores_gemma":[0.00007750461,0.00014862597,0.00004997293,0.00091249915,0.00004600424,0.00014461756,0.000052806085,0.00074323773,0.0000012529808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056932084,0.000057754325,0.00012789342,0.0001347493,0.00021550375,0.000015488058,0.00013626301,0.85198396,0.055353187,0.0020314085,0.000049587066,0.089837246],"study_design_scores_gemma":[0.00560868,0.0027670464,0.017141776,0.0061199805,0.00016018952,0.0014926536,0.0016616522,0.58931524,0.314664,0.0007899711,0.058443476,0.0018353182],"about_ca_topic_score_codex":9.311139e-7,"about_ca_topic_score_gemma":5.5307703e-7,"teacher_disagreement_score":0.26266873,"about_ca_system_score_codex":0.00018196194,"about_ca_system_score_gemma":0.000023802148,"threshold_uncertainty_score":0.6060791},"labels":[],"label_agreement":null},{"id":"W4285047699","doi":"10.1109/icra46639.2022.9812290","title":"DRG: A Dynamic Relation Graph for Unified Prior-Online Environment Modeling in Urban Autonomous Driving","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Robotics and Automation (ICRA)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Relation (database); Software deployment; Graph; Set (abstract data type); Human–computer interaction; Data science; Distributed computing; Data mining; Theoretical computer science; Software engineering","score_opus":0.020085079976666247,"score_gpt":0.23383677983299125,"score_spread":0.213751699856325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285047699","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":[0.62629014,0.00010167822,0.36692566,0.003953724,0.0005648226,0.0005900083,0.00012277909,0.0004944552,0.00095674075],"genre_scores_gemma":[0.9948441,0.000108247026,0.004280151,0.000061097846,0.000017435206,0.000105414365,0.0003528233,0.00002291397,0.00020784594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900633,0.000027940694,0.000354476,0.00024595225,0.00019440374,0.00017090698],"domain_scores_gemma":[0.9996851,0.00003857618,0.00008102663,0.00013645166,0.000027190013,0.000031622832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021240371,0.00014748811,0.00015004973,0.0003059517,0.00018893096,0.000033455108,0.000188762,0.000083566774,0.00015197307],"category_scores_gemma":[0.000015517768,0.00017930841,0.00004381864,0.00009963298,0.000026179152,0.00011566736,0.000088514826,0.00033076882,0.0000044030476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000118875705,0.000059251655,0.0004599647,0.000010153073,0.00002718154,0.000002129165,0.00023864472,0.92641425,0.0009790633,0.064808644,0.000021071697,0.0069677685],"study_design_scores_gemma":[0.0005007441,0.000063765925,0.0044032233,0.000020017447,0.000009445806,0.000004681181,0.0002637264,0.98524994,0.000023827228,0.0090685915,0.00021347038,0.00017855834],"about_ca_topic_score_codex":0.0000041615563,"about_ca_topic_score_gemma":0.000019852167,"teacher_disagreement_score":0.36855394,"about_ca_system_score_codex":0.00033074574,"about_ca_system_score_gemma":0.00003373825,"threshold_uncertainty_score":0.7311985},"labels":[],"label_agreement":null},{"id":"W4285102511","doi":"10.1109/icra46639.2022.9811883","title":"Human Navigational Intent Inference with Probabilistic and Optimal Approaches","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Robotics and Automation (ICRA)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Inference; Probabilistic logic; Bayesian inference; Bayesian probability; Artificial intelligence; Function (biology); Heading (navigation); Machine learning; Prior probability; Statistical model; Engineering","score_opus":0.03667728930411638,"score_gpt":0.24351746117072587,"score_spread":0.2068401718666095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285102511","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":[0.9530544,0.000078981044,0.030082686,0.003627975,0.000416586,0.00049363874,0.00012110052,0.0006972739,0.011427353],"genre_scores_gemma":[0.99742025,0.00001527564,0.0020098437,0.00006184595,0.000024532432,0.000098239034,0.00013029984,0.000014024999,0.00022567612],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916303,0.000025572486,0.00020276895,0.00022416402,0.00025512977,0.00012931152],"domain_scores_gemma":[0.9996679,0.00003331024,0.000064208616,0.00010822303,0.00008321001,0.000043163338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000136898,0.00014290404,0.00012616628,0.00011779839,0.00026041205,0.00006642975,0.00016725544,0.000048997266,0.00030456143],"category_scores_gemma":[0.000020466185,0.00014001886,0.000017313709,0.000087572116,0.00012210014,0.000108639164,0.00012555558,0.00033361957,0.0000041276335],"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":[0.000011745749,0.000051986284,0.0010804586,0.000021118109,0.000054890083,0.000004226021,0.00026108904,0.41403094,0.00029699842,0.58028466,0.000068893634,0.0038330378],"study_design_scores_gemma":[0.00035116685,0.00017633308,0.012885488,0.000025843801,0.000012996103,0.000024575947,0.00041593745,0.9793204,0.00008104071,0.006268627,0.00023627211,0.0002013055],"about_ca_topic_score_codex":0.0000040118657,"about_ca_topic_score_gemma":0.0000063042307,"teacher_disagreement_score":0.574016,"about_ca_system_score_codex":0.00009389914,"about_ca_system_score_gemma":0.000036373793,"threshold_uncertainty_score":0.5709803},"labels":[],"label_agreement":null},{"id":"W4285222939","doi":"10.1109/tmm.2022.3177942","title":"VWP:An Efficient DRL-Based Autonomous Driving Model","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Convergence (economics); Collision; Task (project management); Mode (computer interface); Feature (linguistics); Function (biology); Artificial intelligence; Encoder; Adversarial system; Real-time computing; Algorithm; Simulation; Engineering","score_opus":0.010230341570072467,"score_gpt":0.21343052868102938,"score_spread":0.20320018711095691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285222939","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":[0.2660878,0.00002135763,0.7305219,0.0001056575,0.00064818905,0.00021693567,0.000071410184,0.0018992269,0.00042748728],"genre_scores_gemma":[0.99072516,0.0000036224242,0.008635805,0.00010252023,0.000019117773,0.00025530765,0.000011360544,0.00006639941,0.00018072505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878716,0.000043974735,0.00026089614,0.00030667562,0.00021881833,0.0003824891],"domain_scores_gemma":[0.999296,0.00008325662,0.000029594292,0.00045011067,0.00001797633,0.0001230501],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017465412,0.0002243149,0.00019752773,0.00027478472,0.00052200945,0.00001310697,0.00030540506,0.00013499348,0.00045931304],"category_scores_gemma":[0.0000020803284,0.0002654701,0.00011782691,0.0002534706,0.00007623752,0.000065197186,0.0000023364375,0.00083602377,0.00009223224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016804563,0.00023271194,0.000007728802,0.000006717543,0.00002586738,0.000009077387,0.00043565652,0.9638694,0.0034570773,0.000021392256,0.000038308077,0.03187924],"study_design_scores_gemma":[0.0005828788,0.000089083354,0.000074680975,0.000004493755,0.000027963883,0.00000700127,0.000095369185,0.97952265,0.018970542,0.000028001348,0.0003178606,0.00027947713],"about_ca_topic_score_codex":0.000012173119,"about_ca_topic_score_gemma":0.000031277483,"teacher_disagreement_score":0.7246373,"about_ca_system_score_codex":0.00034402034,"about_ca_system_score_gemma":0.00008022136,"threshold_uncertainty_score":0.99997973},"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":"W4285782836","doi":"10.48550/arxiv.1910.04586","title":"Jointly Learnable Behavior and Trajectory Planning for Self-Driving\\n Vehicles","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","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":"University of Toronto","funders":"","keywords":"Trajectory; Planner; Computer science; Function (biology); Motion (physics); Trajectory optimization; Artificial intelligence; Time horizon; Motion planning; Control theory (sociology); Simulation; Robot; Mathematical optimization; Control (management); Mathematics","score_opus":0.037436622648744665,"score_gpt":0.17597901110852326,"score_spread":0.1385423884597786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285782836","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":[0.971535,0.0003441921,0.024463594,0.000012244061,0.00039744593,0.0005119336,0.00002469118,0.0013792416,0.0013316532],"genre_scores_gemma":[0.9977114,0.00020588282,0.0009103637,0.000011910048,0.000045628047,0.000006186714,0.000017763397,0.000058511716,0.0010323222],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988883,0.00002260537,0.00016369089,0.0005354003,0.000031991385,0.00035799987],"domain_scores_gemma":[0.9992879,0.00007877333,0.00007753925,0.00043681584,0.000038036145,0.00008092111],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013932619,0.00029704487,0.0003629677,0.00020452007,0.0001314246,0.00002713923,0.00034127056,0.00064259046,0.000016207465],"category_scores_gemma":[0.000009343835,0.00038009562,0.00013197014,0.00010293775,0.00008088742,0.00012229008,0.00029047605,0.00076219643,0.000024992045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040905667,0.00007869949,0.09187907,0.00064249727,0.00033654028,0.00015344287,0.00045655493,0.8977915,0.0006228939,0.006118534,0.00072711747,0.0011522538],"study_design_scores_gemma":[0.001545825,0.0001612837,0.05689927,0.0002694217,0.00064809126,0.000020434876,0.000537649,0.9272253,0.0015854886,0.004033406,0.0056945793,0.001379241],"about_ca_topic_score_codex":0.000013287343,"about_ca_topic_score_gemma":0.000009173522,"teacher_disagreement_score":0.0349798,"about_ca_system_score_codex":0.00017581771,"about_ca_system_score_gemma":0.000059088805,"threshold_uncertainty_score":0.9998651},"labels":[],"label_agreement":null},{"id":"W4285813009","doi":"10.1109/iv51971.2022.9827272","title":"Vehicle-to-Everything (V2X) in Scenarios: Extending Scenario Description Language for Connected Vehicle Scenario Descriptions","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Intelligent Vehicles Symposium (IV)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Transport Canada","keywords":"Computer science; Human–computer interaction","score_opus":0.017518840140682498,"score_gpt":0.23576730569860282,"score_spread":0.21824846555792032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285813009","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":[0.9749214,0.001115056,0.01634141,0.0011112847,0.0021917678,0.0018941802,0.00020112692,0.0018056894,0.0004180729],"genre_scores_gemma":[0.9951827,0.00019593585,0.0013122532,0.0006573521,0.00021573542,0.0011538609,0.00014681874,0.00022347261,0.00091188063],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99556375,0.00020171328,0.0011991399,0.0010189399,0.00057964906,0.0014367922],"domain_scores_gemma":[0.9982492,0.00029887,0.00017701052,0.0008603843,0.00011112737,0.0003034414],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012626731,0.0006628033,0.00072683033,0.001134086,0.0010843525,0.00017179923,0.0010625928,0.00037559052,0.00033685172],"category_scores_gemma":[0.000094433475,0.00081949786,0.00032960044,0.0014825306,0.000137492,0.00052466214,0.00038865692,0.0016434882,0.0001762692],"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":[0.00043925922,0.000379184,0.0048445766,0.00015867056,0.00016062721,0.00012202035,0.00915762,0.35575557,0.613545,0.0013247881,0.0024888157,0.01162386],"study_design_scores_gemma":[0.0024301736,0.0009503724,0.0029222923,0.00026544253,0.00016671953,0.00015354251,0.010094918,0.86194956,0.094590046,0.0010865775,0.023299277,0.0020911044],"about_ca_topic_score_codex":0.00045303968,"about_ca_topic_score_gemma":0.0008179794,"teacher_disagreement_score":0.518955,"about_ca_system_score_codex":0.0023121466,"about_ca_system_score_gemma":0.00014651174,"threshold_uncertainty_score":0.9994256},"labels":[],"label_agreement":null},{"id":"W4285819379","doi":"10.1109/tits.2022.3177367","title":"ST CrossingPose: A Spatial-Temporal Graph Convolutional Network for Skeleton-Based Pedestrian Crossing Intention Prediction","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"European Commission; Royal Academy of Engineering; York University","keywords":"Computer science; Pedestrian; Inference; Graph; Artificial intelligence; Convolutional neural network; Context (archaeology); Spatial contextual awareness; Limiting; Data mining; Pattern recognition (psychology); Machine learning; Theoretical computer science; Engineering; Geography; Transport engineering","score_opus":0.0183763501461006,"score_gpt":0.23084254975794483,"score_spread":0.21246619961184424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285819379","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":[0.09413403,0.00020955405,0.8969805,0.00005724402,0.004208611,0.0011017167,0.002164137,0.001118354,0.000025816735],"genre_scores_gemma":[0.9967289,0.000016290393,0.0005966294,0.000035377947,0.000116936084,0.0013383523,0.00092259794,0.00007536935,0.00016957715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977736,0.0000683434,0.0009263662,0.00041326782,0.00038408907,0.00043435348],"domain_scores_gemma":[0.99920785,0.00012558262,0.00016836783,0.00025912345,0.00013933855,0.00009974696],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004838254,0.00031044663,0.00032643884,0.0003345825,0.0017081938,0.00010816422,0.00018948602,0.00021863493,0.000180751],"category_scores_gemma":[0.0000020918746,0.0003759546,0.00032280965,0.00051388453,0.00017175205,0.00016701147,3.7296036e-7,0.00054371485,0.000010069626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002686209,0.00014932241,0.0012107228,0.00016961996,0.0001343819,0.000004166168,0.00031415297,0.9918466,0.00054530014,0.00035644145,0.0003372686,0.004663379],"study_design_scores_gemma":[0.0019211514,0.0006304944,0.0028888003,0.00016250489,0.00020258094,0.000019887853,0.00086094916,0.9672795,0.0067957724,0.0004939135,0.01809508,0.0006493473],"about_ca_topic_score_codex":0.0004173286,"about_ca_topic_score_gemma":0.0006846787,"teacher_disagreement_score":0.90259486,"about_ca_system_score_codex":0.00043434507,"about_ca_system_score_gemma":0.00016619374,"threshold_uncertainty_score":0.9998692},"labels":[],"label_agreement":null},{"id":"W4286300257","doi":"10.1109/iwcmc55113.2022.9824992","title":"Investigating Drivable Space Instance Segmentation for Connected and Autonomous Vehicles","year":2022,"lang":"en","type":"article","venue":"2022 International Wireless Communications and Mobile Computing (IWCMC)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Computer science; SAFER; Artificial intelligence; Space (punctuation); Computer vision; State (computer science); Deep learning; Human–computer interaction; Computer security","score_opus":0.012031088840052225,"score_gpt":0.24741918867143187,"score_spread":0.23538809983137965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286300257","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":[0.983434,0.0017729806,0.012111626,0.00085099105,0.00022120234,0.0004451098,0.0000529941,0.0004330746,0.0006780089],"genre_scores_gemma":[0.99027747,0.0004699467,0.008414379,0.00011077703,0.000025734169,0.00036741077,0.00018953804,0.000027882717,0.00011684462],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991483,0.000059534326,0.000297932,0.00021201448,0.00011047673,0.00017171836],"domain_scores_gemma":[0.9990907,0.00032787686,0.00011088594,0.0003570078,0.000068685804,0.000044886456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002970999,0.00013406943,0.00015898835,0.00012109457,0.00089530327,0.000053994125,0.00048171228,0.000053146785,0.000018271143],"category_scores_gemma":[0.00002275671,0.00016819111,0.00003138332,0.00016148538,0.0001588263,0.00011318734,0.0006344026,0.00034125557,0.0000010205268],"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":[0.000038122424,0.00033878206,0.028612716,0.00021692854,0.0005671095,0.0000040827795,0.009195576,0.16661006,0.07174714,0.24158265,0.002075156,0.47901168],"study_design_scores_gemma":[0.0005807414,0.000070348025,0.0024560709,0.000026263877,0.000014755267,0.000026990052,0.0016355452,0.9704616,0.0009985097,0.001395703,0.022119794,0.00021369755],"about_ca_topic_score_codex":0.000033645385,"about_ca_topic_score_gemma":0.000027430866,"teacher_disagreement_score":0.80385154,"about_ca_system_score_codex":0.0001551176,"about_ca_system_score_gemma":0.00003221045,"threshold_uncertainty_score":0.68860394},"labels":[],"label_agreement":null},{"id":"W4286741712","doi":"10.48550/arxiv.1905.08758","title":"aUToTrack: A Lightweight Object Detection and Tracking System for the\\n SAE AutoDrive Challenge","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","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":true,"ca_institutions":"Institute for Christian Studies; Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Benchmark (surveying); Inertial measurement unit; Global Positioning System; Computer science; Artificial intelligence; Computer vision; Position (finance); Object detection; Tracking (education); Ground truth; Lidar; Video tracking; Pedestrian; Object (grammar); Tracking system; Segmentation; Engineering; Geography; Kalman filter; Cartography; Remote sensing; Transport engineering; Telecommunications","score_opus":0.03542319572148158,"score_gpt":0.16867199766142468,"score_spread":0.1332488019399431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286741712","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":[0.43116984,0.003179077,0.55202025,0.00025723816,0.0036948072,0.0036490369,0.00011669567,0.0021416794,0.0037713873],"genre_scores_gemma":[0.9952251,0.0031808948,0.0001354481,0.0000120838895,0.00023623073,0.00001948713,0.000010736501,0.00013066476,0.0010493849],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996607,0.00014725726,0.00058722694,0.0016448885,0.00010164451,0.0009119704],"domain_scores_gemma":[0.9970451,0.0006817347,0.00046641615,0.0014258113,0.00020631199,0.00017462256],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0006774411,0.0008986642,0.0009776256,0.00043384315,0.00096572074,0.00010814337,0.0010171225,0.0019124805,0.000032235945],"category_scores_gemma":[0.000044859262,0.0009263087,0.0005445165,0.00047212385,0.00034772838,0.00039947248,0.0006346718,0.0019244394,0.00013002334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053360726,0.00016541184,0.0008570154,0.004287992,0.002773813,0.00024805378,0.002762918,0.8084272,0.0012592385,0.14163856,0.000029957573,0.037016217],"study_design_scores_gemma":[0.0016902683,0.00027996764,0.006225798,0.0005950249,0.0011478777,0.000059491755,0.0018320013,0.97938436,0.0017160762,0.002841887,0.0031603095,0.0010669407],"about_ca_topic_score_codex":0.00012203051,"about_ca_topic_score_gemma":0.00037126485,"teacher_disagreement_score":0.56405526,"about_ca_system_score_codex":0.0010745137,"about_ca_system_score_gemma":0.0001713764,"threshold_uncertainty_score":0.9993833},"labels":[],"label_agreement":null},{"id":"W4287203366","doi":"10.48550/arxiv.2104.11212","title":"Imagining The Road Ahead: Multi-Agent Trajectory Prediction via\\n Differentiable Simulation","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Air Force Research Laboratory; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; Western Canada Research Grid; Compute Canada; Defense Advanced Research Projects Agency; Mitacs","keywords":"Trajectory; Computer science; Kinematics; Differentiable function; State (computer science); Acceleration; Artificial neural network; Artificial intelligence; Control theory (sociology); Simulation; Algorithm; Control (management); Mathematics","score_opus":0.043533081869307065,"score_gpt":0.17670025009763066,"score_spread":0.13316716822832358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287203366","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":[0.5407521,0.00020816737,0.4569502,0.000015701691,0.0005780087,0.00020329896,0.0000162269,0.0007857635,0.00049051683],"genre_scores_gemma":[0.99877137,0.00019794378,0.00019450687,0.000016518541,0.00006825619,0.0000028869165,0.00008578835,0.000041441195,0.00062130706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883443,0.00008114875,0.00021546353,0.00051137793,0.000058927635,0.00029862399],"domain_scores_gemma":[0.99905694,0.000051650863,0.00008797553,0.00067649246,0.00006501571,0.00006191718],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001382523,0.00029356356,0.00026786246,0.0001423148,0.00021371189,0.000039461513,0.00040620673,0.00047008658,0.00010433786],"category_scores_gemma":[0.000012720156,0.000304872,0.0001814774,0.00023662327,0.00010908528,0.0001659065,0.00031852702,0.0010469838,0.00003268907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009029231,0.000043992353,0.0031986998,0.00006293944,0.00017278158,0.000040486684,0.00032466493,0.9939934,0.00029008716,0.00018759193,0.000026083055,0.0016502402],"study_design_scores_gemma":[0.0003442441,0.000010436219,0.021639157,0.00005498842,0.00014767065,0.0000024399565,0.0002920878,0.97629553,0.00036966015,0.0003413589,0.00024132937,0.0002610748],"about_ca_topic_score_codex":0.00008046077,"about_ca_topic_score_gemma":0.00006597391,"teacher_disagreement_score":0.45801923,"about_ca_system_score_codex":0.00032200673,"about_ca_system_score_gemma":0.000049359012,"threshold_uncertainty_score":0.99994034},"labels":[],"label_agreement":null},{"id":"W4287549454","doi":"","title":"The Role of Big Data in the Development of Safety in Fully Automated Vehicles: A Survey","year":2020,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Big data; Vehicle safety; Data science; Data mining; Engineering; Automotive engineering","score_opus":0.027272836993093654,"score_gpt":0.2317434425167805,"score_spread":0.20447060552368684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287549454","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":[0.94943815,0.004007131,0.023979636,0.0043632216,0.00020743822,0.0010960224,0.0004149901,0.0007739019,0.015719494],"genre_scores_gemma":[0.9936234,0.00041788834,0.005348907,0.000008678006,0.0000033474294,0.000029806042,0.0005187468,0.000022025082,0.000027207489],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9954616,0.0028374533,0.00087494217,0.00035359457,0.00023966868,0.00023277715],"domain_scores_gemma":[0.9952384,0.0018562952,0.00026952836,0.002315088,0.00028919187,0.000031483058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010570551,0.00019291585,0.0003345746,0.000121542034,0.000113409034,0.0000369633,0.0029416888,0.0002608042,0.000004374502],"category_scores_gemma":[0.0010572866,0.00015505521,0.00004631503,0.0005743406,0.00021190036,0.00004386007,0.0015870894,0.00075387367,0.0000049673795],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012755528,0.0008861054,0.09423565,0.0007351317,0.00038999083,0.000007393493,0.08617328,0.004956673,0.007381857,0.028892806,0.0004073747,0.7758062],"study_design_scores_gemma":[0.0005655364,4.2602932e-7,0.51867497,0.0011062328,0.000018703193,0.0000022344193,0.0010907765,0.4368669,0.030113677,0.0026361367,0.008545636,0.00037879366],"about_ca_topic_score_codex":0.0008420197,"about_ca_topic_score_gemma":0.027363004,"teacher_disagreement_score":0.7754274,"about_ca_system_score_codex":0.00008577675,"about_ca_system_score_gemma":0.0003374456,"threshold_uncertainty_score":0.9903851},"labels":[],"label_agreement":null},{"id":"W4289105254","doi":"10.48550/arxiv.1812.09395","title":"Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural\\n Networks","year":2018,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","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":"Huawei Technologies (Canada)","funders":"","keywords":"Occupancy grid mapping; Computer science; Grid; Artificial intelligence; Frame (networking); Path (computing); Motion planning; Artificial neural network; Recurrent neural network; Occupancy; Motion (physics); State (computer science); State space; Space (punctuation); Machine learning; Computer vision; Pattern recognition (psychology); Algorithm; Engineering; Mobile robot; Robot; Geography; Mathematics","score_opus":0.04175772098599456,"score_gpt":0.17150752707779074,"score_spread":0.12974980609179618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289105254","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":[0.49516138,0.00033814218,0.49911714,0.000018842498,0.0026944645,0.00082220335,0.00032571147,0.00075461314,0.0007675086],"genre_scores_gemma":[0.9954844,0.0019256726,0.0015853872,0.000011255656,0.0002972766,0.0000036022132,0.00017384569,0.00009406333,0.00042446624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967378,0.00016505748,0.0007013876,0.0014587285,0.00011096238,0.00082609244],"domain_scores_gemma":[0.9970565,0.00008586113,0.00063115184,0.0016448923,0.00033429568,0.00024732642],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00030914877,0.0008558877,0.00094608334,0.0004580048,0.00029271492,0.000023186421,0.0011281373,0.0015050413,0.0001794089],"category_scores_gemma":[0.000024047993,0.0009747531,0.0003553401,0.0009425304,0.0008451475,0.00032706032,0.0008140308,0.0020806142,0.00006210407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057681085,0.00033534932,0.08558718,0.00041169368,0.0007742723,0.0001836228,0.00016806982,0.90603566,0.00003124703,0.0016728963,0.00040420698,0.003818997],"study_design_scores_gemma":[0.001815436,0.0005247587,0.041319728,0.0004833049,0.00059677236,0.000022741819,0.00016397782,0.9534783,0.00009103549,0.0002026811,0.0005783558,0.0007229044],"about_ca_topic_score_codex":0.00011475984,"about_ca_topic_score_gemma":0.00013224475,"teacher_disagreement_score":0.50032306,"about_ca_system_score_codex":0.00046675003,"about_ca_system_score_gemma":0.0001267926,"threshold_uncertainty_score":0.9997912},"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":"W4290079687","doi":"10.1155/2022/9328398","title":"Vision-Based Branch Road Detection for Intersection Navigation in Unstructured Environment Using Multi-Task Network","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Intersection (aeronautics); Computer science; Artificial intelligence; Task (project management); Minimum bounding box; Computer vision; Bounding overwatch; Occupancy grid mapping; Artificial neural network; Deep learning; Noise (video); Grid; Object detection; Pattern recognition (psychology); Transport engineering; Image (mathematics); Engineering; Mobile robot; Geography","score_opus":0.00559821021398333,"score_gpt":0.2219454218347125,"score_spread":0.21634721162072917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290079687","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":[0.59837854,0.00009816479,0.40090925,0.00001301071,0.00041682867,0.00014787841,0.0000071609825,0.000028632716,5.263595e-7],"genre_scores_gemma":[0.9804,0.000012346411,0.019447776,0.000010944895,0.000046301007,0.000022299118,0.000039411923,0.000020060881,8.5809506e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991703,0.000025013132,0.00045428541,0.00009686922,0.0001224946,0.00013102083],"domain_scores_gemma":[0.99965596,0.00002377139,0.00020975455,0.00006159052,0.00002671383,0.000022204724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002109962,0.00009657171,0.0001518067,0.00014945299,0.00011994987,0.0000039872007,0.000060294507,0.00006972975,0.000010604878],"category_scores_gemma":[0.000003599739,0.00011059653,0.00008185156,0.00017638321,0.000015394475,0.00021663037,0.0000012600427,0.00031324202,1.668825e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022492271,0.000024941894,0.0006267623,0.000020960159,0.00001373745,0.0000035410972,0.00028245745,0.8579463,0.08765689,0.000008128937,5.447204e-7,0.05319081],"study_design_scores_gemma":[0.005219739,0.0006768918,0.26906094,0.00010447925,0.00008147348,0.000032978034,0.0006629207,0.6901244,0.030929515,0.0021275654,0.00066724874,0.0003118446],"about_ca_topic_score_codex":0.0000048224624,"about_ca_topic_score_gemma":0.000051198276,"teacher_disagreement_score":0.38202146,"about_ca_system_score_codex":0.00041113968,"about_ca_system_score_gemma":0.000018208078,"threshold_uncertainty_score":0.45099953},"labels":[],"label_agreement":null},{"id":"W4290996163","doi":"10.1109/icc45855.2022.9839048","title":"An Interaction-Aware Vehicle Behavior Prediction for Connected Automated Vehicles","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Autonomous Vehicle Technology and Safety","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 Ottawa","funders":"","keywords":"Computer science; Human–computer interaction","score_opus":0.06907104624999051,"score_gpt":0.3433460856599608,"score_spread":0.2742750394099703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290996163","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":[0.96439993,0.0000868475,0.0070638666,0.004386588,0.0030757296,0.0013054858,0.003815804,0.0071880394,0.008677682],"genre_scores_gemma":[0.99548054,0.00009066815,0.00058473984,0.00013164863,0.00005042248,0.0020055121,0.0014732223,0.000038910926,0.0001443207],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988657,0.000110434776,0.00036866803,0.00024205526,0.0002212853,0.00019181363],"domain_scores_gemma":[0.9985359,0.00018037764,0.00010115031,0.0009314686,0.00019152928,0.000059630765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002005961,0.00016463472,0.00014963209,0.00024718148,0.0006818033,0.000060432627,0.0014165203,0.00009909844,0.0007363594],"category_scores_gemma":[0.000023508315,0.00020523879,0.00007493799,0.00020717077,0.00009925246,0.00028035426,0.00014769268,0.00063452707,0.00003641784],"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":[0.00068309583,0.004814861,0.01251552,0.00006417138,0.0012330222,0.000024316603,0.0036990615,0.20090076,0.33454663,0.30906537,0.035972223,0.096480966],"study_design_scores_gemma":[0.0005093902,0.00020561334,0.0053485525,0.000013287085,0.00002960911,0.000016680491,0.0009704784,0.9792196,0.0017937253,0.00086577405,0.010823425,0.00020385807],"about_ca_topic_score_codex":0.000040296705,"about_ca_topic_score_gemma":0.00009713949,"teacher_disagreement_score":0.7783188,"about_ca_system_score_codex":0.00036273804,"about_ca_system_score_gemma":0.00006382474,"threshold_uncertainty_score":0.8369395},"labels":[],"label_agreement":null},{"id":"W4292388080","doi":"10.1109/itc-egypt55520.2022.9855682","title":"An Analysis of the Effects of Hyperparameters on the Performance of Simulated Autonomous Vehicles","year":2022,"lang":"en","type":"article","venue":"2022 International Telecommunications Conference (ITC-Egypt)","topic":"Autonomous Vehicle Technology and Safety","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":"Western University","funders":"","keywords":"Hyperparameter; Computer science; Artificial intelligence","score_opus":0.010240757672847607,"score_gpt":0.2303751588773324,"score_spread":0.2201344012044848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292388080","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":[0.9975948,0.000103365404,0.00014849556,0.00052421255,0.00013588887,0.00022966425,0.000094557116,0.00007824574,0.0010907823],"genre_scores_gemma":[0.999369,0.00015561655,0.0001968829,0.0000595192,0.0000036685574,0.0000691697,0.00006331836,0.000013839031,0.00006893876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880356,0.00019529759,0.00045936945,0.00014174286,0.00027180777,0.00012822675],"domain_scores_gemma":[0.9976088,0.0007116834,0.00025436142,0.0012570744,0.00014847153,0.000019619789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031411732,0.00012870182,0.0002641907,0.0003121193,0.00022850158,0.000008685016,0.0022322105,0.00006366674,0.00034895033],"category_scores_gemma":[0.00010780183,0.00010216421,0.00016059105,0.000837619,0.0002491653,0.00008379943,0.000314382,0.00049405434,0.000002395584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004060952,0.00038018392,0.029207097,0.000034580135,0.0015595948,3.8446413e-7,0.0011046518,0.8793783,0.04458937,0.032653935,0.000054000113,0.010997299],"study_design_scores_gemma":[0.00018545662,0.00014302989,0.09724028,0.000021730448,0.00017130074,0.0000013742944,0.00027920847,0.86019623,0.041093178,0.00020073632,0.00035810788,0.000109360546],"about_ca_topic_score_codex":0.00010426846,"about_ca_topic_score_gemma":0.00004345724,"teacher_disagreement_score":0.06803317,"about_ca_system_score_codex":0.00013036931,"about_ca_system_score_gemma":0.00007716738,"threshold_uncertainty_score":0.41661355},"labels":[],"label_agreement":null},{"id":"W4293052862","doi":"10.1186/s10033-022-00748-7","title":"Lane-Exchanging Driving Strategy for Autonomous Vehicle via Trajectory Prediction and Model Predictive Control","year":2022,"lang":"en","type":"article","venue":"Chinese Journal of Mechanical Engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Science and Technology Major Project of Guangxi; State Key Laboratory of Automotive Safety and Energy; National Natural Science Foundation of China","keywords":"Trajectory; CarSim; Model predictive control; Kinematics; Vehicle dynamics; Computer science; Collision avoidance; Control theory (sociology); Field (mathematics); Simulation; Control (management); Collision; Engineering; Automotive engineering; Artificial intelligence; Mathematics","score_opus":0.005487625317451317,"score_gpt":0.1974708017369269,"score_spread":0.1919831764194756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293052862","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":[0.33084565,0.00050134066,0.6677551,0.000047791276,0.0003500587,0.00016883861,0.000049447277,0.0002610049,0.000020730786],"genre_scores_gemma":[0.99682,0.000032239608,0.0028230052,0.000019570904,0.00018646683,0.00004729029,0.000004612542,0.000058157,0.000008710575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988354,0.000021351747,0.00048839184,0.00015687896,0.00018566735,0.00031231728],"domain_scores_gemma":[0.99943024,0.00016630949,0.00010658531,0.00012413971,0.000048729045,0.0001239937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006004901,0.00022450944,0.0004003224,0.0002367937,0.0001626314,0.000015690568,0.00020678467,0.00013022947,0.000013047369],"category_scores_gemma":[0.00007101748,0.0002120949,0.00013923208,0.0001578892,0.000015938962,0.00021084172,0.00005649665,0.0007590411,2.963983e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005569925,0.000025831388,0.00012392543,0.000050250066,0.00013255315,0.000013293231,0.0001329684,0.95970803,0.036728013,0.00049450016,0.000020983493,0.0025139549],"study_design_scores_gemma":[0.0013095954,0.0003618088,0.00092197885,0.000021823798,0.000063192856,0.00023075491,0.000058071102,0.9948063,0.0006340805,0.001356349,0.00006252405,0.00017355318],"about_ca_topic_score_codex":6.3413006e-7,"about_ca_topic_score_gemma":8.1004004e-7,"teacher_disagreement_score":0.66597426,"about_ca_system_score_codex":0.00022084783,"about_ca_system_score_gemma":0.000039504153,"threshold_uncertainty_score":0.8648979},"labels":[],"label_agreement":null},{"id":"W4293074678","doi":"10.11159/cdsr22.123","title":"Continuous Appropriately-Oriented Collision Detection Algorithm for Physics-Based Simulations","year":2022,"lang":"en","type":"article","venue":"Proceedings of the International Conference of Control, Dynamic systems, and Robotics","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Collision detection; Collision; Computer science; Algorithm; Computational science; Physics; Programming language","score_opus":0.010119248637487858,"score_gpt":0.21449877001798814,"score_spread":0.20437952138050028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293074678","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":[0.18800423,0.00014759063,0.8085903,0.00027183382,0.0011971152,0.0009284584,0.00042832494,0.00013501226,0.00029713064],"genre_scores_gemma":[0.9977812,0.000009045289,0.001996598,0.000009477501,0.000030326917,0.00006328546,0.000013125819,0.000015147027,0.000081783495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991653,0.0000074836835,0.0003592042,0.00013078965,0.00022410623,0.000113139424],"domain_scores_gemma":[0.9990419,0.000088307126,0.00026343885,0.000068086345,0.00051954703,0.000018701512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001972845,0.00011244313,0.00024809444,0.00007901995,0.0001564808,0.000020030435,0.0002747011,0.000069534166,0.0000034074358],"category_scores_gemma":[0.000045721277,0.00009900787,0.000067624824,0.00010457098,0.00008074175,0.00008129519,0.00005557875,0.00014735984,1.2006373e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008809425,0.00006788549,0.000901364,0.0001438799,0.00019392723,1.1770346e-7,0.000105580344,0.9234588,0.034601748,0.03295847,0.000016985605,0.007463142],"study_design_scores_gemma":[0.0010875355,0.00009238851,0.00027688043,0.000045308767,0.00005300662,0.0000049036075,0.00031411403,0.9945013,0.001981572,0.0013839416,0.00016472118,0.00009433691],"about_ca_topic_score_codex":0.0000171611,"about_ca_topic_score_gemma":0.0000036096078,"teacher_disagreement_score":0.80977696,"about_ca_system_score_codex":0.00009804105,"about_ca_system_score_gemma":0.000035161072,"threshold_uncertainty_score":0.40374237},"labels":[],"label_agreement":null},{"id":"W4293118051","doi":"10.1007/978-3-031-01504-5_1","title":"Introductions","year":2020,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on advances in automotive technology","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Automotive industry; Multidisciplinary approach; Intelligent transportation system; Field (mathematics); Engineering; Computer science; Systems engineering; Transport engineering; Aerospace engineering; Political science","score_opus":0.0051720711999669305,"score_gpt":0.2059303310985721,"score_spread":0.20075825989860516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293118051","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009503773,0.022869358,0.005427316,0.007119341,0.0014997789,0.00078604295,0.00013711785,0.010462518,0.9516035],"genre_scores_gemma":[0.91822606,0.03795761,0.0126665635,0.0011611878,0.0013319872,0.00090934034,0.00006872504,0.0011927659,0.026485732],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980223,0.000021390086,0.0005134616,0.00079441443,0.00017191791,0.00047649004],"domain_scores_gemma":[0.9987475,0.00026390475,0.00013655811,0.00074511935,0.000050306382,0.000056554665],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.00009560312,0.00068872335,0.00091873767,0.0014088034,0.000097221324,0.000009685486,0.00072333723,0.0017218952,0.0004734003],"category_scores_gemma":[0.00041150468,0.0007112765,0.00015631343,0.00029992472,0.0006948662,0.00009640929,0.00012088713,0.0025697902,0.0006265295],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003201863,0.000027239508,0.000015977315,0.00009727237,0.00016470462,0.000088078945,0.000034387882,0.0084414845,0.00018481386,0.67077434,0.0013537923,0.3187859],"study_design_scores_gemma":[0.00015465709,0.00014741617,0.000023892755,0.00024829866,0.00005084812,0.000039545754,0.000020764239,0.00047086916,0.01246035,0.42979681,0.55584997,0.00073657284],"about_ca_topic_score_codex":4.156039e-7,"about_ca_topic_score_gemma":0.000050283412,"teacher_disagreement_score":0.92511773,"about_ca_system_score_codex":0.0004358962,"about_ca_system_score_gemma":0.000043372354,"threshold_uncertainty_score":0.9997313},"labels":[],"label_agreement":null},{"id":"W4293248264","doi":"10.1155/2022/4749086","title":"Key Technologies of Vehicle Active Safety System Based on Computer Vision","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":3,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Active safety; Warning system; Key (lock); Lane departure warning system; Computer science; Computer security; Track (disk drive); System safety; Simulation; Engineering; Transport engineering; Automotive engineering; Reliability engineering; Telecommunications","score_opus":0.003288331558449402,"score_gpt":0.19753121725552425,"score_spread":0.19424288569707485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293248264","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":[0.93526876,0.000111903995,0.06337812,0.00021216283,0.00038398398,0.00013865357,0.000049907976,0.0003276915,0.00012882835],"genre_scores_gemma":[0.9928639,0.000023779581,0.0070558856,0.000011113068,0.000012130993,0.000005232061,0.000011415148,0.000014875793,0.0000016708681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991351,0.000020718295,0.00044504862,0.00008156451,0.0002125036,0.00010508514],"domain_scores_gemma":[0.99948996,0.000063203755,0.00024294159,0.000120475095,0.00006684141,0.000016559196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014752282,0.00009605859,0.00022933901,0.00021564888,0.00008274618,0.0000018903899,0.00015572531,0.000070335635,0.000010379462],"category_scores_gemma":[0.000003851345,0.000093041665,0.000090352514,0.00022949983,0.000034412133,0.00014941642,0.0000034259258,0.00039302782,7.5659364e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048179494,0.000047429043,0.00018717453,0.00005942736,0.000027908427,0.00003080497,0.0002628815,0.9369873,0.008614459,0.0008513537,0.000012546329,0.052436933],"study_design_scores_gemma":[0.008484839,0.0066446573,0.3116345,0.00071688165,0.00020554887,0.000078651,0.012170957,0.45392355,0.19785383,0.0012058766,0.006298747,0.00078192796],"about_ca_topic_score_codex":6.305856e-7,"about_ca_topic_score_gemma":0.0000020400494,"teacher_disagreement_score":0.48306373,"about_ca_system_score_codex":0.00017706853,"about_ca_system_score_gemma":0.000023236931,"threshold_uncertainty_score":0.3794129},"labels":[],"label_agreement":null},{"id":"W4294841920","doi":"10.36227/techrxiv.20770486.v1","title":"Self-Learned Autonomous Driving at Unsignalized Intersections: A Hierarchical Reinforced Learning Approach for Feasible Decision-Making","year":2022,"lang":"en","type":"preprint","venue":"","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":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Intersection (aeronautics); Artificial intelligence; Scheme (mathematics); Collision avoidance; Artificial neural network; Machine learning; Layer (electronics); Collision; Engineering; Transport engineering; Computer security; Mathematics","score_opus":0.01515960833369489,"score_gpt":0.26126499505418077,"score_spread":0.24610538672048587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294841920","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":[0.059359886,0.0003791259,0.89840084,0.00007715586,0.0008163284,0.001722628,0.000016236694,0.009138304,0.030089475],"genre_scores_gemma":[0.7940226,0.00014400756,0.20124939,0.000048939415,0.00011070585,0.0012723855,0.00017639967,0.00019493935,0.002780586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99690753,0.00012574006,0.0009045612,0.0009749427,0.0003039087,0.0007832985],"domain_scores_gemma":[0.99796706,0.0008019405,0.00021664613,0.00081693666,0.00006813722,0.00012929837],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000832295,0.00063621084,0.00085767463,0.0006565205,0.00091901433,0.00011373837,0.000869017,0.0010055818,0.001090533],"category_scores_gemma":[0.00031347867,0.0007017332,0.0005503381,0.00035694768,0.00009544592,0.000121184625,0.0022224009,0.0034394793,0.000027996679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015146387,0.00003598822,0.00019697298,0.00037670918,0.0004688055,0.000012257587,0.00097357645,0.9793183,0.00022427423,0.0042005964,0.000456901,0.013584137],"study_design_scores_gemma":[0.00075430423,0.000108144624,0.000062397296,0.00011877987,0.00011296791,0.00006318214,0.00046361156,0.9797793,0.00022311434,0.003292546,0.014237652,0.00078399765],"about_ca_topic_score_codex":0.000012691596,"about_ca_topic_score_gemma":0.000018090728,"teacher_disagreement_score":0.7346628,"about_ca_system_score_codex":0.0016504388,"about_ca_system_score_gemma":0.00019235264,"threshold_uncertainty_score":0.9998226},"labels":[],"label_agreement":null},{"id":"W4295832134","doi":"10.1109/jas.2022.105866","title":"Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic","year":2022,"lang":"en","type":"article","venue":"IEEE/CAA Journal of Automatica Sinica","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Special Project for Research and Development in Key areas of Guangdong Province","keywords":"Trajectory; Computer science; Softmax function; Support vector machine; Sigmoid function; Collision; Gaussian; Function (biology); Inference; Dimension (graph theory); Machine learning; Artificial intelligence; Simulation; Mathematics; Artificial neural network","score_opus":0.007908278095326788,"score_gpt":0.24065500440223014,"score_spread":0.23274672630690335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295832134","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":[0.997431,0.00021995747,0.0007558766,0.00024024176,0.000832895,0.00012800888,0.000017707638,0.00015632872,0.00021797688],"genre_scores_gemma":[0.9987491,0.00030454152,0.0008417666,0.0000151293,0.000040537456,0.000016935935,0.0000017448046,0.000021970882,0.000008311787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842316,0.00019867865,0.0008398721,0.00012620685,0.00020696399,0.00020510262],"domain_scores_gemma":[0.9992657,0.00027350156,0.00022998052,0.00014857129,0.000023775068,0.000058513797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000893679,0.00013827934,0.00034318372,0.00043193303,0.00009303114,0.00001685258,0.00017974837,0.00010592033,0.000112951704],"category_scores_gemma":[0.000039282128,0.00014316874,0.00007845333,0.00026533863,0.000051556584,0.00023211037,0.000028962077,0.001438398,0.000002451855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103507424,0.0006195343,0.042188518,0.000117464144,0.00029757756,0.00028354957,0.0024183057,0.8849593,0.0016560629,0.00007488718,0.0017373189,0.06554398],"study_design_scores_gemma":[0.0015215325,0.0003725703,0.33400014,0.0001042121,0.00005772034,0.00037844558,0.0014733553,0.66046786,0.00024832436,0.00022691055,0.0009711716,0.00017777734],"about_ca_topic_score_codex":0.0000051287425,"about_ca_topic_score_gemma":0.00005290281,"teacher_disagreement_score":0.29181162,"about_ca_system_score_codex":0.00041574647,"about_ca_system_score_gemma":0.00010533824,"threshold_uncertainty_score":0.62492037},"labels":[],"label_agreement":null},{"id":"W4296502315","doi":"10.3390/ijerph191811819","title":"Spatiotemporal Feature Enhancement Aids the Driving Intention Inference of Intelligent Vehicles","year":2022,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","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":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Inference; Computer science; Convolutional neural network; Frame (networking); Artificial intelligence; Feature (linguistics); Feature extraction; Inference engine; Computer vision; Real-time computing; Computer network","score_opus":0.03932472852474372,"score_gpt":0.32735375068673095,"score_spread":0.2880290221619872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296502315","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":[0.9868784,0.0012522497,0.00419824,0.0071719694,0.00023635698,0.000103780665,0.000021960286,0.000008369206,0.00012869669],"genre_scores_gemma":[0.99703,0.0025308926,0.0002297059,0.000074514064,0.00004128937,0.000007782186,0.000009868566,0.000006621629,0.0000692969],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99852026,0.00015922211,0.0003328061,0.000075784454,0.000706459,0.00020546673],"domain_scores_gemma":[0.9995041,0.00013606041,0.00013333354,0.000086933345,0.000047362697,0.00009221801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019367404,0.000061761515,0.0001099988,0.00023295745,0.00017359732,0.000025142586,0.00041039666,0.000030324647,0.00025686293],"category_scores_gemma":[0.00006492906,0.000048832357,0.00003903788,0.00009565566,0.00018872372,0.00014454301,0.00025007338,0.00074305665,0.000001983068],"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":[0.000114799535,0.000648501,0.096078396,0.00003296864,0.00031024608,0.000021215305,0.002201202,0.0007494753,0.0071815155,0.0058811223,0.001300805,0.88547975],"study_design_scores_gemma":[0.0027511334,0.009485455,0.6338769,0.00027273138,0.000015519217,0.0006314359,0.033580035,0.020973172,0.016864618,0.018245695,0.26272532,0.0005779677],"about_ca_topic_score_codex":0.000022389024,"about_ca_topic_score_gemma":0.000011730134,"teacher_disagreement_score":0.88490176,"about_ca_system_score_codex":0.0004847749,"about_ca_system_score_gemma":0.00010642015,"threshold_uncertainty_score":0.32282528},"labels":[],"label_agreement":null},{"id":"W4298946154","doi":"10.1109/jiot.2022.3196639","title":"A Behavior Decision Method Based on Reinforcement Learning for Autonomous Driving","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Autonomous Vehicle Technology and Safety","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 Guelph; Western University","funders":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Markov decision process; Exploit; Function (biology); Collision avoidance; Process (computing); Q-learning; Markov process; Artificial intelligence; Collision; Simulation; Computer security","score_opus":0.011050877867798462,"score_gpt":0.26217559772975013,"score_spread":0.2511247198619517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298946154","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":[0.25299218,0.000031281015,0.7452249,0.00006535111,0.0008144519,0.0001583806,9.509362e-7,0.00018102764,0.0005315081],"genre_scores_gemma":[0.9643315,0.0000042074853,0.03507618,0.00011035715,0.000044084558,0.000061917,0.0000023896375,0.000038414924,0.00033096582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987701,0.00005131237,0.0005033279,0.00014648701,0.00026265037,0.00026608954],"domain_scores_gemma":[0.99926674,0.00026213162,0.00021205572,0.00015138267,0.00004719246,0.00006049836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011379156,0.0001539987,0.00024720517,0.00030094763,0.00021777835,0.00002856662,0.00042084622,0.00008886879,0.00032229567],"category_scores_gemma":[0.000077885765,0.00015796514,0.00019772045,0.000091055044,0.000022574453,0.00012567357,0.00006544852,0.001100672,0.0000041444114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098773955,0.00004019292,0.00042496304,0.000012572207,0.000043015945,0.000022913522,0.0005542943,0.9247011,0.0041419524,0.00015027408,0.00073735655,0.06907262],"study_design_scores_gemma":[0.00072479976,0.0007088881,0.00020839299,0.00006913024,0.00004142286,0.00016272461,0.00010845636,0.9701126,0.02091723,0.00030238475,0.0064792237,0.00016474057],"about_ca_topic_score_codex":0.0000074487107,"about_ca_topic_score_gemma":6.8762364e-7,"teacher_disagreement_score":0.7113393,"about_ca_system_score_codex":0.0003606954,"about_ca_system_score_gemma":0.000040639952,"threshold_uncertainty_score":0.6441632},"labels":[],"label_agreement":null},{"id":"W4299642072","doi":"","title":"Real-time simulator of collaborative autonomous vehicles","year":2014,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","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":"Opal-Rt Technologies (Canada); Université de Sherbrooke","funders":"","keywords":"Computer science; Simulation; Human–computer interaction","score_opus":0.00413702659460863,"score_gpt":0.18942707287838495,"score_spread":0.18529004628377632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299642072","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":[0.8675393,0.00019596449,0.03194117,0.0011633597,0.000058498987,0.00019557335,0.000027075092,0.00096491445,0.09791418],"genre_scores_gemma":[0.9836294,0.0001220951,0.0144984955,0.000013604235,0.000007333564,0.000012820157,0.000030222454,0.000030727897,0.0016552813],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982789,0.0008192514,0.00032710767,0.00022558353,0.00012800371,0.00022111133],"domain_scores_gemma":[0.99758935,0.0008322892,0.00012026488,0.0008121326,0.0005698112,0.000076143595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016408296,0.0001572046,0.0002557303,0.000102089194,0.00013480235,0.000028023966,0.0004371921,0.00017302655,0.00009551577],"category_scores_gemma":[0.00036528963,0.00017085111,0.000064465574,0.00036465342,0.0002175727,0.000103526334,0.00010803734,0.00017871591,0.00008361056],"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":[0.000033707493,0.0005664472,0.004834753,0.00021327386,0.00027443038,0.0000047260696,0.017341735,0.012408124,0.3259449,0.4708548,0.002211989,0.1653111],"study_design_scores_gemma":[0.00068611733,0.0000012780932,0.0071097068,0.00021418328,0.000028019589,0.0000037899247,0.00012447314,0.45328775,0.51954323,0.003180158,0.015453719,0.00036754162],"about_ca_topic_score_codex":0.00006655461,"about_ca_topic_score_gemma":0.00008434606,"teacher_disagreement_score":0.46767464,"about_ca_system_score_codex":0.0000565271,"about_ca_system_score_gemma":0.000059544636,"threshold_uncertainty_score":0.6967106},"labels":[],"label_agreement":null},{"id":"W4301184559","doi":"10.1109/prai55851.2022.9904079","title":"An Overview of Scientometric Mapping in Planning and Decision-Making for UGV","year":2022,"lang":"en","type":"article","venue":"2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Planner; Computer science; Publication; China; Field (mathematics); Unmanned ground vehicle; Operations research; Data science; Geography; Engineering; Artificial intelligence; Political science","score_opus":0.24360771623221394,"score_gpt":0.39253203280303356,"score_spread":0.14892431657081961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4301184559","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":[0.8374593,0.0002704868,0.16061021,0.00017595729,0.00044528698,0.00023230216,0.00022274285,0.00007177736,0.0005118783],"genre_scores_gemma":[0.9986398,0.00018529044,0.00082952343,0.00016897709,0.00002813257,0.00008071785,0.000052515188,0.000011731354,0.0000032850544],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989448,0.00003143336,0.00038833686,0.00026677045,0.0002054368,0.00016318484],"domain_scores_gemma":[0.9994701,0.00023753176,0.00008947998,0.00009038632,0.00007516383,0.00003735248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047303792,0.000117440904,0.00016122195,0.00087235897,0.0001348897,0.000048564198,0.00020175426,0.000055434815,0.00075348845],"category_scores_gemma":[0.0000914432,0.00013430022,0.0000311227,0.00042389182,0.00006481519,0.00015775305,0.00007609472,0.0002508442,0.0000058658543],"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":[0.00004252226,0.00006271183,0.002027452,0.000028896751,0.000014981965,0.0000055953374,0.0005809773,0.0026064098,0.00096949894,0.0072109587,0.000009313864,0.98644066],"study_design_scores_gemma":[0.00014829174,0.00029802794,0.005211134,0.00032441263,0.000008399081,0.000020527574,0.004811692,0.8591755,0.0030575604,0.12632436,0.00030863608,0.00031147813],"about_ca_topic_score_codex":0.000011723567,"about_ca_topic_score_gemma":0.00002474754,"teacher_disagreement_score":0.9861292,"about_ca_system_score_codex":0.00005911104,"about_ca_system_score_gemma":0.0000201536,"threshold_uncertainty_score":0.825017},"labels":[],"label_agreement":null},{"id":"W4303457312","doi":"10.1155/2022/7287511","title":"A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information","year":2022,"lang":"en","type":"review","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Zhengzhou University; Zhengzhou University of Light Industry; National Natural Science Foundation of China","keywords":"Distraction; Computer science; Artificial intelligence; Support vector machine; Distracted driving; Convolutional neural network; Artificial neural network; Machine learning; Deep learning; Feature extraction; Feature (linguistics); Engineering","score_opus":0.040406716467033955,"score_gpt":0.34529880956358805,"score_spread":0.3048920930965541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4303457312","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":[0.000014080474,0.93542165,0.0624179,0.000041172385,0.00057083054,0.0013291937,0.00010111458,0.000088641136,0.000015446323],"genre_scores_gemma":[0.00003476778,0.97060853,0.0283639,0.000096681266,0.000043384156,0.0005016525,0.00031201463,0.00003713024,0.0000019103552],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984107,0.000083336745,0.0011166283,0.000087212655,0.00016014017,0.00014199615],"domain_scores_gemma":[0.9982786,0.00061971194,0.0007981415,0.0001723653,0.00009792522,0.00003325319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007746533,0.00022337017,0.0007282952,0.00021182258,0.00012945032,0.000009680774,0.0001980976,0.00015608268,0.00006437764],"category_scores_gemma":[0.00011578896,0.00016719954,0.0005229359,0.00027038797,0.000018721534,0.000360171,0.0000015168956,0.00070113625,0.0000033975584],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012765744,0.00001713704,3.862645e-7,0.008376613,0.000050281007,0.0000024129206,0.000040678922,0.015274878,0.0000021755413,0.0000067812553,0.00004627158,0.97616965],"study_design_scores_gemma":[0.00040692426,0.0001478345,0.000112564376,0.010346149,0.0017490654,0.0000142925255,0.000038746002,0.0014508205,0.0000146569655,0.000032416687,0.9854903,0.0001961801],"about_ca_topic_score_codex":4.5631552e-7,"about_ca_topic_score_gemma":0.000002286034,"teacher_disagreement_score":0.98544407,"about_ca_system_score_codex":0.00017837953,"about_ca_system_score_gemma":0.0000737145,"threshold_uncertainty_score":0.6818199},"labels":[],"label_agreement":null},{"id":"W4306319527","doi":"10.1016/j.adhoc.2022.103021","title":"STAG: A novel interaction-aware path prediction method based on Spatio-Temporal Attention Graphs for connected automated vehicles","year":2022,"lang":"en","type":"article","venue":"Ad Hoc Networks","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Graph; Benchmark (surveying); Artificial intelligence; Representation (politics); Path (computing); Machine learning; Motion planning; Theoretical computer science; Robot","score_opus":0.010991979332160902,"score_gpt":0.2420432665816751,"score_spread":0.2310512872495142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306319527","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":[0.1970633,0.00016594063,0.794952,0.00021375564,0.0011885803,0.0007621684,0.00031463357,0.005245901,0.00009374332],"genre_scores_gemma":[0.9928013,0.000018316214,0.0049839513,0.00012987769,0.000053654876,0.00049592153,0.0014261132,0.00005859035,0.000032282853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884063,0.00007975048,0.00033439693,0.00029735413,0.00014418263,0.000303697],"domain_scores_gemma":[0.99929714,0.00022647892,0.00010901383,0.00026191692,0.000056625846,0.00004882952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035603347,0.00020236861,0.00021738042,0.00018659976,0.00037870722,0.000020808537,0.00014946479,0.00019356019,0.00008981643],"category_scores_gemma":[0.00001686502,0.00023088025,0.00012257138,0.00039327668,0.00002612917,0.00011730115,0.00003699092,0.00052125216,0.0000033495355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002551401,0.00010166749,0.0015742152,0.00002523864,0.00008722956,0.0000034800303,0.000052429874,0.97849196,0.00028619202,0.0004420727,0.0032586376,0.015421715],"study_design_scores_gemma":[0.00121258,0.00034905676,0.0064708376,0.000030446094,0.000043677705,0.0000072142834,0.00014405724,0.98415107,0.00009704666,0.0001725168,0.0071127885,0.00020871521],"about_ca_topic_score_codex":0.000009632151,"about_ca_topic_score_gemma":0.000033269054,"teacher_disagreement_score":0.795738,"about_ca_system_score_codex":0.00021975032,"about_ca_system_score_gemma":0.000026957276,"threshold_uncertainty_score":0.94150233},"labels":[],"label_agreement":null},{"id":"W4306760804","doi":"10.3390/wevj13100191","title":"Analyzing Performance Effects of Neural Networks Applied to Lane Recognition under Various Environmental Driving Conditions","year":2022,"lang":"en","type":"article","venue":"World Electric Vehicle Journal","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec à Trois-Rivières","keywords":"Computer science; Mean squared error; Adverse weather; Convolutional neural network; Margin (machine learning); Support vector machine; Artificial neural network; Trajectory; Feature (linguistics); Kernel (algebra); Artificial intelligence; Noise (video); Regression; Pattern recognition (psychology); Machine learning; Statistics; Mathematics; Meteorology","score_opus":0.003297036959990658,"score_gpt":0.1728862563738247,"score_spread":0.16958921941383406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306760804","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":[0.9863362,0.0004015966,0.0122618545,0.00008088098,0.0002167314,0.00017418896,0.0000030791068,0.00016854417,0.00035697903],"genre_scores_gemma":[0.99942565,0.00008923496,0.00015723603,0.0001108333,0.00008389004,0.00003710968,0.00001372157,0.000031865227,0.000050456772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893844,0.000050891336,0.00031503348,0.00013968338,0.00016326863,0.0003926619],"domain_scores_gemma":[0.99957675,0.00010003629,0.00009505851,0.00012755896,0.000007520301,0.000093071256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022312066,0.00014637648,0.00021229952,0.00043596,0.0005145388,0.00001628006,0.00019359935,0.000051306437,0.00019526387],"category_scores_gemma":[0.000004445231,0.00016909334,0.00006677153,0.00075349107,0.000022410462,0.00009116199,0.0000620101,0.0010494532,0.000012189668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027035332,0.000067547386,0.007199635,0.000014524683,0.000100925776,0.000021994541,0.000080846,0.8776328,0.04365806,0.000051517185,0.00035344542,0.07079167],"study_design_scores_gemma":[0.0009470103,0.00033811052,0.13121077,0.00002302737,0.00010906879,0.00039540866,0.000058031426,0.855301,0.010443123,0.00052556733,0.00023504338,0.00041378968],"about_ca_topic_score_codex":0.0000016338844,"about_ca_topic_score_gemma":0.0000054568327,"teacher_disagreement_score":0.12401114,"about_ca_system_score_codex":0.00034709336,"about_ca_system_score_gemma":0.000015170629,"threshold_uncertainty_score":0.68954265},"labels":[],"label_agreement":null},{"id":"W4308080232","doi":"10.1109/itsc55140.2022.9921899","title":"Characterizing Structured Versus Unstructured Environments Based on Pedestrians' and Vehicles' Motion Trajectories","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)","topic":"Autonomous Vehicle Technology and Safety","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","funders":"","keywords":"Trajectory; Computer science; Motion (physics); Cluster analysis; Artificial intelligence; Machine learning; Data mining","score_opus":0.02837355751215344,"score_gpt":0.23815440265878965,"score_spread":0.2097808451466362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308080232","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":[0.97597367,0.000057637368,0.013864213,0.0001967802,0.006741838,0.00060053804,0.0009567334,0.00039308445,0.0012155114],"genre_scores_gemma":[0.99858177,0.00007385558,0.00005840333,0.00006911593,0.00008755667,0.00020441506,0.00072199194,0.000043828466,0.0001590776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980466,0.000084326784,0.0005588342,0.00043455866,0.0006249941,0.00025071285],"domain_scores_gemma":[0.999423,0.000051923125,0.00016548576,0.00023627747,0.000039209524,0.000084074476],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019401422,0.00031247822,0.00027361215,0.00031046665,0.00025509443,0.00006263831,0.00032499537,0.00015024153,0.00062758836],"category_scores_gemma":[0.00000936314,0.00034773233,0.00008204941,0.00016705865,0.00007169303,0.00014753279,0.000007850192,0.0005649689,0.000018907675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024499272,0.000246566,0.007776286,0.00019104617,0.00064028444,0.000095452604,0.004088606,0.73726463,0.066994175,0.16209224,0.00023547246,0.017925298],"study_design_scores_gemma":[0.0032534315,0.0007940375,0.044914883,0.00012370844,0.00009583435,0.000016521873,0.003943824,0.9006213,0.03706648,0.00043756422,0.007688074,0.0010443614],"about_ca_topic_score_codex":0.000033357566,"about_ca_topic_score_gemma":0.000025753427,"teacher_disagreement_score":0.16335663,"about_ca_system_score_codex":0.0003281448,"about_ca_system_score_gemma":0.000038016187,"threshold_uncertainty_score":0.9998975},"labels":[],"label_agreement":null},{"id":"W4308141392","doi":"10.1155/2022/6601014","title":"Stacking-Based Ensemble Learning Method for the Recognition of the Pedestrian Crossing Intention","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Pedestrian crossing; Pedestrian; Computer science; Artificial intelligence; Ensemble learning; Support vector machine; Machine learning; Pattern recognition (psychology); Level crossing; Engineering; Transport engineering","score_opus":0.015117057737903518,"score_gpt":0.26030141788004973,"score_spread":0.24518436014214623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308141392","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":[0.49723557,0.00015299021,0.50199425,0.00017670436,0.00027693724,0.00012131037,0.000009152783,0.000024769286,0.000008323335],"genre_scores_gemma":[0.98542494,0.000018868235,0.014471606,0.000017200422,0.000018772284,0.000014128837,0.000013003441,0.00001294202,0.000008551759],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993478,0.00004401589,0.00034944742,0.00004926396,0.00012605629,0.00008339106],"domain_scores_gemma":[0.99933076,0.00017882793,0.00031401785,0.00006252835,0.00010376515,0.000010078517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005158865,0.000058617115,0.00010966661,0.000060957216,0.00034719764,0.000007882826,0.000106743515,0.00003338299,0.000012565648],"category_scores_gemma":[0.000040726027,0.000042873213,0.00013397151,0.000166062,0.00003066209,0.00011913285,0.0000016483527,0.00036110225,8.554346e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001303729,0.000012600128,0.0005039548,0.00003345397,0.000026148733,7.1674344e-7,0.00064690295,0.87981784,0.026679816,0.00004978074,0.0000038108865,0.09209457],"study_design_scores_gemma":[0.007910288,0.0014431917,0.26363438,0.00037157765,0.00081948214,0.00006164868,0.014845827,0.36280188,0.313768,0.016232364,0.017578006,0.0005333323],"about_ca_topic_score_codex":0.0000037371176,"about_ca_topic_score_gemma":0.00003060963,"teacher_disagreement_score":0.517016,"about_ca_system_score_codex":0.000060158345,"about_ca_system_score_gemma":0.000041128413,"threshold_uncertainty_score":0.26703987},"labels":[],"label_agreement":null},{"id":"W4309258624","doi":"10.32920/21576654","title":"Multi-Vehicle Tracking With Road Maps and Car-Following Models","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"McMaster University; Toronto Metropolitan University","funders":"","keywords":"Vehicle tracking system; Exploit; Tracking (education); Computer science; Estimator; State (computer science); Process (computing); Intelligent transportation system; Tracking system; Motion (physics); Real-time computing; Artificial intelligence; Algorithm; Engineering; Mathematics; Kalman filter","score_opus":0.019233597100621513,"score_gpt":0.22154129366091843,"score_spread":0.20230769656029693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309258624","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":[0.93442327,0.0018353892,0.051431123,0.00011587144,0.00031827038,0.00041539068,0.00003563155,0.0028115169,0.008613537],"genre_scores_gemma":[0.98542243,0.00012205897,0.013735266,0.000037156966,0.000020849233,0.000092944334,0.000031105545,0.00008079507,0.00045737228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988323,0.00002258668,0.00024357131,0.00043103492,0.00014142913,0.0003290938],"domain_scores_gemma":[0.99941206,0.000021772194,0.000038705668,0.00045165082,0.000013570124,0.00006223635],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017784868,0.00031798249,0.0003775513,0.00012388123,0.00016351532,0.000041609223,0.0002822256,0.00038109758,0.00007494178],"category_scores_gemma":[0.0000037656414,0.00030671596,0.000090141024,0.00009311567,0.000047640024,0.00013012842,0.00049536064,0.001302878,0.0000047014246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014682943,0.000035857243,0.0028747648,0.00021813328,0.000327301,0.00014453892,0.0011898534,0.96777964,0.00097358203,0.0010873538,0.00007398049,0.02528029],"study_design_scores_gemma":[0.0006890262,0.000030683004,0.0035198545,0.00006896631,0.00009022196,0.000020191066,0.0005899087,0.991247,0.0009937661,0.0016230908,0.0004248616,0.0007024387],"about_ca_topic_score_codex":0.00016493369,"about_ca_topic_score_gemma":0.00009304858,"teacher_disagreement_score":0.05099918,"about_ca_system_score_codex":0.00012750919,"about_ca_system_score_gemma":0.00003692668,"threshold_uncertainty_score":0.9999385},"labels":[],"label_agreement":null},{"id":"W4309258645","doi":"10.32920/21576654.v1","title":"Multi-Vehicle Tracking With Road Maps and Car-Following Models","year":2022,"lang":"en","type":"preprint","venue":"","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":"McMaster University; Toronto Metropolitan University","funders":"","keywords":"Vehicle tracking system; Exploit; Tracking (education); Computer science; Estimator; Process (computing); State (computer science); Intelligent transportation system; Tracking system; Motion (physics); Artificial intelligence; Real-time computing; Computer vision; Algorithm; Engineering; Mathematics; Kalman filter","score_opus":0.019233597100621513,"score_gpt":0.22154129366091843,"score_spread":0.20230769656029693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309258645","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":[0.93442327,0.0018353892,0.051431123,0.00011587144,0.00031827038,0.00041539068,0.00003563155,0.0028115169,0.008613537],"genre_scores_gemma":[0.98542243,0.00012205897,0.013735266,0.000037156966,0.000020849233,0.000092944334,0.000031105545,0.00008079507,0.00045737228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988323,0.00002258668,0.00024357131,0.00043103492,0.00014142913,0.0003290938],"domain_scores_gemma":[0.99941206,0.000021772194,0.000038705668,0.00045165082,0.000013570124,0.00006223635],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017784868,0.00031798249,0.0003775513,0.00012388123,0.00016351532,0.000041609223,0.0002822256,0.00038109758,0.00007494178],"category_scores_gemma":[0.0000037656414,0.00030671596,0.000090141024,0.00009311567,0.000047640024,0.00013012842,0.00049536064,0.001302878,0.0000047014246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014682943,0.000035857243,0.0028747648,0.00021813328,0.000327301,0.00014453892,0.0011898534,0.96777964,0.00097358203,0.0010873538,0.00007398049,0.02528029],"study_design_scores_gemma":[0.0006890262,0.000030683004,0.0035198545,0.00006896631,0.00009022196,0.000020191066,0.0005899087,0.991247,0.0009937661,0.0016230908,0.0004248616,0.0007024387],"about_ca_topic_score_codex":0.00016493369,"about_ca_topic_score_gemma":0.00009304858,"teacher_disagreement_score":0.05099918,"about_ca_system_score_codex":0.00012750919,"about_ca_system_score_gemma":0.00003692668,"threshold_uncertainty_score":0.9999385},"labels":[],"label_agreement":null},{"id":"W4310513210","doi":"10.48550/arxiv.2211.16197","title":"FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Directed acyclic graph; Computer science; Graph; Joint (building); Trajectory; Set (abstract data type); Directed graph; Sequence (biology); Joint probability distribution; Pipeline (software); Artificial intelligence; Theoretical computer science; Machine learning; Algorithm; Mathematics","score_opus":0.07225152161566882,"score_gpt":0.1956734497755,"score_spread":0.12342192815983118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310513210","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":[0.92480975,0.00006836908,0.06692795,0.00002191812,0.0026666839,0.00050188537,0.00010776108,0.00402133,0.00087434094],"genre_scores_gemma":[0.99791837,0.00059532706,0.00014051904,0.000011847495,0.000047091762,0.000009631615,0.00032858306,0.000064072956,0.0008845458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831164,0.0001495831,0.0003312832,0.0007662237,0.00009278577,0.00034849613],"domain_scores_gemma":[0.99891543,0.000037528258,0.00019738928,0.00069276296,0.000053778713,0.00010310123],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000171329,0.0004038907,0.0004038508,0.0005495512,0.0002435458,0.000028535675,0.00037446409,0.00062947965,0.00069018744],"category_scores_gemma":[0.00003314857,0.00052313,0.00030690996,0.00047659499,0.00007631378,0.00026007433,0.00051954045,0.0018033456,0.00006116449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001358289,0.00022510302,0.014955818,0.00015046216,0.00058894174,0.00011057549,0.00038806122,0.97536314,0.0028228515,0.0025701593,0.0003442243,0.0023448276],"study_design_scores_gemma":[0.0012905771,0.000056667148,0.076965995,0.000054784836,0.00021555234,0.0000043354444,0.00025516681,0.9140868,0.00096129626,0.0032175337,0.0023134814,0.000577779],"about_ca_topic_score_codex":0.00015875747,"about_ca_topic_score_gemma":0.00005707304,"teacher_disagreement_score":0.07310861,"about_ca_system_score_codex":0.0011154057,"about_ca_system_score_gemma":0.00003565711,"threshold_uncertainty_score":0.999722},"labels":[],"label_agreement":null},{"id":"W4310972229","doi":"10.1109/iecon49645.2022.9968584","title":"Traffic Enforcement at Intersections Monitored by A Single Fisheye Camera Containing Noisy Detection and Tracking Data","year":2022,"lang":"en","type":"article","venue":"IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society","topic":"Autonomous Vehicle Technology and Safety","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":"McMaster University","funders":"","keywords":"Computer science; Tracking (education); Computer vision; Artificial intelligence","score_opus":0.0358535349065869,"score_gpt":0.2262553523545386,"score_spread":0.1904018174479517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310972229","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":[0.9963205,0.00045069994,0.0006493729,0.00027422523,0.00095970836,0.00039912103,0.00056802755,0.00022324288,0.00015509319],"genre_scores_gemma":[0.9993904,0.00010583069,0.000015644371,0.000049989565,0.00009490141,0.000056080622,0.0000708209,0.000031100015,0.00018519277],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839073,0.00010630959,0.00042126793,0.00036253678,0.00024974602,0.00046942433],"domain_scores_gemma":[0.9991157,0.00008432086,0.00019565449,0.00047792672,0.000065501634,0.00006086406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005132919,0.00022844237,0.0002962536,0.00004424179,0.0007311492,0.00004201999,0.0006981853,0.0002298465,0.00010369269],"category_scores_gemma":[0.000047179667,0.0002354682,0.00012519889,0.0002949143,0.00017146514,0.00026423886,0.00039823452,0.0014990224,8.5472914e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009270012,0.0006611613,0.0012916314,0.00012967066,0.0020661626,0.0000075622684,0.025499035,0.15576011,0.4540688,0.0004803561,0.055581957,0.30352655],"study_design_scores_gemma":[0.0063587693,0.0028759053,0.000107451626,0.00008704878,0.00038681467,0.00017355259,0.03445765,0.67742527,0.1788205,0.00025407376,0.09762013,0.001432846],"about_ca_topic_score_codex":0.00015688491,"about_ca_topic_score_gemma":0.00031357442,"teacher_disagreement_score":0.52166516,"about_ca_system_score_codex":0.00088716764,"about_ca_system_score_gemma":0.00020969327,"threshold_uncertainty_score":0.96021146},"labels":[],"label_agreement":null},{"id":"W4312424509","doi":"10.1109/iros47612.2022.9981421","title":"Vehicle Type Specific Waypoint Generation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Autonomous Vehicle Technology and Safety","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Waypoint; Probabilistic logic; Computer science; Reinforcement learning; Function (biology); Artificial intelligence; Machine learning; Real-time computing","score_opus":0.06451494096416267,"score_gpt":0.25530186405875177,"score_spread":0.1907869230945891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312424509","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":[0.923548,0.0021876772,0.02455794,0.00122461,0.01666855,0.000852189,0.00017645389,0.00084380293,0.029940793],"genre_scores_gemma":[0.9955685,0.00095664436,0.000044791952,0.000094821124,0.00030305196,0.00011601889,0.00010014074,0.00003246248,0.0027835602],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984603,0.00007158532,0.00044877964,0.0003637285,0.00040619922,0.00024943173],"domain_scores_gemma":[0.99943405,0.000031581283,0.00008929838,0.00026785777,0.000097935124,0.00007924821],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003181855,0.00022258009,0.0002370034,0.00020984966,0.00029227525,0.00011043963,0.00036124783,0.00010435414,0.0017724684],"category_scores_gemma":[0.000009492183,0.00022768861,0.00006090002,0.00016001421,0.000060821756,0.00010272056,0.00010341787,0.0005067102,0.00018052619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012233612,0.00020796635,0.0010770472,0.00005402041,0.00029802878,0.000080732185,0.0010733864,0.42928502,0.10673898,0.41607353,0.024922522,0.020066416],"study_design_scores_gemma":[0.00032800192,0.00027339984,0.00039252773,0.000036092217,0.000012566058,0.000081654915,0.0009845435,0.9298811,0.00790111,0.0007449327,0.05891916,0.00044492926],"about_ca_topic_score_codex":0.00004231633,"about_ca_topic_score_gemma":0.000013313594,"teacher_disagreement_score":0.50059605,"about_ca_system_score_codex":0.00029991104,"about_ca_system_score_gemma":0.000031208474,"threshold_uncertainty_score":0.99914},"labels":[],"label_agreement":null},{"id":"W4312609010","doi":"10.1109/tvt.2022.3217299","title":"Exploiting Multi-Modal Fusion for Urban Autonomous Driving Using Latent Deep Reinforcement Learning","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":33,"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","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Modal; Artificial intelligence; Computer science; Machine learning; Perception; Sensor fusion; Deep learning","score_opus":0.014320128419097801,"score_gpt":0.22350890265175433,"score_spread":0.20918877423265653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312609010","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":[0.34008265,0.00012522774,0.65649694,0.00013430463,0.00038645844,0.0004578304,0.000004156575,0.0022872603,0.000025157831],"genre_scores_gemma":[0.9900841,0.00003479288,0.008910128,0.000039642196,0.000023944529,0.00066181854,0.00001104128,0.00010277825,0.00013171502],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981796,0.00004448254,0.00048591584,0.00044825554,0.00018495423,0.00065677735],"domain_scores_gemma":[0.99931496,0.00005690822,0.00010398614,0.00041459603,0.000047406018,0.000062110936],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0002724858,0.00032366344,0.00035150137,0.00077580503,0.0014484125,0.000017431295,0.00037157163,0.00039433845,0.00010862037],"category_scores_gemma":[0.000010080569,0.00039623736,0.00020262068,0.0005700855,0.00011055383,0.000113976734,0.000019014808,0.0014425806,0.000014880845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019063038,0.00008024391,0.00012862452,0.000025040557,0.00010767557,0.00002139038,0.0002081179,0.93191665,0.044743337,0.00033070927,0.0000050899635,0.022414042],"study_design_scores_gemma":[0.000925164,0.00029177577,0.000022339063,0.000022284863,0.00007081671,0.000097454154,0.0006353342,0.9280023,0.06762246,0.000115080395,0.0018012883,0.0003936903],"about_ca_topic_score_codex":0.000011946078,"about_ca_topic_score_gemma":0.000015560701,"teacher_disagreement_score":0.65000147,"about_ca_system_score_codex":0.0007071244,"about_ca_system_score_gemma":0.000041532356,"threshold_uncertainty_score":0.9998516},"labels":[],"label_agreement":null},{"id":"W4312731878","doi":"10.1109/cvpr52688.2022.00862","title":"HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":391,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Artificial intelligence; Invariant (physics); Transformer; Benchmark (surveying); Machine learning; Mathematics; Engineering","score_opus":0.044621562810097604,"score_gpt":0.2561934175399021,"score_spread":0.2115718547298045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312731878","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":[0.269761,0.00003170818,0.72609264,0.0006194627,0.0014557544,0.000628192,0.0006451871,0.00055639277,0.00020969582],"genre_scores_gemma":[0.9967629,0.00014441328,0.0014377905,0.00052253134,0.00015831934,0.0003284208,0.0005319137,0.000038285558,0.000075424156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986094,0.00009982035,0.00036042277,0.00042442602,0.00021238082,0.00029353987],"domain_scores_gemma":[0.99953854,0.000068949856,0.00005624701,0.00017547153,0.000054917247,0.00010585265],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023689854,0.00024896793,0.0002609804,0.00021117003,0.00039292302,0.00005356629,0.00015230868,0.00015358752,0.00081614614],"category_scores_gemma":[0.000003049621,0.00025660993,0.00011007703,0.00012542767,0.00005373205,0.00014191472,0.00003587027,0.0005646557,0.000056531586],"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":[0.000105506595,0.00021232282,0.00023196885,0.000086629705,0.000055066055,0.000008868852,0.00040632405,0.0011789089,0.0029338521,0.00014402995,0.0019217371,0.99271476],"study_design_scores_gemma":[0.00199442,0.0010512769,0.006957222,0.000063787724,0.000033701537,0.00004482615,0.00007370546,0.9810554,0.0022902444,0.0006375902,0.0054193814,0.000378462],"about_ca_topic_score_codex":0.0000047831777,"about_ca_topic_score_gemma":0.000010303985,"teacher_disagreement_score":0.99233633,"about_ca_system_score_codex":0.00008882249,"about_ca_system_score_gemma":0.000018434965,"threshold_uncertainty_score":0.9999886},"labels":[],"label_agreement":null},{"id":"W4312835009","doi":"10.1109/tits.2022.3227122","title":"A Hybrid Driving Decision-Making System Integrating Markov Logic Networks and Connectionist AI","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; General Dynamics","keywords":"Interpretability; Computer science; Connectionism; Artificial intelligence; Markov decision process; Artificial neural network; Inference; Machine learning; Markov process; Mathematics","score_opus":0.007415804425911917,"score_gpt":0.2199766306523436,"score_spread":0.2125608262264317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312835009","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":[0.092625014,0.00036278262,0.90230846,0.000026087764,0.002576869,0.00049965264,0.000081909624,0.0012841267,0.00023511171],"genre_scores_gemma":[0.99882454,0.00007653881,0.000435482,0.000046416517,0.000043764776,0.00044202484,0.000018169416,0.00006331245,0.000049727354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980985,0.00009374442,0.00079941616,0.00039679444,0.00028267343,0.00032885512],"domain_scores_gemma":[0.9991623,0.00031064826,0.00011104026,0.0002703209,0.000064639156,0.000081063255],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004135913,0.000297749,0.00035990524,0.00033586376,0.0010190097,0.000084363426,0.00018984781,0.00012876285,0.000108021515],"category_scores_gemma":[0.000002642551,0.00032637492,0.00013693055,0.00036225663,0.00006432693,0.00016297225,9.884607e-7,0.0008612621,0.000012912357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046940546,0.000037189933,0.00014801693,0.00009954235,0.000107928725,0.000033574663,0.00041529821,0.9685951,0.00007765839,0.0022647537,0.000059281425,0.028114736],"study_design_scores_gemma":[0.00027009362,0.00013907946,0.0001928272,0.00034509008,0.000085801636,0.00022542088,0.005824759,0.99051875,0.0008442167,0.00009357695,0.001038033,0.0004223582],"about_ca_topic_score_codex":0.00006233086,"about_ca_topic_score_gemma":0.00014681827,"teacher_disagreement_score":0.9061996,"about_ca_system_score_codex":0.00041383802,"about_ca_system_score_gemma":0.000025722285,"threshold_uncertainty_score":0.9999188},"labels":[],"label_agreement":null},{"id":"W4312965377","doi":"10.1109/iros47612.2022.9981046","title":"MPC-PF: Social Interaction Aware Trajectory Prediction of Dynamic Objects for Autonomous Driving Using Potential Fields","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Autonomous Vehicle Technology and Safety","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 Alberta; University of Waterloo","funders":"","keywords":"Social force model; Trajectory; Computer science; Set (abstract data type); Field (mathematics); Task (project management); Variety (cybernetics); Object (grammar); Artificial intelligence; Motion planning; Motion (physics); Time horizon; Machine learning; Mathematical optimization; Engineering; Mathematics","score_opus":0.03821510044327424,"score_gpt":0.2751037109590618,"score_spread":0.23688861051578752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312965377","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":[0.75658566,0.00017739936,0.2330177,0.0001450454,0.007774007,0.00067511015,0.00039529486,0.00030416108,0.00092561776],"genre_scores_gemma":[0.99894667,0.00008058484,0.00006899113,0.000019424662,0.0001854459,0.00015902682,0.00011909185,0.000032737793,0.00038804358],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851567,0.000066510045,0.0005590612,0.00031974085,0.00030595163,0.00023306496],"domain_scores_gemma":[0.999438,0.000055540615,0.00020767597,0.00014162897,0.00011155039,0.000045625402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027701055,0.00021575716,0.00030108474,0.0003014646,0.0003431716,0.000054203505,0.00026528558,0.00017178051,0.00024305568],"category_scores_gemma":[0.000013285661,0.00024162178,0.00013354576,0.00010014197,0.00006191606,0.00014312324,0.00007482609,0.00046655102,0.0000028622553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024575682,0.00024151929,0.00083844137,0.0003115889,0.0006369611,0.000021209864,0.002843462,0.9040184,0.055616036,0.013990006,0.0007440026,0.020492617],"study_design_scores_gemma":[0.00031714674,0.000198794,0.0005958502,0.00007189751,0.00003829722,0.000056203135,0.0026431056,0.99347293,0.0018299232,0.0002528383,0.0003209359,0.0002020605],"about_ca_topic_score_codex":0.0000870535,"about_ca_topic_score_gemma":0.00004148008,"teacher_disagreement_score":0.24236098,"about_ca_system_score_codex":0.00049444323,"about_ca_system_score_gemma":0.00006401702,"threshold_uncertainty_score":0.985305},"labels":[],"label_agreement":null},{"id":"W4312991107","doi":"10.1007/978-3-031-19842-7_16","title":"Rethinking Closed-Loop Training for Autonomous Driving","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto","funders":"","keywords":"Closed loop; Training (meteorology); Loop (graph theory); Computer science; Control theory (sociology); Psychology; Aeronautics; Artificial intelligence; Control engineering; Engineering; Mathematics; Geography; Control (management); Meteorology","score_opus":0.021753238671766397,"score_gpt":0.22989596246453475,"score_spread":0.20814272379276835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312991107","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":[0.0013370056,0.0004766932,0.9880873,0.00028580698,0.0017654927,0.00043249782,0.000011585104,0.0010384957,0.006565128],"genre_scores_gemma":[0.8276491,0.00005503131,0.17083098,0.0004627177,0.00044101587,0.000053426327,0.000019098949,0.00013482885,0.0003537696],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978507,0.000010013247,0.00042589149,0.0007558996,0.00031047684,0.0006469815],"domain_scores_gemma":[0.9987227,0.00046210867,0.00010985635,0.0005940643,0.000041485102,0.00006982599],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00079530576,0.0004104887,0.00048202675,0.00054913375,0.00043545492,0.000079349564,0.0011854038,0.0004697394,0.00010120358],"category_scores_gemma":[0.00006250242,0.00045703622,0.00013550192,0.00023303836,0.00040081667,0.00018735956,0.00038848774,0.0014965563,0.000007173933],"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":[0.0000029192888,0.0000038096596,0.0000152040775,0.00004243051,0.000019185263,0.000031799635,0.0021762466,0.28048092,0.00016269664,0.012090691,0.0000086595,0.7049654],"study_design_scores_gemma":[0.00023727781,0.00010907544,0.000047857062,0.0001724922,0.000017149407,0.00006433528,8.6900263e-7,0.7885245,0.0008429489,0.19841576,0.010861966,0.00070575497],"about_ca_topic_score_codex":0.0000034886398,"about_ca_topic_score_gemma":0.000051064,"teacher_disagreement_score":0.8263121,"about_ca_system_score_codex":0.00055618206,"about_ca_system_score_gemma":0.00025489606,"threshold_uncertainty_score":0.99978817},"labels":[],"label_agreement":null},{"id":"W4313001420","doi":"10.1109/iros47612.2022.9981588","title":"Behaviour Learning with Adaptive Motif Discovery and Interacting Multiple Model","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Interpretability; Motif (music); Computer science; Artificial intelligence; Estimator; Machine learning; Entropy (arrow of time); Gaussian; Mathematics; Statistics","score_opus":0.03427360411468759,"score_gpt":0.24089392242224497,"score_spread":0.2066203183075574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313001420","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":[0.8852234,0.00025454484,0.10912933,0.00014393855,0.0010498981,0.00036570287,0.00009417789,0.0002809318,0.0034580822],"genre_scores_gemma":[0.9968245,0.0002009787,0.00013781093,0.000027631324,0.000052248146,0.00016239352,0.000036491918,0.00003867161,0.002519279],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856895,0.00006726777,0.00036686397,0.0004018948,0.00033868128,0.00025633196],"domain_scores_gemma":[0.99945676,0.00010603237,0.0001350401,0.0001576364,0.000068499714,0.00007602794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025535942,0.000260561,0.00027883504,0.00020339343,0.00034728533,0.00015562549,0.00025560963,0.00008449753,0.00010073776],"category_scores_gemma":[0.000021458614,0.0002420548,0.000045858575,0.0000875863,0.000084254476,0.0002683349,0.00016291068,0.0008823901,0.000007804117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000132888,0.000055535133,0.012731929,0.000026195608,0.00015323467,0.000034734854,0.0011394351,0.9566679,0.0019556032,0.024455616,0.000092620445,0.0025543084],"study_design_scores_gemma":[0.00028810732,0.00023583547,0.00067290646,0.00010430205,0.000019395426,0.00009489124,0.004542547,0.9928048,0.00060149276,0.00014257744,0.0002068483,0.0002862973],"about_ca_topic_score_codex":0.00014539348,"about_ca_topic_score_gemma":0.00004966857,"teacher_disagreement_score":0.1116011,"about_ca_system_score_codex":0.00020917061,"about_ca_system_score_gemma":0.000033034277,"threshold_uncertainty_score":0.9870708},"labels":[],"label_agreement":null},{"id":"W4313022330","doi":"10.1007/978-3-031-10268-4","title":"Intelligent Tire Systems","year":2022,"lang":"en","type":"book","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Business","score_opus":0.00941406725186536,"score_gpt":0.18699950107411384,"score_spread":0.17758543382224848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313022330","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001027869,0.003706869,0.0009878764,0.000019223466,0.0010789663,0.0001884693,0.000024883217,0.0024730603,0.9915104],"genre_scores_gemma":[0.003426155,0.00039943864,0.00002323974,0.000022921822,0.00009378175,0.000049936214,0.000085743,0.00007192461,0.99582684],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993488,0.0000065891377,0.00021646863,0.00015454278,0.000101039506,0.00017255059],"domain_scores_gemma":[0.9995762,0.000024264476,0.000024874817,0.000335698,0.000008036561,0.000030928688],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000074598356,0.00019210635,0.00025169004,0.00010544317,0.000055975896,0.000008696792,0.0002694974,0.00038643728,0.0036621066],"category_scores_gemma":[0.0000017337911,0.00019691416,0.00007304167,0.000034486573,0.000030189529,0.000016838125,0.00009311418,0.00071307056,0.0007550351],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002026362,0.00000794333,0.0000043691057,0.0002811534,0.00023608468,0.00007001359,0.00006414106,0.011999582,0.0000029104447,0.20063557,0.729576,0.057120185],"study_design_scores_gemma":[0.000025978874,0.0000156894,0.0000012180284,0.000017190296,0.000016138336,0.00001972252,0.000027313383,0.013299402,0.000012319616,0.0005581516,0.9857886,0.0002183068],"about_ca_topic_score_codex":0.0000038316853,"about_ca_topic_score_gemma":0.0000035218122,"teacher_disagreement_score":0.25621256,"about_ca_system_score_codex":0.00044590907,"about_ca_system_score_gemma":0.0000579693,"threshold_uncertainty_score":0.9972487},"labels":[],"label_agreement":null},{"id":"W4313524847","doi":"10.1109/sec54971.2022.00029","title":"Poster: Distracted Driving Management","year":2022,"lang":"en","type":"article","venue":"","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":"McMaster University","funders":"","keywords":"Distracted driving; Computer science; Control (management); Collision; Computer security; Simulation; Distraction; Artificial intelligence; Psychology","score_opus":0.0031369792675730876,"score_gpt":0.16829788049228103,"score_spread":0.16516090122470795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313524847","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":[0.69434863,0.000055993776,0.0041695563,0.00034380466,0.00025676138,0.00012724032,0.0000050167705,0.0026906254,0.29800236],"genre_scores_gemma":[0.9977131,0.000006540436,0.00034065917,0.00005247927,0.0000056130734,0.000027801834,0.00000749007,0.000010706474,0.0018355965],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999667,0.000005491736,0.00007864433,0.00007196296,0.000054064618,0.00012283839],"domain_scores_gemma":[0.9998351,0.0000064126402,0.000005934113,0.00013586036,0.0000018076628,0.000014906555],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000048426682,0.000051019346,0.00004798811,0.000042116517,0.00010572249,0.0000042839706,0.00012552651,0.000018835519,0.0011502254],"category_scores_gemma":[5.1002604e-7,0.000055615383,0.000019345911,0.00009095912,0.0000092756545,0.00002910641,0.00011233181,0.00014699972,0.000050666516],"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":[0.000030227735,0.00031849399,0.046275105,0.00016808983,0.00074666616,0.0007368356,0.0008229234,0.077525266,0.007901911,0.22973722,0.03596958,0.5997677],"study_design_scores_gemma":[0.0010985557,0.00013096575,0.40335864,0.000009584314,0.000059050184,0.00015344864,0.0014809676,0.068241976,0.0061836923,0.004212416,0.51405704,0.0010136898],"about_ca_topic_score_codex":0.0000013513763,"about_ca_topic_score_gemma":0.0000027360904,"teacher_disagreement_score":0.598754,"about_ca_system_score_codex":0.00005543584,"about_ca_system_score_gemma":0.0000013538802,"threshold_uncertainty_score":0.99976283},"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":"W4314945994","doi":"10.1109/cdc51059.2022.9992673","title":"Distracted Drivers Detection in Mixed Vehicle Platoons Using Velocity Measurements Only","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 61st Conference on Decision and Control (CDC)","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":"Memorial University of Newfoundland","funders":"","keywords":"Automotive engineering; Computer science; Aeronautics; Engineering","score_opus":0.030185078067814477,"score_gpt":0.23453245654898497,"score_spread":0.2043473784811705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4314945994","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":[0.9825605,0.00007363831,0.015752476,0.00008036051,0.0005280983,0.0002783752,0.000054355,0.0002367068,0.00043549415],"genre_scores_gemma":[0.99964666,0.00003141776,0.00010898356,0.00010295385,0.000015686976,0.00003964542,0.000005781668,0.00002201166,0.000026871898],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985254,0.00009884411,0.00034867993,0.00034598284,0.00035893146,0.00032220507],"domain_scores_gemma":[0.999433,0.00010499335,0.00007162946,0.00026012742,0.00004237038,0.0000878735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000400513,0.00021163985,0.00030541327,0.00023985408,0.000369063,0.000039274804,0.00023095701,0.00014989775,0.000262077],"category_scores_gemma":[0.000047721707,0.00023379068,0.00006169443,0.00032625455,0.0000632921,0.00012510821,0.000054677923,0.00070589397,0.000018157129],"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":[0.0010284375,0.00029349417,0.01306776,0.000019041669,0.00015150935,0.00009889065,0.00034925263,0.04457045,0.2354692,0.00083149347,0.00015961433,0.70396084],"study_design_scores_gemma":[0.0054781265,0.00025587928,0.14420494,0.000040069677,0.000045659213,0.000034836838,0.0005845363,0.8406301,0.0054202783,0.0014649036,0.0013177288,0.0005229381],"about_ca_topic_score_codex":0.00005480227,"about_ca_topic_score_gemma":0.0005135874,"teacher_disagreement_score":0.79605967,"about_ca_system_score_codex":0.00030820668,"about_ca_system_score_gemma":0.000071742004,"threshold_uncertainty_score":0.9533707},"labels":[],"label_agreement":null},{"id":"W4317632833","doi":"10.2514/6.2023-2192","title":"Attack-tolerant Trajectory Prediction using Generative Adversarial Network Secured by Blockchain Application to the UAS-S4 Ehécatl","year":2023,"lang":"en","type":"article","venue":"AIAA SCITECH 2023 Forum","topic":"Autonomous Vehicle Technology and Safety","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":"École de Technologie Supérieure","funders":"","keywords":"Adversarial system; Computer science; Trajectory; Artificial neural network; Artificial intelligence; Data mining; Blockchain; Deep learning; Machine learning; Computer security","score_opus":0.00970730986334685,"score_gpt":0.22487959401039756,"score_spread":0.2151722841470507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317632833","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":[0.54546577,0.0005834744,0.4394531,0.0048450595,0.0023794915,0.0018791109,0.0003796681,0.0043775085,0.00063681626],"genre_scores_gemma":[0.9975671,0.00003385082,0.0008156776,0.00027010302,0.0005390537,0.00024469613,0.00013708531,0.000062343606,0.0003300865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844646,0.000047406713,0.00031939612,0.00035827237,0.0002079631,0.0006205164],"domain_scores_gemma":[0.9993368,0.0000617379,0.00005493082,0.00042826496,0.000037275917,0.00008103341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004374193,0.00023115776,0.00021043107,0.00013179972,0.0005022389,0.000027606136,0.00033738796,0.00031309848,0.00002211768],"category_scores_gemma":[0.000017703209,0.00020762131,0.00008283352,0.0012482874,0.00007220754,0.000074466996,0.00011211424,0.00039997185,0.00022912273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023377173,0.000011359929,0.00034155525,0.000009378544,0.00008157027,0.0000027696613,0.00036977197,0.8747996,0.015529315,0.00038779477,0.10147307,0.006970435],"study_design_scores_gemma":[0.00030533978,0.000055509572,0.0005831226,0.000019694877,0.000030319543,0.0000061016412,0.00031434486,0.93137527,0.0038517076,0.00044527667,0.06276829,0.00024505157],"about_ca_topic_score_codex":0.000044535813,"about_ca_topic_score_gemma":0.00018348485,"teacher_disagreement_score":0.45210132,"about_ca_system_score_codex":0.00018830877,"about_ca_system_score_gemma":0.000035990466,"threshold_uncertainty_score":0.84665513},"labels":[],"label_agreement":null},{"id":"W4317892891","doi":"10.1109/iccspa55860.2022.10019159","title":"Road Boundary Detection using Camera and mmwave Radar","year":2022,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Ontario Tech University","funders":"","keywords":"Visibility; Lidar; Computer science; Radar; Remote sensing; Object detection; Extremely high frequency; Weather radar; Artificial intelligence; Computer vision; Environmental science; Meteorology; Geology; Geography; Telecommunications","score_opus":0.006940414318273064,"score_gpt":0.1897490663730966,"score_spread":0.18280865205482355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317892891","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":[0.9843718,0.00043507176,0.011500583,0.000046538393,0.00017191157,0.000059377588,0.0000029467078,0.00062516384,0.00278662],"genre_scores_gemma":[0.99883723,0.000014163451,0.0009477429,0.000048065827,0.00001536954,0.000006397292,0.0000012301502,0.000012946296,0.00011686771],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996578,0.000011425174,0.00007965738,0.00008828054,0.000046593676,0.00011621717],"domain_scores_gemma":[0.9998723,0.000006593848,0.00000903569,0.00009012421,0.000003570396,0.00001832461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000059703918,0.0000609644,0.00006890811,0.00005736171,0.0002819907,0.000008050866,0.000043358803,0.000041792217,0.00018195275],"category_scores_gemma":[0.000001983447,0.00006867215,0.000015279827,0.00009196614,0.00003424722,0.000054688815,0.000057389534,0.00021588567,0.0000041589788],"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":[0.000033190714,0.0000326203,0.001476941,0.00004006804,0.00013061555,0.000054444386,0.00076119776,0.02715533,0.17078091,0.0017124948,0.00025199214,0.79757017],"study_design_scores_gemma":[0.000613993,0.00012545004,0.010990996,0.0000034895054,0.000033707413,0.00050431996,0.0011226111,0.8917608,0.038535114,0.0020727625,0.053750835,0.0004859034],"about_ca_topic_score_codex":0.00006763836,"about_ca_topic_score_gemma":0.000023473427,"teacher_disagreement_score":0.8646055,"about_ca_system_score_codex":0.0000904824,"about_ca_system_score_gemma":0.000009757702,"threshold_uncertainty_score":0.2800369},"labels":[],"label_agreement":null},{"id":"W4318566874","doi":"10.1016/j.adhoc.2023.103101","title":"CCPAV: Centralized cooperative perception for autonomous vehicles using CV2X","year":2023,"lang":"en","type":"article","venue":"Ad Hoc Networks","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Perception; Computer science; Human–computer interaction; Business; Psychology; Neuroscience","score_opus":0.018636154709051177,"score_gpt":0.24896346654885673,"score_spread":0.23032731183980557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318566874","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":[0.8427222,0.0013765586,0.1506614,0.00020966187,0.0007323392,0.00059189094,0.000025545787,0.0033614177,0.00031899853],"genre_scores_gemma":[0.9961864,0.0009165103,0.0021308532,0.00007797972,0.00016793198,0.00006866088,0.00009024882,0.00006338379,0.00029803714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989224,0.000024792758,0.00024177642,0.00022943314,0.0000621055,0.00051948265],"domain_scores_gemma":[0.99956745,0.00008881444,0.000032338314,0.00021064212,0.00003908828,0.00006168308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017905516,0.0001922765,0.00023804446,0.00008476113,0.00023772477,0.000027480624,0.00015353759,0.00031546588,0.000050665003],"category_scores_gemma":[0.000012705146,0.00020301886,0.000090016765,0.00033351174,0.000076261655,0.00012664347,0.000039780436,0.00026755387,0.00007541613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038889346,0.000013478603,0.00021527725,0.000022205153,0.000081162216,0.000008334482,0.00041928815,0.9114994,0.0030813664,0.0006620901,0.0030151228,0.08094341],"study_design_scores_gemma":[0.00058731576,0.000046249163,0.002300275,0.000024238778,0.000030794366,0.00000642247,0.00019704699,0.9837593,0.0003129531,0.0003318232,0.012154705,0.00024890772],"about_ca_topic_score_codex":0.0000029508813,"about_ca_topic_score_gemma":0.00001765881,"teacher_disagreement_score":0.1534642,"about_ca_system_score_codex":0.0001426142,"about_ca_system_score_gemma":0.000023290213,"threshold_uncertainty_score":0.8278869},"labels":[],"label_agreement":null},{"id":"W4318764471","doi":"10.1155/2023/3215817","title":"Risk Assessment and Enhancement Suggestions for Automated Driving Systems through Examining Testing Collision and Disengagement Reports","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science and Technology Council; Ministry of Science and Technology, Taiwan","keywords":"Disengagement theory; Fault tree analysis; sort; Computer science; Key (lock); Software; Collision; Computer security; Fault (geology); Risk analysis (engineering); Reliability (semiconductor); Engineering; Reliability engineering; Power (physics); Database","score_opus":0.01825620090725522,"score_gpt":0.28360199147666637,"score_spread":0.26534579056941116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318764471","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":[0.89799744,0.00026322957,0.10072821,0.000037896956,0.0003272186,0.00026443804,0.0000087306435,0.00035273307,0.000020139856],"genre_scores_gemma":[0.9569603,0.0006973424,0.042235956,0.0000021323365,0.000025252635,0.000033505134,0.000020401518,0.00001750259,0.0000075911003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9990186,0.000018543136,0.0005771748,0.00011949421,0.0001236011,0.0001426014],"domain_scores_gemma":[0.9992078,0.00025316695,0.00032476604,0.000071023,0.00010581871,0.00003745159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005421517,0.000104590894,0.00020211138,0.00010206194,0.00019045902,0.000020473903,0.000028667202,0.000058831134,6.8055857e-7],"category_scores_gemma":[0.00006816057,0.00010109393,0.000021896061,0.00019357454,0.000022682707,0.00032614503,0.0000030446167,0.0001754314,1.7142402e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010499151,0.000016339678,0.035342354,0.00016005248,0.00007373342,0.000051089977,0.0008253288,0.9394039,0.01519856,0.0001961548,0.00004155429,0.008680418],"study_design_scores_gemma":[0.00062769133,0.0002460037,0.80549,0.0003707354,0.000117701136,0.000032487107,0.001535821,0.18959531,0.0010285162,0.0005450368,0.00026825527,0.00014240656],"about_ca_topic_score_codex":0.0000025239103,"about_ca_topic_score_gemma":0.0000051400357,"teacher_disagreement_score":0.7701477,"about_ca_system_score_codex":0.00006893443,"about_ca_system_score_gemma":0.000021487816,"threshold_uncertainty_score":0.4122491},"labels":[],"label_agreement":null},{"id":"W4319150617","doi":"10.1145/3526072.3527531","title":"AmbieGen tool at the SBST 2022 tool competition","year":2022,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Polytechnique Montréal","funders":"","keywords":"Context (archaeology); Competition (biology); Test (biology); Computer science; Reliability engineering; Simulation; Engineering","score_opus":0.004407132927205286,"score_gpt":0.1721165674259611,"score_spread":0.1677094344987558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319150617","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":[0.9512666,0.000292925,0.0022025595,0.0016875343,0.00038169476,0.00023191821,0.000039526447,0.0013088485,0.0425884],"genre_scores_gemma":[0.9932718,0.000023868606,0.00008268215,0.00036124946,0.000024937952,0.000099693774,0.00003153712,0.000015747324,0.006088441],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994812,0.000023752895,0.00012542705,0.00010460585,0.00010297387,0.00016205858],"domain_scores_gemma":[0.9996672,0.000033401433,0.000012879459,0.00027054106,0.000005719183,0.000010220564],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016254577,0.00007724107,0.00007526486,0.000028668672,0.000406305,0.0000067428527,0.00019663578,0.00004054179,0.0101854075],"category_scores_gemma":[0.000003956304,0.00006367725,0.000042294578,0.00012903719,0.000041170002,0.000038695853,0.00019989228,0.0002666258,0.00031640372],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000776955,0.00014061815,0.005810542,0.000060212515,0.00027107552,0.00006144489,0.0009477428,0.23141219,0.048344273,0.4191197,0.22217542,0.07157912],"study_design_scores_gemma":[0.000536999,0.0000868312,0.0164392,0.000002504024,0.000022199045,0.0000870431,0.0005818154,0.061656892,0.014522242,0.0029646645,0.9026547,0.00044489707],"about_ca_topic_score_codex":0.000010521236,"about_ca_topic_score_gemma":0.0000519669,"teacher_disagreement_score":0.6804793,"about_ca_system_score_codex":0.00016220844,"about_ca_system_score_gemma":0.000007957626,"threshold_uncertainty_score":0.99071944},"labels":[],"label_agreement":null},{"id":"W4319988646","doi":"10.1007/978-981-19-7528-8_2","title":"Vehicle Detection Using Deep Learning Method and Adaptive and Dynamic Automated Traffic System via IoT Using Surveillance Camera","year":2023,"lang":"en","type":"book-chapter","venue":"Studies in autonomic, data-driven and industrial computing","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Internet of Things; Process (computing); Computer science; Real-time computing; Interconnection; Artificial intelligence; Embedded system; Computer network","score_opus":0.08304185754268023,"score_gpt":0.3061587594072746,"score_spread":0.22311690186459437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319988646","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":[0.9544676,0.0068229535,0.030614156,0.000022247374,0.001996373,0.0011487402,0.00021084602,0.00425655,0.00046055735],"genre_scores_gemma":[0.99455285,0.000540458,0.0044202097,0.0000037024274,0.00021755327,0.0000031386128,0.000057345587,0.00013500091,0.00006972607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99756664,0.00016233593,0.0008239321,0.0008629189,0.00011082458,0.00047335235],"domain_scores_gemma":[0.9986613,0.0005239603,0.0003609868,0.00033857583,0.000042749867,0.000072438765],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010286502,0.00057258987,0.0011923427,0.00040529622,0.0006685624,0.00007562514,0.0002328338,0.0007961954,0.0000010467145],"category_scores_gemma":[0.00007174165,0.0006543761,0.000047130583,0.00013836102,0.0004280953,0.00012606574,0.0010082455,0.001556894,0.00000228061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005061373,0.000004365429,0.00020508982,0.00024839764,0.0008143348,0.000057683246,0.00080762076,0.76021564,0.0003504268,0.00036242942,0.0000043337955,0.23687904],"study_design_scores_gemma":[0.0008471659,0.000071313196,0.00022487332,0.0006837565,0.00013175918,0.00010573727,0.00086417096,0.99623495,0.000011615819,0.000042821735,0.00020097598,0.0005808532],"about_ca_topic_score_codex":0.00010294159,"about_ca_topic_score_gemma":0.0002836203,"teacher_disagreement_score":0.23629819,"about_ca_system_score_codex":0.00075576454,"about_ca_system_score_gemma":0.000055210894,"threshold_uncertainty_score":0.99959075},"labels":[],"label_agreement":null},{"id":"W4320086381","doi":"10.48550/arxiv.2206.01601","title":"A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pedestrian; Computer science; Replicate; Process (computing); Planner; Scenario testing; Social force model; Routing (electronic design automation); Fidelity; Simulation; Artificial intelligence; Transport engineering; Engineering; Embedded system","score_opus":0.09054647525105733,"score_gpt":0.23038767066052104,"score_spread":0.13984119540946371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320086381","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":[0.9742892,0.000018141864,0.023485718,0.000017897193,0.00018512814,0.00072250096,0.00012422768,0.00078124536,0.0003759384],"genre_scores_gemma":[0.99889284,0.000024955492,0.00025950174,0.000017504357,0.000032442153,0.00003706435,0.00018415236,0.000062490384,0.0004890478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841744,0.00007806196,0.00031404657,0.0007151859,0.00007478922,0.0004004615],"domain_scores_gemma":[0.9990439,0.00003554459,0.00006560963,0.00067968253,0.000028491935,0.00014679921],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016527221,0.00033943442,0.00037987725,0.00059446186,0.00027428014,0.000018722962,0.00060898217,0.00052818545,0.00011691858],"category_scores_gemma":[0.00001245908,0.00048267105,0.00014440056,0.00044913663,0.000073101524,0.00007722222,0.0007046337,0.0014511831,0.000016607064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003180068,0.00012902015,0.0025660761,0.000033746688,0.00001818883,0.00050418056,0.00022966514,0.9919207,0.00012855127,0.003856994,0.0000142177205,0.00056685694],"study_design_scores_gemma":[0.00042598092,0.000054358345,0.009054796,0.000016031521,0.0001477072,0.000002121141,0.000041367734,0.9883526,0.000016934297,0.0013637671,0.00005034835,0.00047398172],"about_ca_topic_score_codex":0.0000964935,"about_ca_topic_score_gemma":0.00041530657,"teacher_disagreement_score":0.024603639,"about_ca_system_score_codex":0.0007011649,"about_ca_system_score_gemma":0.00009279986,"threshold_uncertainty_score":0.9997625},"labels":[],"label_agreement":null},{"id":"W4321021972","doi":"10.1109/tits.2023.3243004","title":"MPC-PF: Socially and Spatially Aware Object Trajectory Prediction for Autonomous Driving Systems Using Potential Fields","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Waterloo","funders":"","keywords":"Trajectory; Social force model; Computer science; Set (abstract data type); Field (mathematics); Artificial intelligence; Task (project management); Object (grammar); Motion planning; State space; Motion (physics); Variety (cybernetics); Time horizon; Machine learning; Autonomous agent; Mathematical optimization; Engineering; Mathematics","score_opus":0.01964674410887052,"score_gpt":0.23641351867248658,"score_spread":0.21676677456361607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321021972","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":[0.23837546,0.0001299619,0.755203,0.000023362076,0.0033595494,0.0009010259,0.0003603106,0.0016168032,0.000030533614],"genre_scores_gemma":[0.99863344,0.00026917094,0.00018536698,0.000007580383,0.0001655326,0.00035221333,0.00009045641,0.000084004634,0.00021225231],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981829,0.000049867318,0.00075823197,0.00037963942,0.00023633208,0.0003930563],"domain_scores_gemma":[0.99934053,0.00014401559,0.00010234591,0.00021119758,0.00010322613,0.00009870174],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003026014,0.0003051883,0.0003778688,0.00042471208,0.00041246723,0.00007062312,0.00013249306,0.00047869852,0.000014407116],"category_scores_gemma":[0.000002707917,0.00034790512,0.00018491273,0.0003388301,0.000065211025,0.00021361599,3.7875694e-7,0.00032994073,0.000018550278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035089106,0.000030919822,0.00012488708,0.00042248864,0.00020643022,0.000010177911,0.0011487381,0.99125373,0.00241217,0.00017580845,0.000053384592,0.004126171],"study_design_scores_gemma":[0.000518278,0.00016531606,0.0011044827,0.0002367043,0.00020600687,0.000021142552,0.0016265309,0.9878135,0.00744987,0.00004104897,0.0004153819,0.00040176365],"about_ca_topic_score_codex":0.00020551105,"about_ca_topic_score_gemma":0.0005074216,"teacher_disagreement_score":0.76025796,"about_ca_system_score_codex":0.0001990886,"about_ca_system_score_gemma":0.0001174866,"threshold_uncertainty_score":0.9998973},"labels":[],"label_agreement":null},{"id":"W4321197451","doi":"10.1155/2023/6465225","title":"Conflict Judgment and Safety Assessment at Unsignalized Intersections Based on Machine Vision","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Major Scientific and Technological Innovation Project of Shandong Province; Natural Science Foundation of Shandong Province; China Postdoctoral Science Foundation; Key Technology Research and Development Program of Shandong; National Natural Science Foundation of China","keywords":"Obstacle; Intersection (aeronautics); Computer science; Monocular vision; Computer vision; Trajectory; Monocular; Artificial intelligence; Machine vision; Collision; Ranging; Simulation; Transport engineering; Computer security; Engineering","score_opus":0.007335019694325705,"score_gpt":0.25773632696687365,"score_spread":0.25040130727254795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321197451","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":[0.9463747,0.00011626446,0.05181472,0.00059397146,0.00039057477,0.00015160788,0.000028541688,0.0002335966,0.00029600103],"genre_scores_gemma":[0.9974954,0.00043082668,0.001911406,0.000047324636,0.000015673031,0.000004634858,0.000046192625,0.000016235728,0.000032312968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99927956,0.000015314647,0.0003675302,0.00008499008,0.00014331055,0.00010932266],"domain_scores_gemma":[0.9996622,0.00006901118,0.00010343317,0.0000760426,0.000040233746,0.00004905149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018314733,0.00010185105,0.00017107511,0.0002103398,0.00008251725,0.0000049073046,0.000047478363,0.00006745516,0.000045905173],"category_scores_gemma":[0.000004224424,0.00009240811,0.000063783045,0.00017951865,0.000026402238,0.00012549365,0.0000016889181,0.00022847352,0.000004100405],"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":[0.0003292367,0.00003066152,0.0014136538,0.000036588313,0.000046181718,0.000037465128,0.00032764723,0.9627643,0.021494092,0.00029035963,0.00008772873,0.013142069],"study_design_scores_gemma":[0.0053389356,0.0009787071,0.8060478,0.00026077594,0.0001036455,0.000019075047,0.00036670902,0.16353117,0.009627407,0.0004642701,0.012976697,0.00028478992],"about_ca_topic_score_codex":0.000001225735,"about_ca_topic_score_gemma":0.000034834895,"teacher_disagreement_score":0.80463415,"about_ca_system_score_codex":0.00013396527,"about_ca_system_score_gemma":0.000014403428,"threshold_uncertainty_score":0.37682933},"labels":[],"label_agreement":null},{"id":"W4321325528","doi":"10.2316/j.2023.201-0286","title":"FEASIBLE AND OPTIMAL TRAJECTORIES GENERATION FOR AUTONOMOUS DRIVING VEHICLES, 11-24.","year":2023,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Automotive engineering; Computer science; Aeronautics; Engineering","score_opus":0.011707444901221647,"score_gpt":0.20429614174912972,"score_spread":0.19258869684790808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321325528","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":[0.91082007,0.0044073337,0.08193647,0.00021379371,0.00070063816,0.0006854859,0.00002976064,0.0011593397,0.0000471155],"genre_scores_gemma":[0.99879116,0.0001403619,0.00016744263,0.000010430834,0.00023596527,0.0002936666,0.000013372236,0.000033195756,0.00031440853],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912065,0.000020619616,0.00022901931,0.0002244908,0.00006324059,0.00034198366],"domain_scores_gemma":[0.9996688,0.000070991424,0.000035670033,0.00014745124,0.000021845832,0.000055244116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003243887,0.00015931895,0.0002759464,0.00009170046,0.00020316702,0.00006563713,0.000068619345,0.00017925746,0.0000021356025],"category_scores_gemma":[0.0000137451225,0.0001558487,0.000039914754,0.00009071879,0.000032324853,0.000113155715,0.000017293038,0.000100291436,0.0000048420065],"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":[0.00012440514,0.00007691336,0.02436438,0.0016538291,0.0013125075,0.000049625225,0.0030715556,0.2490592,0.21333371,0.36209208,0.0072769215,0.13758488],"study_design_scores_gemma":[0.0013807195,0.00008791236,0.0015477809,0.000022778939,0.000046778376,0.000024134819,0.00038789035,0.9878534,0.00095331273,0.00019819838,0.0072805937,0.00021647009],"about_ca_topic_score_codex":0.00003387232,"about_ca_topic_score_gemma":0.000065442575,"teacher_disagreement_score":0.7387942,"about_ca_system_score_codex":0.000061547646,"about_ca_system_score_gemma":0.000027025355,"threshold_uncertainty_score":0.63553256},"labels":[],"label_agreement":null},{"id":"W4323654390","doi":"10.18280/isi.280124","title":"Discretion-Preserving with Data Mining Drive Distribution Scheme with a Universal Social Grid Web for Vans Using Vast Data","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Discretion; Scheme (mathematics); Grid; Computer science; World Wide Web; Distribution (mathematics); Business; Internet privacy; Computer security; Political science; Law; Mathematics","score_opus":0.035438101600475974,"score_gpt":0.24877364354428938,"score_spread":0.2133355419438134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323654390","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":[0.39622965,0.000020245925,0.5985194,0.00006199317,0.00012684218,0.0003813037,0.0031702875,0.0011183681,0.0003718817],"genre_scores_gemma":[0.97039,0.00001558107,0.011393164,0.000009413539,0.00009413863,0.00002474777,0.018035352,0.00002864768,0.000008962567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989248,0.00001677916,0.00031778446,0.00020006749,0.00017532466,0.00036522537],"domain_scores_gemma":[0.9990106,0.00006172724,0.00014315773,0.00063147536,0.00011088733,0.000042107924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035494374,0.00019010447,0.00019931748,0.00015239597,0.00054369005,0.00011104036,0.00066422275,0.00015656294,0.000009352298],"category_scores_gemma":[0.000067840614,0.00017698626,0.000019727755,0.000609161,0.00015763185,0.004464317,0.00029682627,0.00016556469,0.000011271239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027059931,0.00019026833,0.037721533,0.009325568,0.0044376804,0.00014031892,0.065048225,0.46910396,0.0036316256,0.020371938,0.042230718,0.34509215],"study_design_scores_gemma":[0.0006571707,0.000048871934,0.0016033419,0.00016675935,0.000053551106,0.000026409252,0.0052403146,0.98764753,0.00012071416,0.00008224078,0.004081604,0.00027146217],"about_ca_topic_score_codex":0.000030647217,"about_ca_topic_score_gemma":0.000107177,"teacher_disagreement_score":0.58712626,"about_ca_system_score_codex":0.0002563935,"about_ca_system_score_gemma":0.00012334174,"threshold_uncertainty_score":0.72172904},"labels":[],"label_agreement":null},{"id":"W4324118701","doi":"10.1109/iotsms58070.2022.10061930","title":"Aggressive Driver Behavior Detection using Parallel Convolutional Neural Networks on Simulated and Real Driving Data","year":2022,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Science and Engineering Research Council","keywords":"Computer science; Convolutional neural network; Hidden Markov model; Recurrent neural network; Artificial intelligence; Task (project management); Time series; Deep learning; Artificial neural network; Data modeling; Machine learning; Pattern recognition (psychology); Real-time computing; Engineering","score_opus":0.0218632735708663,"score_gpt":0.24364291313461883,"score_spread":0.22177963956375252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324118701","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":[0.984463,0.00009643837,0.014360695,0.000017267725,0.00024021097,0.00014489656,0.000016977665,0.0005462321,0.00011428991],"genre_scores_gemma":[0.99948645,0.000026640862,0.00030090637,0.000027010728,0.000038059374,0.000009334945,0.00006472118,0.000020509457,0.000026397756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992888,0.0000325174,0.00014706502,0.00023214327,0.00009403675,0.0002054336],"domain_scores_gemma":[0.99958223,0.00005115764,0.000046903548,0.000272149,0.000010994989,0.000036567184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009033951,0.000116791605,0.00011235179,0.00006920666,0.00036796601,0.000011459604,0.00017475117,0.00009712293,0.000117982934],"category_scores_gemma":[0.000007783686,0.00012724749,0.00001817444,0.00011354105,0.0000591354,0.00012892949,0.0002896311,0.00040670746,0.0000015885473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017342196,0.000017917382,0.01218917,0.000001688696,0.000024549587,0.00003699469,0.00002256542,0.97789323,0.00076994067,0.00016668162,0.000030718253,0.008829187],"study_design_scores_gemma":[0.00029146043,0.00004203846,0.04682687,0.0000030676022,0.000027279786,0.000063822576,0.000044763954,0.95241547,0.000050131042,0.00002682753,0.00006950216,0.0001387951],"about_ca_topic_score_codex":0.00006452736,"about_ca_topic_score_gemma":0.00004756997,"teacher_disagreement_score":0.0346377,"about_ca_system_score_codex":0.000108217304,"about_ca_system_score_gemma":0.000008739935,"threshold_uncertainty_score":0.5189002},"labels":[],"label_agreement":null},{"id":"W4361288549","doi":"10.1016/j.aap.2023.107044","title":"Wide-area vehicle trajectory data based on advanced tracking and trajectory splicing technologies: Potentials in transportation research","year":2023,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National University's Basic Research Foundation of China; China Scholarship Council; National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Vehicle tracking system; Vehicle Information and Communication System; Transport engineering; Engineering; Artificial intelligence; Road traffic","score_opus":0.05550160545796415,"score_gpt":0.3276611869379589,"score_spread":0.27215958147999475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361288549","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":[0.98412645,0.00034833082,0.013573997,0.000213532,0.00004857103,0.00034752907,0.000003809872,0.0012706752,0.0000671267],"genre_scores_gemma":[0.99856645,0.00047520245,0.00040296954,0.000008685825,0.000007993613,0.000068653295,0.0004031752,0.00003047666,0.00003639338],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9980278,0.0001243465,0.000519399,0.0005665551,0.0003413348,0.00042058696],"domain_scores_gemma":[0.9988047,0.00028108325,0.0000780194,0.0007572195,0.000044886925,0.000034078148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016783122,0.00018880429,0.00035765197,0.0019869958,0.00016052954,0.00003952028,0.0004500522,0.0002684624,0.000042263317],"category_scores_gemma":[0.00012813971,0.00020952431,0.000117357864,0.0031755203,0.00007168398,0.00048028896,0.000043951928,0.00052503595,0.000018257111],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006631943,0.00009775243,0.39475843,0.000034173016,0.0003791055,0.0000507194,0.00032939666,0.45651415,0.01693408,0.00007796477,0.00012682333,0.13063107],"study_design_scores_gemma":[0.00045961025,0.000048227434,0.6529785,0.0000952255,0.00020708989,1.9636711e-7,0.00084459904,0.34046355,0.0040584146,0.00061049184,0.000046150308,0.00018795054],"about_ca_topic_score_codex":0.00008031176,"about_ca_topic_score_gemma":0.008309773,"teacher_disagreement_score":0.25822005,"about_ca_system_score_codex":0.00014059868,"about_ca_system_score_gemma":0.000026161184,"threshold_uncertainty_score":0.85441536},"labels":[],"label_agreement":null},{"id":"W4362014397","doi":"10.54364/aaiml.2023.1145","title":"Autonomous Vehicles: Open-Source Technologies, Considerations, and Development","year":2023,"lang":"en","type":"article","venue":"Advances in Artificial Intelligence and Machine Learning","topic":"Autonomous Vehicle Technology and Safety","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":"McMaster University","funders":"","keywords":"Computer science; Automation; Field (mathematics); Software; Emerging technologies; Systems engineering; Engineering; Artificial intelligence","score_opus":0.03452762198896793,"score_gpt":0.28231224327797666,"score_spread":0.24778462128900874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362014397","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":[0.7837016,0.03853603,0.1608252,0.0024021731,0.0004497279,0.0010722684,0.000007686662,0.009292975,0.0037123424],"genre_scores_gemma":[0.9902338,0.005859616,0.003718338,0.000033165732,0.000008583971,0.000045419012,0.000008151261,0.000019774992,0.000073162926],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990738,0.000024249342,0.00032054132,0.00025468587,0.000055491895,0.0002712354],"domain_scores_gemma":[0.9996401,0.00017269391,0.000036038688,0.00011050778,0.000012372821,0.000028295812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003263144,0.00014595047,0.0001879292,0.00020460348,0.00028541626,0.000065494976,0.00014503335,0.00012131162,0.000014975004],"category_scores_gemma":[0.00014705904,0.00015040589,0.000008836364,0.00029053364,0.0001694939,0.00031063744,0.00023917705,0.0004607348,0.000047384998],"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":[0.0000057824145,0.000009001155,0.004175793,0.00002416946,0.000007109305,0.000017465532,0.0007602767,0.06704193,0.0002597757,0.014934246,0.0000059651998,0.91275847],"study_design_scores_gemma":[0.00016269117,0.00013334445,0.0016520028,0.0001563843,0.000009530516,0.00006197335,0.008085967,0.65860015,0.04284627,0.101874575,0.18558651,0.0008305998],"about_ca_topic_score_codex":0.000020681817,"about_ca_topic_score_gemma":0.00032533045,"teacher_disagreement_score":0.9119279,"about_ca_system_score_codex":0.000034259647,"about_ca_system_score_gemma":0.000023019602,"threshold_uncertainty_score":0.61333746},"labels":[],"label_agreement":null},{"id":"W4362577074","doi":"10.1155/2023/4127486","title":"Learning to Drive in the NGSIM Simulator Using Proximal Policy Optimization","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Education of the People's Republic of China","keywords":"Simulation; Artificial neural network; Task (project management); Reinforcement learning; Driving simulator; Computer science; Artificial intelligence; Field (mathematics); Host (biology); ENCODE; Engineering; Machine learning; Chemistry","score_opus":0.007694385268668237,"score_gpt":0.2547893032878882,"score_spread":0.24709491801921996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362577074","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":[0.93276095,0.000019361269,0.06665042,0.000278271,0.000072226656,0.00011138629,0.0000011366445,0.00008530828,0.000020905785],"genre_scores_gemma":[0.9921463,0.0000480643,0.007691893,0.00003054473,0.000055041146,0.0000027605602,0.000006028744,0.000014247041,0.0000051309803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993927,0.000016249347,0.00029300974,0.000053956737,0.00011351977,0.00013058844],"domain_scores_gemma":[0.9997654,0.000038842198,0.000076870514,0.000048554066,0.000047186837,0.000023171757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019173007,0.00006801698,0.000109896355,0.00030576772,0.00004787038,0.000006592146,0.00008875152,0.000058367325,0.0000027829701],"category_scores_gemma":[0.000035134963,0.0000575307,0.00003709153,0.00072773645,0.000011028546,0.00023133532,0.0000010371501,0.00025419792,0.0000020009868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002467853,0.0000064198507,0.0013768915,0.00001082828,0.000007770529,0.00002122247,0.004302426,0.9867944,0.0032223756,0.00013550182,0.0000016869334,0.004095778],"study_design_scores_gemma":[0.00091320707,0.00020402783,0.16441797,0.00009364942,0.000025862822,0.000014451212,0.00425473,0.82701087,0.0019342657,0.00048016896,0.00048104126,0.00016973849],"about_ca_topic_score_codex":0.0000018441535,"about_ca_topic_score_gemma":0.000009820223,"teacher_disagreement_score":0.16304109,"about_ca_system_score_codex":0.00006684707,"about_ca_system_score_gemma":0.000027648277,"threshold_uncertainty_score":0.23460338},"labels":[],"label_agreement":null},{"id":"W4365460338","doi":"10.1155/2023/1513008","title":"An Integrated Lateral and Longitudinal Decision-Making Model forAutonomous Driving Based on Deep Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Heilongjiang Province","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Action (physics); Kinematics; Control (management); Simulation; Engineering","score_opus":0.006982275472435828,"score_gpt":0.24538864492045595,"score_spread":0.23840636944802013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365460338","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":[0.57991886,0.000016633929,0.41976458,0.000016427164,0.000070529844,0.00004369487,6.5173816e-7,0.00015026335,0.000018382036],"genre_scores_gemma":[0.9827137,0.000063168416,0.017138088,0.000015000611,0.000017897659,0.0000035285652,0.000019621386,0.000024921068,0.0000040842965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911803,0.00000983281,0.00042644184,0.000112639296,0.0001542208,0.00017885126],"domain_scores_gemma":[0.99958074,0.00007807958,0.00013345359,0.00008469925,0.00006880974,0.000054187156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020574375,0.00013258419,0.00018964475,0.0003031636,0.000101010555,0.000017017546,0.00008692288,0.00009323917,0.000008893352],"category_scores_gemma":[0.000020173758,0.00012512859,0.000053777472,0.00021820037,0.000021447177,0.00041903523,0.0000015017553,0.00037950167,0.0000018603017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011519785,0.000009446329,0.0049832417,0.000014695487,0.000016384178,0.00004545625,0.00047461368,0.92918307,0.0010777534,0.00010722906,0.0000015788926,0.06397135],"study_design_scores_gemma":[0.0005311497,0.00023966354,0.092950135,0.00016912844,0.000020829451,0.0000052548007,0.00017638411,0.9046243,0.0003554815,0.00078119943,0.000030463272,0.000116000316],"about_ca_topic_score_codex":5.1114796e-7,"about_ca_topic_score_gemma":0.000024408804,"teacher_disagreement_score":0.40279484,"about_ca_system_score_codex":0.000079538455,"about_ca_system_score_gemma":0.000025103263,"threshold_uncertainty_score":0.51025957},"labels":[],"label_agreement":null},{"id":"W4366503402","doi":"10.1049/cth2.12441","title":"HOPAV: Hybrid optimization‐oriented path planning for non‐connected and connected automated vehicles","year":2023,"lang":"en","type":"article","venue":"IET Control Theory and Applications","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":"Horizon College and Seminary","funders":"Silesian University of Technology","keywords":"Path (computing); Motion planning; Scheme (mathematics); Function (biology); Computer science; Transport engineering; Penetration rate; Engineering; Automotive engineering; Simulation; Operations research; Real-time computing; Artificial intelligence; Computer network","score_opus":0.004477260036320305,"score_gpt":0.21997909557869613,"score_spread":0.21550183554237584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366503402","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":[0.2723817,0.000404077,0.7186191,0.0003057453,0.000057769463,0.0012955773,0.00034953258,0.0061808703,0.0004056412],"genre_scores_gemma":[0.99758625,0.0000698011,0.00092659105,0.00009323546,0.00004720786,0.0009973572,0.00019312096,0.000034449808,0.000051979274],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992068,0.000038315266,0.00022361545,0.0002389822,0.000041694282,0.00025061227],"domain_scores_gemma":[0.9989834,0.00064678164,0.00004736482,0.00019674432,0.00005748344,0.00006825072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031816078,0.00015871185,0.0002147431,0.00011677249,0.00034048903,0.00002577318,0.00009542186,0.000118142685,0.000008262391],"category_scores_gemma":[0.000068699475,0.00016266234,0.00003132965,0.00028329916,0.000120891884,0.00008073457,0.000022865743,0.00012541167,0.00000981026],"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":[0.0003730979,0.000087962966,0.000823432,0.00020650282,0.0005479964,0.000011894224,0.0007533325,0.43633616,0.024508737,0.51095605,0.0028196953,0.022575157],"study_design_scores_gemma":[0.0016399554,0.00003667044,0.002037053,0.000020551663,0.00006258433,0.000011500375,0.00022334383,0.9805536,0.00096325437,0.009845066,0.0043882458,0.00021812528],"about_ca_topic_score_codex":9.1270965e-7,"about_ca_topic_score_gemma":4.7997065e-7,"teacher_disagreement_score":0.7252045,"about_ca_system_score_codex":0.0000133885615,"about_ca_system_score_gemma":0.000013052066,"threshold_uncertainty_score":0.6633178},"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":"W4366692929","doi":"10.1007/s10846-023-01808-3","title":"Verifying Collision Risk Estimation using Autonomous Driving Scenarios Derived from a Formal Model","year":2023,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Horizon 2020 Framework Programme","keywords":"Computer science; Formal verification; Probabilistic logic; Formal methods; Verification and validation; Collision; Representation (politics); Model checking; Runtime verification; Range (aeronautics); Formal specification; Data mining; Reliability engineering; Artificial intelligence; Software engineering; Algorithm; Programming language; Engineering","score_opus":0.02529287078887632,"score_gpt":0.24920578058055728,"score_spread":0.22391290979168096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366692929","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":[0.4915233,0.0003322649,0.5068921,0.000013562181,0.00087015674,0.00012390023,0.0000037467864,0.00021440735,0.000026578955],"genre_scores_gemma":[0.9857071,0.00035772653,0.0136996675,0.0000050039503,0.00014998957,0.0000047136928,0.0000065455547,0.000049765185,0.000019469577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980137,0.00006363506,0.0010655117,0.00015286801,0.00030345158,0.00040084665],"domain_scores_gemma":[0.9988649,0.00017684193,0.00047808242,0.00024233198,0.00012499269,0.00011287787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077494857,0.0002302497,0.00050268474,0.0004034446,0.0002221132,0.00007923672,0.0002984742,0.00026083365,0.0000066132634],"category_scores_gemma":[0.00012842542,0.00021509266,0.00017001906,0.0003466967,0.00003633815,0.00046488334,0.00007145627,0.00053275895,0.000057026267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017088067,0.000018320534,0.0010464918,0.00004959117,0.00015276844,0.00003956374,0.00079630833,0.9882945,0.0044242353,0.00006365577,0.000052186122,0.0050452836],"study_design_scores_gemma":[0.00028096928,0.00005714029,0.0007923252,0.0004970684,0.00010658046,0.00011509818,0.0006940037,0.9937324,0.0032521125,0.00024330514,0.000026161111,0.00020282388],"about_ca_topic_score_codex":0.000073877854,"about_ca_topic_score_gemma":0.000008761986,"teacher_disagreement_score":0.4941838,"about_ca_system_score_codex":0.0005117685,"about_ca_system_score_gemma":0.000098962824,"threshold_uncertainty_score":0.8771224},"labels":[],"label_agreement":null},{"id":"W4366957387","doi":"10.1109/taes.2023.3267763","title":"L-Shape model based vehicle tracking with joint kinematic and geometric estimation using Lidar","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","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":"General Dynamics (Canada); McMaster University","funders":"","keywords":"Kinematics; Computer vision; Artificial intelligence; Tracking (education); Outlier; Computer science; Lidar; Position (finance); Point (geometry); Mathematics; Geometry; Geography","score_opus":0.01658933873713513,"score_gpt":0.21574553594497403,"score_spread":0.1991561972078389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366957387","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":[0.5097979,0.00024145372,0.48925638,0.000053078085,0.000040423067,0.00017576573,0.0000035504167,0.0004162979,0.000015185303],"genre_scores_gemma":[0.9991343,0.00014982003,0.0005474188,0.0000127406165,0.000009414743,0.00005265692,0.0000017315665,0.000043775224,0.000048122547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990163,0.000020403688,0.00020198709,0.00021998685,0.00013259873,0.00040874042],"domain_scores_gemma":[0.9996455,0.00005736407,0.00004125774,0.00017330163,0.000022786906,0.000059835278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021116677,0.0001901166,0.00024781135,0.0003834565,0.00023562823,0.000052900705,0.00005090001,0.00014986232,0.0000036841693],"category_scores_gemma":[0.0000025415134,0.00017768613,0.00003160172,0.0007532918,0.000047618894,0.00012965837,7.9721787e-7,0.00032602326,0.00001057528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012611004,0.000014467467,0.000030087615,0.00017563527,0.00004661218,0.00000249221,0.00009355114,0.9852052,0.008026182,0.00006272505,0.000004743028,0.006325737],"study_design_scores_gemma":[0.0005141002,0.000127753,0.00022239466,0.00012684177,0.000059196704,0.00003537292,0.00011305036,0.99207664,0.0064884773,0.000033217326,0.000005933335,0.00019703727],"about_ca_topic_score_codex":0.000035373574,"about_ca_topic_score_gemma":0.000030802767,"teacher_disagreement_score":0.48933643,"about_ca_system_score_codex":0.00014685995,"about_ca_system_score_gemma":0.00004961908,"threshold_uncertainty_score":0.724583},"labels":[],"label_agreement":null},{"id":"W4367162725","doi":"10.1109/access.2023.3270447","title":"Assessing Distribution Shift in Probabilistic Object Detection Under Adverse Weather","year":2023,"lang":"en","type":"article","venue":"IEEE Access","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":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Lidar; Artificial intelligence; Ground truth; Object detection; Probabilistic logic; Computer vision; Classifier (UML); Robustness (evolution); Data mining; Machine learning; Pattern recognition (psychology); Remote sensing","score_opus":0.024086514547692468,"score_gpt":0.28522235358276027,"score_spread":0.2611358390350678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367162725","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":[0.9466607,0.000017293747,0.050868936,0.0000623349,0.00040765674,0.00014624636,0.0000057214393,0.00132735,0.00050376554],"genre_scores_gemma":[0.9998257,0.000011387235,0.000010514172,0.000011615726,0.000039455455,0.000046067515,0.000017096365,0.000020792855,0.00001738307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9993388,0.000022691982,0.00016701718,0.00015934474,0.00007082967,0.00024131105],"domain_scores_gemma":[0.99972975,0.00005045672,0.000022413744,0.00016430464,0.000010685305,0.00002238896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017884056,0.00010476037,0.00011177226,0.00011436628,0.00006906258,0.000040057064,0.00016102077,0.00016745093,0.000018394832],"category_scores_gemma":[0.000023920653,0.00010939306,0.000030538344,0.00062958605,0.000037639562,0.0004911047,0.000028742585,0.00023578554,0.000101859354],"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":[0.000029744453,0.000080274964,0.025072062,0.00022738826,0.00006866655,0.000088134795,0.000397165,0.8855293,0.011274647,0.0016102714,0.00027227748,0.07535011],"study_design_scores_gemma":[0.00048107072,0.000017877044,0.7864254,0.000060205897,0.000019004317,0.0000063155776,0.00015163691,0.17796339,0.017280716,0.01690964,0.00034357895,0.0003411275],"about_ca_topic_score_codex":0.000040103423,"about_ca_topic_score_gemma":0.00033201143,"teacher_disagreement_score":0.7613534,"about_ca_system_score_codex":0.00019601999,"about_ca_system_score_gemma":0.000016881222,"threshold_uncertainty_score":0.44609198},"labels":[],"label_agreement":null},{"id":"W4375798883","doi":"10.1109/tits.2023.3271295","title":"The Interacting Multiple Model Smooth Variable Structure Filter for Trajectory Prediction","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Trajectory; Variable (mathematics); Control theory (sociology); Computer science; Filter (signal processing); Mathematics; Physics; Artificial intelligence; Computer vision; Mathematical analysis","score_opus":0.020615418064285778,"score_gpt":0.227815673186436,"score_spread":0.20720025512215023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375798883","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":[0.06189603,0.000056325316,0.9317813,0.000035530316,0.0028601435,0.0007546378,0.0010127695,0.0015310072,0.00007223652],"genre_scores_gemma":[0.9980577,0.00011994723,0.00047617237,0.000011592362,0.00005985318,0.0005033895,0.000114174334,0.00006341795,0.00059374387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866396,0.000024433248,0.00056016474,0.00025213772,0.000170782,0.00032849505],"domain_scores_gemma":[0.9991233,0.0004123331,0.00006453593,0.00026295683,0.00008193034,0.00005499109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002299396,0.00021726893,0.00019072933,0.00018717989,0.00047337456,0.00004709956,0.0001759969,0.00023765497,0.00001898644],"category_scores_gemma":[0.0000053790022,0.00018622598,0.00013093765,0.00034285735,0.00004333613,0.00017909192,1.7694113e-7,0.00036747797,0.000027660251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006190189,0.000016117172,0.000039313098,0.00009941644,0.00011185055,7.242467e-7,0.0008531593,0.9885561,0.0045289947,0.00034155243,0.00041606335,0.004974849],"study_design_scores_gemma":[0.00027207326,0.000046821908,0.00012932833,0.000065130356,0.000050994724,0.0000021209846,0.0009765449,0.9737473,0.019756122,0.00015878462,0.0046194224,0.00017531647],"about_ca_topic_score_codex":0.000023573153,"about_ca_topic_score_gemma":0.00016960605,"teacher_disagreement_score":0.9361617,"about_ca_system_score_codex":0.00011734009,"about_ca_system_score_gemma":0.00003062671,"threshold_uncertainty_score":0.75940746},"labels":[],"label_agreement":null},{"id":"W4375858314","doi":"10.1155/2023/1349269","title":"Leveraging the GIDAS Database for the Criticality Analysis of Automated Driving Systems","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Domain (mathematical analysis); Relation (database); Database; Criticality; Failure mode, effects, and criticality analysis; Risk analysis (engineering); Field (mathematics); Computer security","score_opus":0.014541725917131225,"score_gpt":0.27234674614331894,"score_spread":0.25780502022618773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375858314","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":[0.8203294,0.00031365323,0.17836551,0.00032260528,0.00026688632,0.00013280596,0.000043659104,0.00021864026,0.0000068214613],"genre_scores_gemma":[0.99906486,0.00015260532,0.0006965496,0.000008357201,0.000023213046,0.000009312883,0.00003105908,0.000009867044,0.0000041630497],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920064,0.000017373099,0.0004729795,0.000057214093,0.00013186813,0.000119946075],"domain_scores_gemma":[0.9990984,0.00047386822,0.0001399878,0.0001476612,0.000121007906,0.00001907821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056780246,0.00006796272,0.00020886306,0.00015990958,0.00009364664,0.000007474717,0.00015097248,0.000042137537,0.0000030515635],"category_scores_gemma":[0.000052420088,0.00004303393,0.00013793766,0.00069862057,0.000043221225,0.00015877196,0.0000016155986,0.00015386757,4.4680723e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001576794,0.000006527408,0.0016440433,0.000059142792,0.00050182507,0.000004216853,0.00068089634,0.9887701,0.005990325,0.0008886719,0.00004238593,0.0013960898],"study_design_scores_gemma":[0.00026438528,0.000019260193,0.35161045,0.000049724484,0.0008358854,0.0000021220394,0.001749203,0.64333504,0.0015559124,0.00016188636,0.00035677414,0.00005935917],"about_ca_topic_score_codex":0.0000049789064,"about_ca_topic_score_gemma":0.000027383148,"teacher_disagreement_score":0.3499664,"about_ca_system_score_codex":0.000024945006,"about_ca_system_score_gemma":0.000014961536,"threshold_uncertainty_score":0.17548728},"labels":[],"label_agreement":null},{"id":"W4376457019","doi":"10.1109/tits.2023.3270887","title":"Uncertainties in Onboard Algorithms for Autonomous Vehicles: Challenges, Mitigation, and Perspectives","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Risk analysis (engineering); Software deployment; Context (archaeology); Commercialization; Computer science; Systems engineering; Engineering; Transport engineering; Business","score_opus":0.04115902531664435,"score_gpt":0.2534863769107184,"score_spread":0.21232735159407407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376457019","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":[0.37286898,0.00249662,0.6201136,0.00041331202,0.0009988174,0.001054489,0.00027782115,0.0016929749,0.00008338808],"genre_scores_gemma":[0.9951972,0.0036024514,0.0002611465,0.0000070946107,0.000040121082,0.00064714183,0.000029485129,0.000046275738,0.00016909336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987647,0.000026110045,0.0004784174,0.00031943765,0.00013173466,0.0002795877],"domain_scores_gemma":[0.99950576,0.00015385797,0.00004115441,0.00016848139,0.000070961454,0.00005976214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023402242,0.00021388553,0.0002692013,0.00047269432,0.00012632238,0.000022643471,0.000099843004,0.00022413184,0.0000067517212],"category_scores_gemma":[0.0000022613972,0.00023629174,0.00008673389,0.00034990872,0.00009115667,0.00014640842,1.671337e-7,0.00022692929,0.000026290414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005522958,0.000088358844,0.00012252589,0.00050967745,0.00020835275,0.000007776434,0.02345236,0.9199485,0.00068679807,0.007382169,0.0000654054,0.0474728],"study_design_scores_gemma":[0.0025456739,0.0009322358,0.015831882,0.00066188036,0.00017158651,0.000019766074,0.1167396,0.7897555,0.060163356,0.002713027,0.008810581,0.0016548972],"about_ca_topic_score_codex":0.000042885236,"about_ca_topic_score_gemma":0.00034651978,"teacher_disagreement_score":0.6223282,"about_ca_system_score_codex":0.00014430674,"about_ca_system_score_gemma":0.00002574794,"threshold_uncertainty_score":0.96356976},"labels":[],"label_agreement":null},{"id":"W4378192079","doi":"10.5194/isprs-archives-xlviii-1-w1-2023-339-2023","title":"A FRAMEWORK FOR MULTI-SENSOR POSITIONING AND MAPPING FOR AUTONOMOUS VEHICLES","year":2023,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Autonomous Vehicle Technology and Safety","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":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Suite; Reliability (semiconductor); Truck; Wireless; Real-time computing; Key (lock); Embedded system; Computer security; Engineering; Telecommunications; Automotive engineering","score_opus":0.02796971003680031,"score_gpt":0.2726818156432587,"score_spread":0.2447121056064584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378192079","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":[0.020711377,0.00003846337,0.97235084,0.0033153843,0.0012742517,0.0008731787,0.0001737799,0.00016174346,0.0011009651],"genre_scores_gemma":[0.97272646,0.00017058362,0.026344279,0.0005342905,0.00010988208,0.0000015562912,0.000045228586,0.000015631587,0.0000520831],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972941,0.00010001429,0.001075348,0.00031986076,0.00070755323,0.00050310075],"domain_scores_gemma":[0.9971389,0.0014894445,0.00076101493,0.0003127567,0.00019246711,0.00010537549],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0011631949,0.00034118004,0.00037075658,0.0008707096,0.001483751,0.0004696558,0.0009644393,0.00013124353,0.0000018405212],"category_scores_gemma":[0.000699885,0.00023743686,0.00028730006,0.00074322894,0.002049968,0.00034152635,0.00049911655,0.00036482883,0.0000018062237],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084766594,0.000008428002,0.00015093702,0.00007914838,0.000100036865,1.5331638e-7,0.003296013,0.00813954,0.002497169,0.000051190164,0.000029916653,0.9855627],"study_design_scores_gemma":[0.0007196223,0.00009774383,0.0035416763,0.00032176916,0.000035569446,0.000068326764,0.0023610115,0.968038,0.006160547,0.015757194,0.0026276764,0.00027081714],"about_ca_topic_score_codex":0.20788693,"about_ca_topic_score_gemma":0.033563573,"teacher_disagreement_score":0.9852919,"about_ca_system_score_codex":0.000040389303,"about_ca_system_score_gemma":0.00010977538,"threshold_uncertainty_score":0.9998162},"labels":[],"label_agreement":null},{"id":"W4380989136","doi":"10.3390/su15129661","title":"Towards Sustainable Safe Driving: A Multimodal Fusion Method for Risk Level Recognition in Distracted Driving Status","year":2023,"lang":"en","type":"article","venue":"Sustainability","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; University of Windsor","keywords":"Modal; Computer science; Artificial intelligence; Sensor fusion; Modality (human–computer interaction); Activity recognition; Concatenation (mathematics); Modalities; Discriminative model; Machine learning; Pattern recognition (psychology); Computer vision; Mathematics","score_opus":0.0151364723790261,"score_gpt":0.28325697152121143,"score_spread":0.26812049914218533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380989136","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":[0.8626467,0.00002876861,0.13342988,0.00043538606,0.00012361634,0.0014016237,0.000093305,0.0015319808,0.0003087206],"genre_scores_gemma":[0.9871665,0.000051103838,0.01171561,0.000007102281,0.00003962945,0.00042593371,0.00017432538,0.00005685034,0.00036294435],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99730545,0.00018657482,0.00056249293,0.0005264587,0.00016007862,0.0012589433],"domain_scores_gemma":[0.9982569,0.00058024784,0.00008930548,0.00046931594,0.00047583424,0.0001283974],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0021296223,0.0002929245,0.00039623096,0.00039687756,0.00029778504,0.00003696553,0.00021867083,0.000435862,0.00004395442],"category_scores_gemma":[0.00462054,0.00032088504,0.00014506553,0.0010443156,0.00010164012,0.00032508702,0.00017476757,0.0006462148,0.0000131975075],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021367948,0.00025892985,0.23072027,0.0014685126,0.00008210342,0.00010726372,0.0032696486,0.03153468,0.0006380769,0.0020688516,0.00042109872,0.7292169],"study_design_scores_gemma":[0.0009962994,0.00008025821,0.6895735,0.000019999728,0.000027037215,0.0000022977738,0.004569393,0.21943988,0.0013363657,0.080829844,0.0027401443,0.00038497604],"about_ca_topic_score_codex":0.0012278025,"about_ca_topic_score_gemma":0.00073769665,"teacher_disagreement_score":0.7288319,"about_ca_system_score_codex":0.0024396842,"about_ca_system_score_gemma":0.00034156322,"threshold_uncertainty_score":0.9999243},"labels":[],"label_agreement":null},{"id":"W4381198920","doi":"10.1109/tits.2023.3283019","title":"Uncertainty-Aware Decision-Making for Autonomous Driving at Uncontrolled Intersections","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"CVAR; Intersection (aeronautics); Reinforcement learning; Computer science; Quantile; Parameterized complexity; Baseline (sea); Mathematical optimization; Expected shortfall; Engineering; Artificial intelligence; Transport engineering; Econometrics; Mathematics; Risk management","score_opus":0.014466900404278766,"score_gpt":0.2534064320874675,"score_spread":0.23893953168318874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381198920","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":[0.15918599,0.00005260185,0.8332552,0.000046114194,0.003369517,0.0009887789,0.00027616337,0.002709414,0.0001162527],"genre_scores_gemma":[0.99771756,0.00010931784,0.0002318163,0.000021002048,0.00006415207,0.0010807709,0.000065155495,0.00009341428,0.00061681715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979865,0.000030995994,0.00086712616,0.00041627546,0.00023056971,0.0004685372],"domain_scores_gemma":[0.99863464,0.0006959018,0.00009389231,0.00035513157,0.00012023572,0.000100182406],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029466947,0.00033395478,0.0004267918,0.0006404266,0.00052651233,0.00004265521,0.00022750573,0.00032092741,0.00012480193],"category_scores_gemma":[0.0000070438396,0.00035184168,0.0003477172,0.0006860972,0.00006048858,0.00015603061,5.198534e-7,0.000323926,0.00031903316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000119692944,0.000036797774,0.0000784478,0.00009338044,0.00021900279,0.0000092112305,0.0010229002,0.9769068,0.00047891831,0.00022956973,0.00039392788,0.02041136],"study_design_scores_gemma":[0.0009925459,0.0001765332,0.00041670463,0.00041845202,0.00015639301,0.000016408861,0.0025475244,0.98142534,0.005489745,0.00031087257,0.007460929,0.0005885271],"about_ca_topic_score_codex":0.000035008175,"about_ca_topic_score_gemma":0.0015161317,"teacher_disagreement_score":0.83853155,"about_ca_system_score_codex":0.00051487365,"about_ca_system_score_gemma":0.00003957922,"threshold_uncertainty_score":0.99989337},"labels":[],"label_agreement":null},{"id":"W4381733133","doi":"10.1109/tits.2023.3285440","title":"Self-Learned Autonomous Driving at Unsignalized Intersections: A Hierarchical Reinforced Learning Approach for Feasible Decision-Making","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":76,"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; Computer science; Artificial intelligence; Artificial neural network; Motion planning; Machine learning; Collision avoidance; Collision; Simulation; Computer security; Robot","score_opus":0.020805590674889452,"score_gpt":0.26102251308723307,"score_spread":0.2402169224123436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381733133","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":[0.08668124,0.00006584833,0.9056176,0.000022091279,0.0011164589,0.0011614687,0.000043927113,0.0048887488,0.0004026116],"genre_scores_gemma":[0.99256235,0.00022145188,0.00487091,0.000013210934,0.000052404746,0.0010316415,0.000092576105,0.000118332275,0.0010370904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976267,0.00007067486,0.00094689714,0.0005143192,0.00030453995,0.0005368599],"domain_scores_gemma":[0.9987373,0.0005906972,0.00011909357,0.0003219176,0.00010574174,0.00012527092],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004849454,0.00036946998,0.0004579897,0.00079273235,0.00066613563,0.00006678366,0.0002499816,0.00041488264,0.00007262857],"category_scores_gemma":[0.000013924043,0.0003999526,0.0003476149,0.0009292836,0.00006611195,0.00020911374,0.0000010456474,0.0006685826,0.00016265568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017488898,0.00004108526,0.00005042251,0.00025125398,0.00026015087,0.000007402201,0.0022800423,0.98573554,0.0009851495,0.0006581742,0.000077393954,0.009478519],"study_design_scores_gemma":[0.0006500099,0.00016709164,0.00008618457,0.00022128112,0.00010871356,0.00002256915,0.0015086792,0.9869733,0.006361732,0.000097410324,0.0033637306,0.00043930585],"about_ca_topic_score_codex":0.00001535974,"about_ca_topic_score_gemma":0.000056600926,"teacher_disagreement_score":0.90588117,"about_ca_system_score_codex":0.00045907413,"about_ca_system_score_gemma":0.000052398267,"threshold_uncertainty_score":0.9998452},"labels":[],"label_agreement":null},{"id":"W4382059190","doi":"10.3390/machines11070676","title":"Autonomous Vehicle Decision-Making and Control in Complex and Unconventional Scenarios—A Review","year":2023,"lang":"en","type":"article","venue":"Machines","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Computer science; Benchmarking; Risk analysis (engineering); Control (management); Autonomy; Data science; Transport engineering; Artificial intelligence; Engineering; Business","score_opus":0.009187003250769989,"score_gpt":0.25200052817433144,"score_spread":0.24281352492356145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382059190","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":[0.9780172,0.016361982,0.0017974025,0.0016530822,0.00011719347,0.00031083295,0.000029476558,0.0009253625,0.00078744534],"genre_scores_gemma":[0.9974481,0.001780953,0.00037681495,0.00031687156,0.000016576549,0.000017525896,0.0000079599495,0.000014947942,0.00002026858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993904,0.000018398765,0.00021005503,0.00015171454,0.00005873078,0.0001706645],"domain_scores_gemma":[0.9996701,0.00016136825,0.000019779469,0.00011065713,0.00000942488,0.000028698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025056006,0.00010938699,0.00021791465,0.000110671645,0.00006834546,0.000012175065,0.00007532911,0.00006899869,0.00008217551],"category_scores_gemma":[0.000053227544,0.00010320099,0.000023900431,0.00020633519,0.00005628138,0.00006097288,0.000053041185,0.00015323742,0.00003378269],"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":[0.000028473896,0.000026919359,0.1637732,0.0009240921,0.00006460702,0.00010579009,0.0001427728,0.005416529,0.0010832158,0.0028731404,0.0031784803,0.8223828],"study_design_scores_gemma":[0.00060086075,0.000015473166,0.45443726,0.00043698,0.000013876611,0.000032255004,0.00000897227,0.533129,0.000004467676,0.0060544442,0.005100102,0.00016631515],"about_ca_topic_score_codex":0.000009861261,"about_ca_topic_score_gemma":0.00006993061,"teacher_disagreement_score":0.82221645,"about_ca_system_score_codex":0.000021344125,"about_ca_system_score_gemma":0.00000738606,"threshold_uncertainty_score":0.42084143},"labels":[],"label_agreement":null},{"id":"W4382197356","doi":"10.1155/2023/2661136","title":"An Acceleration Denoising Method Based on an Adaptive Kalman Filter for Trajectory in Merging Zones","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Trajectory; Jerk; Kalman filter; Acceleration; Smoothing; Computer science; Noise reduction; Noise (video); Reduction (mathematics); Range (aeronautics); Filter (signal processing); Artificial intelligence; Control theory (sociology); Computer vision; Mathematics; Engineering","score_opus":0.018239688180237384,"score_gpt":0.28915229877349574,"score_spread":0.27091261059325833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382197356","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":[0.6346919,0.00001763533,0.36489835,0.00004353738,0.000121861514,0.0001049798,0.000007093741,0.000106172825,0.000008472549],"genre_scores_gemma":[0.92297786,0.000016334187,0.076840244,0.000025444228,0.000051059535,0.000013106761,0.000048050202,0.0000257973,0.000002114171],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992268,0.000032835003,0.00037411842,0.00011030025,0.0001070672,0.00014889937],"domain_scores_gemma":[0.9996132,0.00009359994,0.00010791699,0.00008521966,0.000061266786,0.000038764458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037386038,0.00010609302,0.00017830502,0.0003822107,0.00005539751,0.000007737624,0.00008320648,0.00009567006,0.000005612923],"category_scores_gemma":[0.000008927455,0.00010914702,0.00005891386,0.0002405338,0.000010390976,0.000666708,2.9061783e-7,0.00020555411,6.5964565e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025854073,0.000030821902,0.00029746964,0.000020155412,0.000010588108,0.0000137223815,0.0011199354,0.9246463,0.03336829,0.00022375188,0.0000024472129,0.04000798],"study_design_scores_gemma":[0.0016095428,0.0008782976,0.2371542,0.00010379049,0.00003531842,0.0000023979853,0.0014233771,0.71902895,0.037804727,0.0016790444,0.000075534495,0.00020480785],"about_ca_topic_score_codex":0.0000018471694,"about_ca_topic_score_gemma":0.00015807783,"teacher_disagreement_score":0.28828597,"about_ca_system_score_codex":0.000069996764,"about_ca_system_score_gemma":0.00002414611,"threshold_uncertainty_score":0.44508862},"labels":[],"label_agreement":null},{"id":"W4382983421","doi":"10.1177/03611981231178814","title":"Automatically Refining Degraded and Occluded Lane Marking Information to Enhance the quality of High Definition Maps for Autonomous Vehicles","year":2023,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Autonomous Vehicle Technology and Safety","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 British Columbia","funders":"","keywords":"Computer science; Process (computing); Obstacle; Artificial intelligence; Computer vision; Segmentation; Mobile mapping; Kalman filter; Representation (politics); Point cloud; Geography","score_opus":0.07980500846529227,"score_gpt":0.37006232962910335,"score_spread":0.2902573211638111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382983421","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":[0.986507,0.00006957559,0.007828977,0.0042901263,0.0001914449,0.0007379687,0.00015540692,0.00013815514,0.00008134778],"genre_scores_gemma":[0.99316627,0.00039675614,0.0060909935,0.000037861624,0.00004113643,0.00013360418,0.000051425028,0.00002932215,0.000052634903],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9960851,0.00043425796,0.0014477872,0.00018138325,0.001279981,0.0005715037],"domain_scores_gemma":[0.99608225,0.001965853,0.00029570144,0.00032496374,0.0011855791,0.00014567138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064426386,0.00016918057,0.00038286432,0.00077111064,0.00047954338,0.00006607594,0.00060683704,0.00019792034,0.000017804829],"category_scores_gemma":[0.00040541298,0.00012348084,0.0001618345,0.0015865775,0.00035396309,0.00048076597,0.000009794988,0.0010985216,0.000013976335],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0073291278,0.00034276056,0.18071882,0.0061687827,0.001238298,0.000060463095,0.030198514,0.053634223,0.07557602,0.17741458,0.012658935,0.45465946],"study_design_scores_gemma":[0.0008740994,0.00036330972,0.9672128,0.0002834964,0.00003335446,5.9182946e-7,0.0019961046,0.001882665,0.006933645,0.017341055,0.0029082443,0.00017064296],"about_ca_topic_score_codex":0.0012128794,"about_ca_topic_score_gemma":0.005141181,"teacher_disagreement_score":0.78649396,"about_ca_system_score_codex":0.00012676456,"about_ca_system_score_gemma":0.00018675248,"threshold_uncertainty_score":0.5035403},"labels":[],"label_agreement":null},{"id":"W4383066546","doi":"10.1109/icra48891.2023.10161318","title":"PedFormer: Pedestrian Behavior Prediction via Cross-Modal Attention Modulation and Gated Multitask Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Huawei Technologies (Canada)","funders":"","keywords":"Pedestrian; Computer science; Transformer; Artificial intelligence; Modal; Machine learning; Human–computer interaction; Engineering","score_opus":0.008828543657084605,"score_gpt":0.23465171567988918,"score_spread":0.22582317202280458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383066546","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":[0.9825413,0.0000144973255,0.013169649,0.000027731572,0.00015180308,0.00016841531,0.0000056731287,0.003415701,0.00050521916],"genre_scores_gemma":[0.9988758,0.000045733716,0.00025280323,0.0000021264782,0.000032672975,0.000023793065,0.0001382276,0.000022397178,0.0006064725],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993934,0.000011341788,0.00018117164,0.00015289354,0.00007329426,0.00018787719],"domain_scores_gemma":[0.9998141,0.00001491978,0.000022640877,0.00008929649,0.00002309034,0.000035977213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013580699,0.000100669924,0.00008784038,0.00013375613,0.00017977737,0.00002559841,0.000039807994,0.00019849179,0.000049977578],"category_scores_gemma":[0.000012992308,0.00010671248,0.000027792681,0.00020868139,0.000042496453,0.00024289133,0.000024149911,0.0002135382,0.00010634441],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000131681545,0.00001877021,0.77030075,0.00003912166,0.000029891808,0.000007424399,0.00013366614,0.08029895,0.046779685,0.00015068821,0.00004021637,0.102187656],"study_design_scores_gemma":[0.00022302307,0.000022094704,0.51752687,0.0000028988518,0.000009690818,0.000006185614,0.000038298163,0.4813973,0.0005459296,0.00004965181,0.000115376955,0.00006266985],"about_ca_topic_score_codex":0.000034959063,"about_ca_topic_score_gemma":0.0000209192,"teacher_disagreement_score":0.40109834,"about_ca_system_score_codex":0.000041774667,"about_ca_system_score_gemma":0.0000043377345,"threshold_uncertainty_score":0.4351609},"labels":[],"label_agreement":null},{"id":"W4383109074","doi":"10.1109/icra48891.2023.10160984","title":"GoRela: Go Relative for Viewpoint-Invariant Motion Forecasting","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; ENCODE; Leverage (statistics); Inference; Invariant (physics); Artificial intelligence; Graph; Reference frame; Computation; Context (archaeology); Machine learning; Theoretical computer science; Frame (networking); Algorithm; Mathematics","score_opus":0.0291254997773316,"score_gpt":0.22403751703078104,"score_spread":0.19491201725344942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383109074","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":[0.074960455,0.000107654676,0.88517874,0.0015199741,0.00042947495,0.0005583186,0.000020999494,0.006295432,0.030928936],"genre_scores_gemma":[0.98697937,0.000027225064,0.011253783,0.000049439004,0.00004200101,0.000086537395,0.000030639043,0.000034822682,0.0014962128],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940073,0.0000068963614,0.00017401877,0.00013151587,0.00004052551,0.0002463215],"domain_scores_gemma":[0.99968624,0.00012227187,0.000019478228,0.0001249355,0.000019857007,0.00002719718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023456357,0.00009688618,0.00011969014,0.00006535385,0.0001258401,0.00000870294,0.00007934733,0.00015944615,0.000044353525],"category_scores_gemma":[0.00007551367,0.000090980335,0.000056302848,0.00024730945,0.00003165737,0.00011976096,0.0000278652,0.00015133744,0.0003477655],"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":[0.000029800745,0.000032127267,0.0011884018,0.0002590095,0.000311245,0.00003559775,0.0015008057,0.095887356,0.0023241977,0.55892926,0.022507837,0.31699434],"study_design_scores_gemma":[0.0002593588,0.00003172052,0.0014828697,0.000015174392,0.0000137748875,0.0000067822384,0.000071956965,0.9622952,0.0017894771,0.027803969,0.0060919453,0.00013781247],"about_ca_topic_score_codex":0.0000023497346,"about_ca_topic_score_gemma":0.0000115409275,"teacher_disagreement_score":0.9120189,"about_ca_system_score_codex":0.00005032049,"about_ca_system_score_gemma":0.000007918541,"threshold_uncertainty_score":0.44699383},"labels":[],"label_agreement":null},{"id":"W4383220198","doi":"10.1109/tie.2023.3290255","title":"Scalable Game-Theoretic Decision-Making for Self-Driving Cars at Unsignalized Intersections","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Autonomous Vehicle Technology and Safety","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":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scalability; Computer science; Game theory; Graph; Graph theory; Distributed computing; Theoretical computer science","score_opus":0.013659266626618084,"score_gpt":0.24177568953054132,"score_spread":0.22811642290392323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383220198","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":[0.36157894,0.00007937224,0.631886,0.00011508924,0.0017053892,0.00062788895,0.00003885208,0.0036029269,0.0003655421],"genre_scores_gemma":[0.9984533,0.00024907515,0.0005209798,0.000023693268,0.00009931265,0.00025039123,0.000006681596,0.00009083388,0.0003056877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983403,0.000039622355,0.00040094036,0.000332077,0.00016307012,0.00072395697],"domain_scores_gemma":[0.9987899,0.00070069183,0.00005057269,0.00033353854,0.00004676207,0.000078519326],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032249722,0.0002641119,0.0003013557,0.00044450985,0.00047591745,0.000033475982,0.0002410005,0.0005728403,0.00018271526],"category_scores_gemma":[0.000030528452,0.0002948211,0.00022309572,0.00097280636,0.00007145924,0.00011607168,0.0000035379658,0.0009202105,0.00015457359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029305436,0.000090816124,0.000021115566,0.000024411662,0.00044885592,0.000008062875,0.000416198,0.8801953,0.0035090165,0.0015407174,0.0030360653,0.110416375],"study_design_scores_gemma":[0.0041525806,0.00077054306,0.000018354664,0.0002711983,0.0004157525,0.000067745124,0.00028428718,0.8673349,0.07948948,0.01001181,0.036242936,0.0009403836],"about_ca_topic_score_codex":0.0000040520194,"about_ca_topic_score_gemma":0.00014836148,"teacher_disagreement_score":0.63687444,"about_ca_system_score_codex":0.000990846,"about_ca_system_score_gemma":0.0001361073,"threshold_uncertainty_score":0.9999504},"labels":[],"label_agreement":null},{"id":"W4383535560","doi":"10.54254/2755-2721/5/20230566","title":"Exploration of the feasibility of steering wheelless cars based on Robotaxi operation data","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","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":"Queen's University","funders":"","keywords":"Steering wheel; Control (management); Automotive engineering; Course (navigation); Computer science; Self driving; Aeronautics; Transport engineering; Engineering; Artificial intelligence","score_opus":0.040728071366712915,"score_gpt":0.23949302465127675,"score_spread":0.19876495328456384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383535560","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":[0.5569869,0.00003477253,0.4418432,0.000105730665,0.00012263023,0.00024336693,0.000052929936,0.00032674253,0.00028372128],"genre_scores_gemma":[0.998201,0.0000053064336,0.0016767882,0.000006643866,0.000011298539,0.000010636699,0.00007444705,0.000012014189,0.0000018331151],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999506,0.000004890276,0.00017775453,0.00012149406,0.000111150846,0.00007871316],"domain_scores_gemma":[0.999639,0.000081609745,0.00002568282,0.0002238206,0.000016062406,0.000013871616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014059458,0.00007861679,0.00010845637,0.00006670803,0.00003699127,0.000005105704,0.00014650509,0.000050867242,0.000001747614],"category_scores_gemma":[0.000010056079,0.00007152221,0.000015596852,0.00020868707,0.00002518993,0.000079148704,0.00005816728,0.00009222014,0.0000015778063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035903722,0.0000064121773,0.00017190434,0.000055676006,0.000009988729,1.3296629e-7,0.00008013244,0.9864253,0.0031927815,0.008273669,0.000009691854,0.0017707472],"study_design_scores_gemma":[0.00018448221,0.000008720895,0.015514277,0.000020536127,0.0000053551557,3.089606e-7,0.0000364431,0.98147774,0.0022183242,0.0004556591,0.000018953631,0.000059176713],"about_ca_topic_score_codex":0.000001680527,"about_ca_topic_score_gemma":0.0000012739071,"teacher_disagreement_score":0.4412141,"about_ca_system_score_codex":0.000016968192,"about_ca_system_score_gemma":0.000014592053,"threshold_uncertainty_score":0.29165912},"labels":[],"label_agreement":null},{"id":"W4384161746","doi":"10.1109/tits.2023.3291196","title":"Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review","year":2023,"lang":"en","type":"review","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":93,"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; University of Waterloo","keywords":"Pedestrian; Trajectory; Computer science; Machine learning; Artificial intelligence; Motion (physics); Data mining; Transport engineering; Engineering","score_opus":0.04420379350282414,"score_gpt":0.27107016414971047,"score_spread":0.22686637064688633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384161746","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":[0.000015334967,0.85614884,0.133981,0.000006549717,0.0019838265,0.0056733196,0.00084883434,0.0012851526,0.00005714543],"genre_scores_gemma":[0.0037424592,0.989867,0.000011562135,0.000009886483,0.000050194285,0.0047946414,0.00032818885,0.0002395528,0.000956563],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9944905,0.0003898342,0.0033779119,0.00070094364,0.00050529104,0.0005354923],"domain_scores_gemma":[0.9982201,0.00035756055,0.00044317576,0.0007960551,0.000027609394,0.00015551854],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007894896,0.0008696411,0.0030440264,0.0009811361,0.0001352543,0.000039664363,0.00046735292,0.0009837161,0.000075434815],"category_scores_gemma":[0.000009787058,0.0008419866,0.000842818,0.0012162527,0.000074311385,0.00020004819,4.7094937e-7,0.0012816592,0.0012572766],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000073311685,0.00013822048,9.80596e-7,0.9474585,0.0006920252,0.000054942888,0.00018071126,0.033185888,0.0000013735635,0.000022743292,0.0001218787,0.018135417],"study_design_scores_gemma":[0.0005554454,0.00017634936,0.000008156027,0.8973852,0.005766068,0.000057105473,0.00046100075,0.004476113,0.00005609008,0.000009145568,0.08963931,0.0014100239],"about_ca_topic_score_codex":0.000074929994,"about_ca_topic_score_gemma":0.00030784163,"teacher_disagreement_score":0.13396944,"about_ca_system_score_codex":0.00082080683,"about_ca_system_score_gemma":0.000121097764,"threshold_uncertainty_score":0.99952036},"labels":[],"label_agreement":null},{"id":"W4384946173","doi":"10.1109/iwcmc58020.2023.10182377","title":"A Reinforcement Learning Based Approach for Controlling Autonomous Vehicles in Complex Scenarios","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Université du Québec à Trois-Rivières","funders":"","keywords":"Reinforcement learning; Intersection (aeronautics); Computer science; Motion (physics); Control (management); Artificial intelligence; Deep learning; Motion control; Simulation; Transport engineering; Engineering; Robot","score_opus":0.023715163538942756,"score_gpt":0.2319758513262791,"score_spread":0.20826068778733633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384946173","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":[0.08629204,0.000059095804,0.8987359,0.00032142623,0.0000626674,0.00095782196,0.0000038911317,0.0049006334,0.0086665265],"genre_scores_gemma":[0.991365,0.000006936325,0.0077332333,0.00008702994,0.000021432503,0.00022665464,0.00010230156,0.00003586072,0.0004215274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897236,0.000015660964,0.0003075747,0.00019472871,0.00007606176,0.00043360103],"domain_scores_gemma":[0.9996261,0.00013221739,0.000027233715,0.00015644157,0.000018239987,0.00003973755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038026564,0.0001540357,0.00025005636,0.00025856172,0.00011413938,0.000017224353,0.0001540667,0.00015804224,0.000039005718],"category_scores_gemma":[0.00003694466,0.00015733017,0.000073240866,0.000310327,0.000036809273,0.00005840953,0.000028375,0.0002600131,0.000039971143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023106073,0.000011425221,0.0008253791,0.0000617689,0.000020979007,0.0000029783832,0.000100298756,0.9897694,0.0015478941,0.001587549,0.00023179597,0.0058174417],"study_design_scores_gemma":[0.0014000342,0.000056625875,0.0011962684,0.000008738118,0.000006651212,8.819612e-7,0.00019425653,0.9919223,0.0008709318,0.00011214735,0.0040437146,0.00018745328],"about_ca_topic_score_codex":0.000017744473,"about_ca_topic_score_gemma":0.000012103381,"teacher_disagreement_score":0.905073,"about_ca_system_score_codex":0.00010754216,"about_ca_system_score_gemma":0.00002536497,"threshold_uncertainty_score":0.64157385},"labels":[],"label_agreement":null},{"id":"W4385231794","doi":"10.1109/itec55900.2023.10186896","title":"Perception Stack Design for the EcoCAR Electric Vehicle Challenge","year":2023,"lang":"en","type":"article","venue":"","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":"McMaster University","funders":"","keywords":"Computer science; Cluster analysis; Suite; Context (archaeology); Toolbox; Sensor fusion; Real-time computing; Automation; MATLAB; Simulation; Computer vision; Artificial intelligence; Engineering","score_opus":0.027458512939388738,"score_gpt":0.23341728684420918,"score_spread":0.20595877390482042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385231794","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":[0.33916625,0.0007726569,0.63749516,0.0045363205,0.00042048396,0.0013655757,0.0000076202673,0.009194244,0.0070417007],"genre_scores_gemma":[0.9978753,0.0007090785,0.0005483867,0.000042678363,0.000051076244,0.00012348869,0.0000032043163,0.000024737987,0.00062207266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949497,0.00001041459,0.00009817025,0.00010315896,0.00004622502,0.00024705273],"domain_scores_gemma":[0.99960136,0.00018004574,0.000008043076,0.00017882936,0.000013893243,0.00001783903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023647751,0.00008079746,0.000075793614,0.00006368406,0.00013272217,0.000008378931,0.00014492657,0.00011340676,0.00008198584],"category_scores_gemma":[0.000013130939,0.00005949452,0.00004277265,0.00024233379,0.000016597158,0.000050343573,0.000014769645,0.00012278707,0.00049509935],"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":[0.000056597244,0.00004813217,0.00024004075,0.00009599368,0.00023737409,0.000006467605,0.0012978619,0.18843776,0.04483762,0.024425823,0.057147887,0.6831685],"study_design_scores_gemma":[0.00019295597,0.0000743899,0.004907103,0.0000017240031,0.000012671844,0.0000012636759,0.00027410116,0.9829394,0.0022287967,0.0026349367,0.0066214553,0.00011117695],"about_ca_topic_score_codex":0.0000026672335,"about_ca_topic_score_gemma":0.0000058244823,"teacher_disagreement_score":0.79450166,"about_ca_system_score_codex":0.0000431243,"about_ca_system_score_gemma":0.000008739466,"threshold_uncertainty_score":0.6363666},"labels":[],"label_agreement":null},{"id":"W4385258922","doi":"10.1109/itec55900.2023.10186935","title":"Short-range and Long-range Obstacle Detection Method for a Delivery Robot Based on Multi-sensor Fusion","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Ontario Tech University","funders":"University of Ontario Institute of Technology","keywords":"Obstacle; Computer science; Lidar; Computer vision; Sensor fusion; Artificial intelligence; Motion planning; Obstacle avoidance; Reliability (semiconductor); Path (computing); Range (aeronautics); Object detection; Real-time computing; Robot; Mobile robot; Engineering; Remote sensing; Segmentation","score_opus":0.028588305811346942,"score_gpt":0.26654140209289034,"score_spread":0.2379530962815434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385258922","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":[0.29386517,0.00006165317,0.7034931,0.000117044925,0.00013559446,0.0003629594,0.000013849609,0.0018203597,0.0001302497],"genre_scores_gemma":[0.9676488,0.00004431105,0.03179886,0.00007392486,0.000028494802,0.00009871695,0.000011476921,0.000040829855,0.00025456183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924994,0.0000235039,0.00015908704,0.00023595014,0.00007061494,0.00026092675],"domain_scores_gemma":[0.9994916,0.00022406045,0.000012404509,0.00019663235,0.000025578916,0.00004977752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024199073,0.00015279473,0.0001681454,0.00019008268,0.00013257413,0.00001189108,0.00007050282,0.00023509515,0.000021800402],"category_scores_gemma":[0.000032306278,0.0001459125,0.00006508973,0.00021993282,0.000024311543,0.000064286665,0.000023544155,0.00015865684,0.000039152463],"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":[0.00018939411,0.000070018265,0.0060611623,0.00019963393,0.00006444766,0.000031047795,0.0001651471,0.10670171,0.049802274,0.000088017114,0.00021565684,0.8364115],"study_design_scores_gemma":[0.00061652146,0.000088611174,0.05720764,0.000012741144,0.000020592797,0.0000041047215,0.000053420732,0.92298555,0.018409235,0.00002997902,0.00039435778,0.00017724687],"about_ca_topic_score_codex":0.000019084222,"about_ca_topic_score_gemma":0.00023821328,"teacher_disagreement_score":0.8362343,"about_ca_system_score_codex":0.000051970295,"about_ca_system_score_gemma":0.0000049628247,"threshold_uncertainty_score":0.5950139},"labels":[],"label_agreement":null},{"id":"W4385322823","doi":"10.1109/iv55152.2023.10186749","title":"Intend-Wait-Perceive-Cross: Exploring the Effects of Perceptual Limitations on Pedestrian Decision-Making","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Huawei Technologies (Canada); York University","funders":"","keywords":"Pedestrian; Perception; Computer science; Affect (linguistics); Field (mathematics); Human–computer interaction; Artificial intelligence; Psychology; Communication; Transport engineering; Engineering","score_opus":0.03603555161170362,"score_gpt":0.2645312501166258,"score_spread":0.22849569850492218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385322823","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":[0.98957425,0.00003706553,0.0031042336,0.00011079213,0.0005588512,0.00017349869,0.0000028091708,0.0012680613,0.0051704426],"genre_scores_gemma":[0.99902004,0.00017976743,0.0004943125,0.000040474308,0.000041090476,0.00006561778,0.0000025794775,0.000030075998,0.0001260218],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99920815,0.000021448634,0.00023541102,0.00015610419,0.00012644165,0.00025241717],"domain_scores_gemma":[0.99741066,0.0021836183,0.000022267808,0.00032478353,0.000030058727,0.000028620034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016766934,0.00013613758,0.00015467795,0.00018315553,0.00017806425,0.000018416136,0.0002747753,0.00009454893,0.00004463808],"category_scores_gemma":[0.0006589245,0.00010026546,0.00008395333,0.0004463322,0.0001192993,0.00012550491,0.00006750833,0.0002893544,0.0003455988],"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":[0.00005629722,0.00006413554,0.004020591,0.00017847585,0.00017724036,0.000054045413,0.011679085,0.07470508,0.008342441,0.020071011,0.0021687525,0.8784828],"study_design_scores_gemma":[0.0011684801,0.0005242536,0.8610247,0.0007781816,0.00007297819,0.000022066859,0.012362001,0.09818552,0.013682485,0.008758922,0.002657018,0.0007634244],"about_ca_topic_score_codex":0.0000057066472,"about_ca_topic_score_gemma":0.00003443984,"teacher_disagreement_score":0.8777194,"about_ca_system_score_codex":0.000044755372,"about_ca_system_score_gemma":0.000012491431,"threshold_uncertainty_score":0.44420886},"labels":[],"label_agreement":null},{"id":"W4385333917","doi":"10.1109/isscs58449.2023.10190867","title":"A Novel Model for Driver Lane Change Prediction in Cooperative Adaptive Cruise Control Systems","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cruise control; Acceleration; Cooperative Adaptive Cruise Control; Advanced driver assistance systems; Computer science; Position (finance); Cruise; Control (management); Intelligent transportation system; Vehicle dynamics; Automotive engineering; Simulation; Real-time computing; Engineering; Artificial intelligence; Transport engineering","score_opus":0.04094487147945416,"score_gpt":0.22833445874730116,"score_spread":0.187389587267847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385333917","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":[0.05364028,0.00009462251,0.94084704,0.00015868898,0.00022093565,0.001331171,0.00039037818,0.0018914628,0.0014254372],"genre_scores_gemma":[0.9976571,0.000018987113,0.00035198565,0.000035200723,0.000045512683,0.00085130375,0.00002582796,0.000020652882,0.00099338],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994499,0.000006693634,0.00015176924,0.00013504545,0.000046393256,0.00021021315],"domain_scores_gemma":[0.99977976,0.0000503879,0.000013452798,0.00009757476,0.000033817047,0.000024978615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012068372,0.000102492595,0.0001678217,0.00012731642,0.000039510414,0.000005998295,0.00006587441,0.0001629696,0.000006321835],"category_scores_gemma":[0.000010043785,0.000095522664,0.000025254541,0.00017579278,0.00002314177,0.000111926944,0.000012359188,0.00012182723,0.00004908905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004309777,0.000020606498,0.00026916357,0.000026932597,0.000049325292,0.0000029316323,0.000751386,0.9785224,0.0021353273,0.0166571,0.0009501227,0.0005715984],"study_design_scores_gemma":[0.001198275,0.000042876443,0.0020709964,0.000018636512,0.000008573301,0.0000018989954,0.00020460179,0.9960116,0.00009584579,0.00009922802,0.00015047009,0.00009697449],"about_ca_topic_score_codex":0.000041931893,"about_ca_topic_score_gemma":0.00013993509,"teacher_disagreement_score":0.9440169,"about_ca_system_score_codex":0.00007923139,"about_ca_system_score_gemma":0.000011220532,"threshold_uncertainty_score":0.38953012},"labels":[],"label_agreement":null},{"id":"W4385334275","doi":"10.1109/isscs58449.2023.10190899","title":"LSTM-based Preceding Vehicle Behaviour Prediction during Aggressive Lane Change for ACC Application","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; University of Windsor","keywords":"Computer science; Artificial intelligence","score_opus":0.017590548038061267,"score_gpt":0.2341021386289792,"score_spread":0.21651159059091793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385334275","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":[0.9733824,0.00004976023,0.020452842,0.00025753715,0.00016784265,0.000662621,0.00004869406,0.0046439,0.00033437685],"genre_scores_gemma":[0.9980359,0.000028200579,0.00047304766,0.000018932042,0.00012845107,0.0010137702,0.00012605278,0.000035307115,0.00014035043],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934757,0.0000051069374,0.00015128362,0.00017732807,0.00006653565,0.00025215038],"domain_scores_gemma":[0.99968225,0.000036630197,0.000042885607,0.00017699042,0.000024915282,0.0000363397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010143756,0.00010771509,0.00010475027,0.00011352611,0.00022454579,0.000018140221,0.00010890205,0.00017723595,0.000013808325],"category_scores_gemma":[0.000015292952,0.00011389236,0.000041088464,0.00023964555,0.000018097446,0.00011596995,0.00002326732,0.000121399724,0.00006741137],"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":[0.00026642994,0.00022764581,0.3467649,0.0014115868,0.0002818551,0.00004304689,0.0021246444,0.07476816,0.34232637,0.0047438294,0.005050345,0.2219912],"study_design_scores_gemma":[0.0008958192,0.00005361428,0.19245818,0.000042901174,0.000029927214,0.0000040886885,0.00014104569,0.6900113,0.1152207,0.00027458853,0.00064140395,0.00022640002],"about_ca_topic_score_codex":0.000016671842,"about_ca_topic_score_gemma":0.000026881842,"teacher_disagreement_score":0.61524314,"about_ca_system_score_codex":0.00007255849,"about_ca_system_score_gemma":0.0000059035237,"threshold_uncertainty_score":0.46443957},"labels":[],"label_agreement":null},{"id":"W4385384759","doi":"10.23977/jaip.2023.060504","title":"Vehicle Driving Intent Recognition Based on Enhanced Bidirectional Long Short-Term Memory Network","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Context (archaeology); Hyperparameter; Trajectory; Feature (linguistics); Artificial intelligence; Machine learning; Term (time); Long short term memory; Artificial neural network; Recurrent neural network","score_opus":0.04173288790346016,"score_gpt":0.2954710916081289,"score_spread":0.25373820370466876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385384759","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":[0.6860281,0.00010783832,0.30576736,0.0015311409,0.0029454082,0.00017849471,0.0000027986448,0.0005423484,0.0028965112],"genre_scores_gemma":[0.9968164,0.00023918255,0.002113289,0.00014994445,0.0006117616,0.000008122602,0.0000043563264,0.000030703144,0.000026247762],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838674,0.00010764302,0.0006999464,0.00016063756,0.00031764692,0.00032735956],"domain_scores_gemma":[0.9982691,0.0009725051,0.00021951865,0.00016708924,0.0002804579,0.000091324095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014039028,0.00016190375,0.0002240358,0.00028890942,0.0001756247,0.000052999963,0.00021061086,0.00017076201,0.00015041223],"category_scores_gemma":[0.0008610579,0.00016313107,0.000113268135,0.00066671614,0.0000695301,0.0005400228,0.000028454066,0.0008512003,0.00038376],"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":[0.00025916452,0.00011325178,0.00019020849,0.000016169235,0.00009007895,0.00014786456,0.00021344064,0.4885991,0.016927006,0.00012840907,0.00039888226,0.49291644],"study_design_scores_gemma":[0.00008903637,0.00063781603,0.0032336253,0.00038821442,0.00013906912,0.00015815222,0.0013118524,0.60600597,0.3823371,0.0044231857,0.00081255496,0.00046340364],"about_ca_topic_score_codex":0.0000028152972,"about_ca_topic_score_gemma":0.000019731806,"teacher_disagreement_score":0.49245304,"about_ca_system_score_codex":0.00016521852,"about_ca_system_score_gemma":0.00006279458,"threshold_uncertainty_score":0.6652292},"labels":[],"label_agreement":null},{"id":"W4385625740","doi":"10.1109/icaeca56562.2023.10199402","title":"Vehicle Detection and Traffic Control Using Sensor Technology","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"FPInnovations","funders":"","keywords":"Computer science; Automotive engineering; Embedded system; Real-time computing; Engineering","score_opus":0.007220568318182968,"score_gpt":0.1992299510877782,"score_spread":0.19200938276959523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385625740","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":[0.9793041,0.00008122874,0.015265215,0.0002155604,0.00008252943,0.000088795256,0.0000016543262,0.0047468846,0.0002140711],"genre_scores_gemma":[0.99945647,0.000021750953,0.00041485592,0.000017383909,0.000015399299,0.000007209766,3.7146603e-7,0.000017314223,0.00004923119],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995545,0.000005845308,0.000100986916,0.00011147013,0.000030095836,0.00019706326],"domain_scores_gemma":[0.9998278,0.000023199274,0.000008864057,0.00010895931,0.000009449864,0.000021749098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000068829606,0.000078791025,0.00010589507,0.00021855616,0.00010571114,0.000008450921,0.000045529312,0.00021041077,0.000012955095],"category_scores_gemma":[0.000010105667,0.00007946128,0.000015929312,0.00039009436,0.0000850104,0.000044884426,0.000015181518,0.00015404506,0.00005651296],"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":[0.000016384665,0.000011817178,0.0022780087,0.000040932795,0.000096668206,0.000042648626,0.00009942314,0.20576265,0.49034476,0.0019224413,0.000044288056,0.29933995],"study_design_scores_gemma":[0.00035035025,0.000020914591,0.0018341664,0.0000029262526,0.000010132517,0.000034396824,0.00014167206,0.97704923,0.019669747,0.00029539433,0.00048796477,0.00010312953],"about_ca_topic_score_codex":0.0000025350716,"about_ca_topic_score_gemma":0.000015413574,"teacher_disagreement_score":0.77128655,"about_ca_system_score_codex":0.000025319607,"about_ca_system_score_gemma":0.000004620905,"threshold_uncertainty_score":0.3240337},"labels":[],"label_agreement":null},{"id":"W4385730966","doi":"10.1109/dsn-s58398.2023.00058","title":"Beyond Detection: Securing Robotic Vehicles","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Robot; Computer security; Artificial intelligence","score_opus":0.007117624538805633,"score_gpt":0.1941671005522332,"score_spread":0.18704947601342758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385730966","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":[0.9364315,0.00013402531,0.014048641,0.0002695504,0.00049687555,0.00008916884,8.9612433e-7,0.011009617,0.03751968],"genre_scores_gemma":[0.99884814,0.00003811544,0.00020321459,0.000025774576,0.000045111825,0.000011147821,0.0000016106629,0.000019627203,0.00080723804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954826,0.000004295754,0.00010049298,0.000096102995,0.000047527472,0.0002033007],"domain_scores_gemma":[0.9997919,0.000024395662,0.00000591614,0.00014268015,0.0000066821353,0.00002840474],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006841751,0.00007687463,0.00008160853,0.0001027739,0.000077920056,0.000008394716,0.00008861444,0.00010466685,0.00008024251],"category_scores_gemma":[0.0000054692937,0.0000778952,0.000029624534,0.00031943782,0.000019678442,0.00006614719,0.000031399442,0.00014892367,0.0010931432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009621492,0.000023924016,0.0034275926,0.0001581747,0.00019803342,0.00012634597,0.0006615159,0.5223386,0.07572466,0.028396286,0.0051236404,0.3638116],"study_design_scores_gemma":[0.00035233382,0.00003972032,0.028176248,0.000012586212,0.000018203891,0.000040557505,0.00029353722,0.7475138,0.19970027,0.016590184,0.0068140416,0.00044853878],"about_ca_topic_score_codex":0.000002654699,"about_ca_topic_score_gemma":0.000023374494,"teacher_disagreement_score":0.36336306,"about_ca_system_score_codex":0.000026933092,"about_ca_system_score_gemma":0.0000033854865,"threshold_uncertainty_score":0.99968463},"labels":[],"label_agreement":null},{"id":"W4386066695","doi":"10.1109/cvpr52729.2023.01319","title":"ReasonNet: End-to-End Driving with Temporal and Global Reasoning","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":94,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; End-to-end principle; Benchmark (surveying); Artificial intelligence; Anticipation (artificial intelligence); Perception; Exploit; Event (particle physics); Adverse weather; Software deployment; Computer vision; Machine learning; Computer security","score_opus":0.005094810900067455,"score_gpt":0.20334552712164697,"score_spread":0.19825071622157953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386066695","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":[0.96680886,0.00006149689,0.0064023104,0.0004528213,0.00005909482,0.00010308646,0.0000057888933,0.0030950764,0.023011466],"genre_scores_gemma":[0.9940292,0.000017993698,0.005473083,0.000042285512,0.000026752885,0.000011399592,0.00000648537,0.000020512625,0.0003722897],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993227,0.0000070932065,0.00010589788,0.00018599036,0.000080579746,0.00029774717],"domain_scores_gemma":[0.9996901,0.000025060126,0.000010909548,0.0001766236,0.000010046472,0.00008725714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012796804,0.00012707255,0.00013527925,0.00006079249,0.00008713465,0.000020078121,0.00009691263,0.00010205012,0.00006700006],"category_scores_gemma":[0.000016710614,0.00011061604,0.000015054838,0.00042568106,0.00004687066,0.00008483482,0.000067826055,0.00012678989,0.000118354794],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003422482,0.000016748609,0.79606867,0.000057036388,0.00018753602,0.00024846237,0.0005895442,0.011326283,0.0009070722,0.04345544,0.0062654936,0.1408435],"study_design_scores_gemma":[0.0007872956,0.00019161792,0.85291094,0.0001480707,0.000035704274,0.00021867367,0.0009766321,0.123342015,0.0016738436,0.0014554416,0.017445713,0.0008140383],"about_ca_topic_score_codex":0.000060129052,"about_ca_topic_score_gemma":0.00043396815,"teacher_disagreement_score":0.14002946,"about_ca_system_score_codex":0.00005088665,"about_ca_system_score_gemma":0.000014347945,"threshold_uncertainty_score":0.45107913},"labels":[],"label_agreement":null},{"id":"W4386076044","doi":"10.1109/cvpr52729.2023.01321","title":"FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs","year":2023,"lang":"en","type":"article","venue":"","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":"Directed acyclic graph; Computer science; Trajectory; Graph; Joint (building); Directed graph; Set (abstract data type); Sequence (biology); Pipeline (software); Theoretical computer science; Artificial intelligence; Algorithm; Machine learning","score_opus":0.033899020226170515,"score_gpt":0.25342057294448656,"score_spread":0.21952155271831603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386076044","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":[0.9540845,0.00002281747,0.029025447,0.00012442352,0.0013271382,0.00026017943,0.000013127879,0.013666026,0.0014763427],"genre_scores_gemma":[0.99832195,0.00015358861,0.00033067062,0.000015249009,0.000036306723,0.000044427456,0.000101140446,0.000034593428,0.00096207677],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991117,0.000031201478,0.00027480163,0.00022082443,0.00010789336,0.00025357335],"domain_scores_gemma":[0.9996394,0.000028911425,0.000037442253,0.00021958337,0.000024174522,0.00005050542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001273092,0.00016012289,0.00016531069,0.0003008555,0.000098568846,0.000017578735,0.00007555831,0.0002365445,0.00034018682],"category_scores_gemma":[0.000041415726,0.00015532889,0.000090268004,0.00046904682,0.000025618545,0.0002274681,0.000036059042,0.00032255915,0.0004451893],"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":[0.00015273645,0.0003765051,0.043653805,0.00020222859,0.00084880163,0.000040716583,0.0021296628,0.07433947,0.66292834,0.0025098263,0.016914092,0.1959038],"study_design_scores_gemma":[0.0008203073,0.000033569493,0.3678363,0.000016320288,0.00002192353,0.000003634317,0.00013708089,0.6014562,0.025763176,0.00038646333,0.0033296677,0.00019536901],"about_ca_topic_score_codex":0.00004288114,"about_ca_topic_score_gemma":0.000037528687,"teacher_disagreement_score":0.6371652,"about_ca_system_score_codex":0.00014688898,"about_ca_system_score_gemma":0.0000050612334,"threshold_uncertainty_score":0.63341284},"labels":[],"label_agreement":null},{"id":"W4386076117","doi":"10.1109/cvpr52729.2023.00139","title":"Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Occupancy; Occupancy grid mapping; Trajectory; Convolutional neural network; Artificial intelligence; Curse of dimensionality; Field (mathematics); Perception; Pedestrian; Planner; Object (grammar); Grid; Machine learning; Computer vision; Robot; Mobile robot","score_opus":0.0062932883330383115,"score_gpt":0.21446226411331187,"score_spread":0.20816897578027357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386076117","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":[0.95862734,0.000013153866,0.0376454,0.00014461282,0.00010006117,0.00015314629,0.000004715986,0.0021227489,0.0011888088],"genre_scores_gemma":[0.9972174,0.00007532316,0.0024547088,0.000015921172,0.000028172777,0.00004629341,0.000009921706,0.000009528161,0.0001426973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996538,0.0000029856285,0.00009664801,0.00009139138,0.00002101932,0.00013416175],"domain_scores_gemma":[0.99987763,0.00002876098,0.0000047135295,0.00006936091,0.0000052200244,0.000014307155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008474391,0.000053953776,0.00006639863,0.000090979025,0.0000371339,0.000005142045,0.000031445124,0.00014193036,0.00002669674],"category_scores_gemma":[0.0000071514824,0.000055429933,0.000014833045,0.00011450668,0.000006457173,0.00006950004,0.0000147970395,0.00008826847,0.000021747119],"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":[0.000029796716,0.000053963864,0.42857596,0.00043379428,0.000102864076,0.000009130228,0.0040311166,0.057015374,0.0149972,0.004302652,0.016210321,0.47423783],"study_design_scores_gemma":[0.00016074056,0.000020073721,0.2832617,0.000006176196,0.0000031608058,0.000002287516,0.000097674114,0.7150657,0.00013946056,0.0007082403,0.00048323403,0.000051573865],"about_ca_topic_score_codex":0.0000046573273,"about_ca_topic_score_gemma":0.00006427561,"teacher_disagreement_score":0.6580503,"about_ca_system_score_codex":0.000028369002,"about_ca_system_score_gemma":0.000003076797,"threshold_uncertainty_score":0.2260367},"labels":[],"label_agreement":null},{"id":"W4386076483","doi":"10.1109/cvpr52729.2023.00140","title":"UniSim: A Neural Closed-Loop Sensor Simulator","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":131,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Leverage (statistics); Computer science; Artificial intelligence; Convolutional neural network; Closed loop; Viewpoints; Simulation; Real-time computing; Control engineering; Engineering","score_opus":0.010652823673883363,"score_gpt":0.2166913990916978,"score_spread":0.20603857541781442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386076483","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":[0.9809631,0.00003236229,0.00070753344,0.00045434636,0.00021212689,0.000086736814,0.000005402423,0.0073404163,0.010197972],"genre_scores_gemma":[0.9959682,0.000016855358,0.00015958997,0.00010191709,0.00003722014,0.0000058025507,0.0000063725843,0.000027709715,0.0036763148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999422,0.0000072832613,0.00012586958,0.00011786875,0.000058306756,0.00026864436],"domain_scores_gemma":[0.9996672,0.00005049114,0.000007256619,0.00022059317,0.000010381351,0.000044073473],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006329233,0.00010393219,0.000113349255,0.00009225211,0.000054721026,0.000007943565,0.0001195057,0.00013852012,0.00019060298],"category_scores_gemma":[0.00001280336,0.00009748281,0.000040544317,0.000340205,0.000034477704,0.00005397267,0.000038922903,0.00017634267,0.0014344386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025364741,0.00004004442,0.00882299,0.00010162536,0.00020134496,0.0003511346,0.000368767,0.8079194,0.019822491,0.022484893,0.032142635,0.107719354],"study_design_scores_gemma":[0.00024480687,0.000019050396,0.00663875,0.0000026123303,0.0000067077253,0.0000072970083,0.000093850715,0.9736856,0.0052071814,0.00046790604,0.013443111,0.000183114],"about_ca_topic_score_codex":0.000004191073,"about_ca_topic_score_gemma":0.0000057897364,"teacher_disagreement_score":0.16576625,"about_ca_system_score_codex":0.000020107298,"about_ca_system_score_gemma":0.000006164741,"threshold_uncertainty_score":0.99934304},"labels":[],"label_agreement":null},{"id":"W4386083237","doi":"10.1109/tvt.2023.3307409","title":"From Naturalistic Traffic Data to Learning-Based Driving Policy: A Sim-to-Real Study","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Autonomous Vehicle Technology and Safety","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 Toronto; York University","funders":"","keywords":"Computer science; Reinforcement learning; Observability; Robustness (evolution); Modular design; Artificial intelligence; Machine learning; Generalization; Fidelity","score_opus":0.018735276254664753,"score_gpt":0.27329411346283755,"score_spread":0.2545588372081728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386083237","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":[0.8568863,0.000015103046,0.12763745,0.0041242233,0.0003711826,0.00065260945,0.00012658558,0.010154711,0.000031810505],"genre_scores_gemma":[0.9986782,0.000016533326,0.0006539877,0.00010645644,0.000058364792,0.0002241276,0.00005246578,0.00010119796,0.00010862964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792546,0.000062744264,0.0003736116,0.0007546902,0.00022755774,0.00065592583],"domain_scores_gemma":[0.9981257,0.00016278595,0.000033353142,0.0014915067,0.000043213153,0.00014347142],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022912485,0.00035705278,0.0004223493,0.001972933,0.0003255729,0.000028857425,0.0011610825,0.0005936275,0.000036479454],"category_scores_gemma":[0.00004947986,0.00039280747,0.000078296434,0.003249111,0.00010098331,0.00008695552,0.000023245093,0.0013166543,0.001029189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021953038,0.00015252984,0.00014672727,0.000008796616,0.00016648935,0.000114978764,0.00026840708,0.96451634,0.0060324194,0.000035986915,0.00020638129,0.028328994],"study_design_scores_gemma":[0.0014297382,0.0009455403,0.0035709469,0.0000856527,0.00019547618,0.000016356624,0.0014869171,0.96890426,0.015556753,0.00019817615,0.0065882253,0.0010219885],"about_ca_topic_score_codex":0.00019832696,"about_ca_topic_score_gemma":0.0011198308,"teacher_disagreement_score":0.14179191,"about_ca_system_score_codex":0.00021496619,"about_ca_system_score_gemma":0.00007397124,"threshold_uncertainty_score":0.99985236},"labels":[],"label_agreement":null},{"id":"W4386113162","doi":"10.3390/app13179503","title":"Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Adversarial system; Computer science; Trajectory; Blockchain; Artificial neural network; Generative adversarial network; Artificial intelligence; Generative grammar; Deep learning; Data mining; Machine learning; Computer security","score_opus":0.007448631020037727,"score_gpt":0.21957752722292437,"score_spread":0.21212889620288664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386113162","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":[0.47393042,0.00015029224,0.5130167,0.00051591644,0.00066522387,0.0009246681,0.000033063323,0.0026051104,0.008158599],"genre_scores_gemma":[0.99780947,0.000045145604,0.0012490104,0.00015278878,0.00019621328,0.00039810568,0.000038192786,0.000018794462,0.00009225688],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985502,0.000019428342,0.00026302657,0.00048283374,0.0002455023,0.00043904688],"domain_scores_gemma":[0.9995628,0.0000619091,0.00004443047,0.00024060407,0.000020753261,0.0000695086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003619676,0.00018532634,0.0001803431,0.00010973944,0.00041808226,0.00002526153,0.00035608947,0.00021819513,0.000016389438],"category_scores_gemma":[0.000009840505,0.00018359841,0.00003753249,0.0012165183,0.00023902276,0.00007668247,0.00006047293,0.00022354913,0.0001260519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001981907,0.000017623193,0.000034918976,0.0000062630097,0.00002179405,5.0180284e-7,0.0014927016,0.84276634,0.13417165,0.0038950155,0.010445154,0.0071281814],"study_design_scores_gemma":[0.0006100962,0.00017267853,0.0010831738,0.000013922738,0.000024973275,0.0000018010394,0.0014841284,0.7483327,0.23507021,0.002761831,0.0099088885,0.0005356261],"about_ca_topic_score_codex":0.000012793835,"about_ca_topic_score_gemma":0.000036793328,"teacher_disagreement_score":0.52387905,"about_ca_system_score_codex":0.00009895918,"about_ca_system_score_gemma":0.000034977245,"threshold_uncertainty_score":0.74869263},"labels":[],"label_agreement":null},{"id":"W4386246157","doi":"10.24908/iqurcp16764","title":"NSERC USRA Research Paper","year":2023,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Python (programming language); Computer science; Cruise control; Disk formatting; Onboarding; Real-time computing; Multimedia; Human–computer interaction; Artificial intelligence; Control (management); Operating system","score_opus":0.14159985330735003,"score_gpt":0.3822881218415242,"score_spread":0.2406882685341742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386246157","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":[0.8361319,0.0003255236,0.00068153103,0.062806636,0.0007184518,0.0016676842,0.000020146354,0.008854653,0.0887935],"genre_scores_gemma":[0.99122167,0.0036589548,0.00021032724,0.000044956425,0.0002759533,0.00039617275,0.000018297003,0.00011649909,0.0040571466],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9944579,0.00014279727,0.0005118973,0.00079659256,0.0017024302,0.0023883637],"domain_scores_gemma":[0.9969088,0.00054230215,0.000034938774,0.0004888417,0.0016539167,0.0003712169],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005954419,0.00034578049,0.00040330316,0.0013814723,0.0008683147,0.00036923194,0.0013332801,0.0005761806,0.00016442464],"category_scores_gemma":[0.0006803763,0.00034602094,0.000094644936,0.0044815904,0.0017184434,0.0008684417,0.0007060238,0.0032535496,0.004490616],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011072274,0.0001103079,0.0035783644,0.0006546182,0.00022778624,0.00018082577,0.0062280404,0.00016590653,0.044885553,0.5780634,0.31739813,0.04839633],"study_design_scores_gemma":[0.0010438863,0.00049901777,0.007476324,0.000433566,0.000015874928,0.000047825615,0.011155802,0.03863814,0.021633513,0.73622787,0.18163793,0.001190269],"about_ca_topic_score_codex":0.000114411254,"about_ca_topic_score_gemma":0.000025471538,"teacher_disagreement_score":0.15816444,"about_ca_system_score_codex":0.00042393288,"about_ca_system_score_gemma":0.00032828358,"threshold_uncertainty_score":0.9998992},"labels":[],"label_agreement":null},{"id":"W4386253056","doi":"10.1007/s11042-023-16522-3","title":"LEHA: A novel lightweight efficient and highly accurate lane departure warning system","year":2023,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Computer science; Deep learning; Focus (optics); Distraction; Warning system; Artificial intelligence; Simple (philosophy); Computer security; Real-time computing; Telecommunications","score_opus":0.012353765354816559,"score_gpt":0.21452772429085332,"score_spread":0.20217395893603676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386253056","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":[0.9098811,0.0019388901,0.07376753,0.0015086475,0.00023450083,0.0015414277,0.00047864032,0.006577744,0.0040714853],"genre_scores_gemma":[0.99720246,0.00015344104,0.0019355036,0.0000135935,0.0000802102,0.000402987,0.00008256254,0.00002113738,0.00010808693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936837,0.000005306877,0.00016213542,0.00020009515,0.000056690104,0.0002074141],"domain_scores_gemma":[0.99958456,0.000121037534,0.000026762793,0.00017696401,0.000016449212,0.00007425072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008781854,0.00012526386,0.00014801632,0.00007180546,0.00019681852,0.000031721574,0.00008642301,0.00014241991,0.0000030545968],"category_scores_gemma":[0.000009238846,0.00011264825,0.000019399053,0.0002469509,0.0000574958,0.000043876666,0.0000523858,0.00017573293,0.00010251194],"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":[0.000033161425,0.00023591725,0.0048926896,0.0014731003,0.00043406515,0.000054349588,0.0040603722,0.07601597,0.123342134,0.17772166,0.0046998537,0.6070367],"study_design_scores_gemma":[0.000556951,0.00001330243,0.026297772,0.000041962794,0.0000387164,0.000030475354,0.00026775463,0.86174726,0.0019481779,0.00005189378,0.108707674,0.0002980513],"about_ca_topic_score_codex":0.000004395293,"about_ca_topic_score_gemma":0.0000059308195,"teacher_disagreement_score":0.7857313,"about_ca_system_score_codex":0.00002249414,"about_ca_system_score_gemma":0.000007346069,"threshold_uncertainty_score":0.45936626},"labels":[],"label_agreement":null},{"id":"W4386598463","doi":"10.1109/icip49359.2023.10222754","title":"Context-Aware Pedestrian Trajectory Prediction with Multimodal Transformer","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science; Pedestrian; Encoder; Trajectory; Artificial intelligence; Transformer; Computer vision; Engineering","score_opus":0.007524199647448098,"score_gpt":0.18405016805021526,"score_spread":0.17652596840276716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386598463","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":[0.94190896,0.00004464661,0.035990484,0.00030262902,0.0001892023,0.00027016774,0.00003736609,0.008124651,0.013131924],"genre_scores_gemma":[0.99908143,0.000039793067,0.000110907655,0.000023467794,0.000029516965,0.000033727363,0.000025577237,0.000026508138,0.0006290658],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945146,0.0000059849363,0.000120997676,0.00012211713,0.00006848574,0.00023096887],"domain_scores_gemma":[0.9998003,0.000021083944,0.000006921961,0.00011945838,0.000011139967,0.00004106063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006546302,0.00011059928,0.00010965633,0.0000951647,0.000068592315,0.0000063692964,0.00007383881,0.00015095637,0.00015078591],"category_scores_gemma":[0.0000018074438,0.00009135382,0.00003253403,0.00021365957,0.000051440027,0.000113534334,0.0000028642032,0.00018930131,0.0001861978],"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":[0.00041450816,0.00018882359,0.08765391,0.00050114765,0.0010512276,0.00024475384,0.005462615,0.16415744,0.019244675,0.00541041,0.02175692,0.6939136],"study_design_scores_gemma":[0.0045010587,0.0004999511,0.28772795,0.00006448003,0.00009008536,0.000075584954,0.0034240012,0.63711756,0.035656013,0.00030567416,0.02964007,0.0008975906],"about_ca_topic_score_codex":0.000025541342,"about_ca_topic_score_gemma":0.00022555717,"teacher_disagreement_score":0.693016,"about_ca_system_score_codex":0.000033156764,"about_ca_system_score_gemma":0.000017109092,"threshold_uncertainty_score":0.37253004},"labels":[],"label_agreement":null},{"id":"W4386634516","doi":"10.1109/jiot.2023.3314373","title":"Group Frenet Frame CAV Path Planning on Highways","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Waypoint; Motion planning; Frame (networking); Frenet–Serret formulas; Computer science; Path (computing); Reference frame; Motion (physics); Term (time); Real-time computing; Simulation; Robot; Artificial intelligence; Computer network; Mathematics; Geometry","score_opus":0.013935504394964342,"score_gpt":0.2269332175977071,"score_spread":0.21299771320274274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386634516","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":[0.9818776,0.00015848347,0.01234765,0.00013214591,0.0016932285,0.00004467276,0.0000047141784,0.0011665121,0.0025749805],"genre_scores_gemma":[0.9986823,0.000063147025,0.00074045494,0.000119506105,0.00015797041,0.000002688126,0.0000031659206,0.00003692487,0.00019384777],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898034,0.000020254822,0.0003684908,0.00012647592,0.0001913212,0.000313115],"domain_scores_gemma":[0.9995455,0.00008063301,0.00011284129,0.00016146174,0.000025972748,0.00007359108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036521224,0.00016108653,0.00023414187,0.00026883458,0.000057059246,0.000031791045,0.00040961403,0.00022105711,0.00005475302],"category_scores_gemma":[0.000030367779,0.00014894732,0.00010580787,0.0001523457,0.000053851087,0.00017364707,0.000039388648,0.001005646,0.00015325367],"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":[0.000368915,0.0002438373,0.014339202,0.00035803794,0.0015381696,0.0026857727,0.027488863,0.17749272,0.1656967,0.014880568,0.533558,0.061349224],"study_design_scores_gemma":[0.0029321027,0.0017817732,0.022456009,0.0027231057,0.00012324336,0.0015448672,0.0012148438,0.6851903,0.21431278,0.029136857,0.03690444,0.0016796774],"about_ca_topic_score_codex":0.000014127186,"about_ca_topic_score_gemma":7.01374e-7,"teacher_disagreement_score":0.5076976,"about_ca_system_score_codex":0.00007769198,"about_ca_system_score_gemma":0.0000105290455,"threshold_uncertainty_score":0.60738957},"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":"W4386767574","doi":"10.1155/2023/6926304","title":"Dynamic Trajectory Planning for Automated Lane Changing Using the Quintic Polynomial Curve","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Trajectory; Quintic function; Sequential quadratic programming; Polynomial; Mathematical optimization; Quadratic function; Function (biology); Algorithm; Computer science; Trajectory optimization; Quadratic equation; Quadratic programming; Dynamic programming; Mathematics; Control theory (sociology); Optimal control; Artificial intelligence","score_opus":0.011638856780733151,"score_gpt":0.2696381606600579,"score_spread":0.25799930387932474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386767574","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":[0.9350835,0.00023903875,0.063405246,0.000072980874,0.00054096815,0.00012637039,0.0000134385555,0.0005122696,0.000006191026],"genre_scores_gemma":[0.9936729,0.0000380748,0.0061647035,0.000010279139,0.00005305675,0.0000049159116,0.000022607312,0.000026431433,0.000007035496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993135,0.000009160226,0.00032603025,0.00006216768,0.00008531959,0.00020382785],"domain_scores_gemma":[0.9996632,0.00007849467,0.00012950053,0.00006367866,0.000041790936,0.000023373299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002343155,0.000093366834,0.00015851021,0.00024067127,0.00010948974,0.0000066584726,0.00009881422,0.00007677225,0.0000023091575],"category_scores_gemma":[0.000010757898,0.000077798584,0.000085195614,0.0003026266,0.000023880877,0.00018162071,0.0000010771806,0.00019409067,0.0000011514155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050643157,0.0000043972173,0.00028190532,0.000042975364,0.00004934139,0.000018320876,0.001958601,0.96635,0.028671142,0.0000334715,0.000026970025,0.0025122121],"study_design_scores_gemma":[0.0012456609,0.00011452462,0.08914109,0.00016729572,0.00011100152,0.000036358655,0.0029353492,0.9020625,0.0032333247,0.00032281363,0.0004315928,0.00019848422],"about_ca_topic_score_codex":7.5870315e-7,"about_ca_topic_score_gemma":0.000009762984,"teacher_disagreement_score":0.088859186,"about_ca_system_score_codex":0.00006698476,"about_ca_system_score_gemma":0.000023731982,"threshold_uncertainty_score":0.3172534},"labels":[],"label_agreement":null},{"id":"W4386803149","doi":"10.23977/acss.2023.070801","title":"Research on Lane Line Detection Based on Around View System","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Perspective (graphical); Artificial intelligence; Convolutional neural network; Computer vision; Segmentation; Line (geometry); Object detection; Position (finance); Pattern recognition (psychology); Mathematics","score_opus":0.0361135085767188,"score_gpt":0.30595921716654084,"score_spread":0.26984570858982204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386803149","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":[0.6024905,0.009916432,0.3764907,0.00013651792,0.0033546858,0.0011093783,0.000021668006,0.0029234148,0.0035567097],"genre_scores_gemma":[0.99916977,0.00040459738,0.00008714973,0.000017475613,0.00020477238,0.00007033134,0.0000044924745,0.000019569743,0.00002185998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887085,0.0001419723,0.00026657403,0.00024671375,0.00019010442,0.0002837684],"domain_scores_gemma":[0.9992593,0.00042046592,0.000026409489,0.00022587433,0.000028560158,0.000039368166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009479194,0.00012965228,0.0002487172,0.0003630626,0.00010489223,0.00003606981,0.00012816158,0.00013215076,0.0000011353492],"category_scores_gemma":[0.000004565278,0.000113932394,0.000022993063,0.00053113286,0.00003705122,0.00008205054,0.000025475401,0.00033276834,0.00008912276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017805674,0.000012926631,0.00031222735,0.0005178261,0.000007935682,0.00004704343,0.00002653383,0.92405134,0.00011872607,0.0013359423,0.0000369146,0.07351478],"study_design_scores_gemma":[0.0002600284,0.0002879582,0.0005018087,0.000677005,0.0000014759017,0.000008634769,0.00010686435,0.99403757,0.00039248707,0.00009558113,0.0035100016,0.000120562865],"about_ca_topic_score_codex":0.000015995689,"about_ca_topic_score_gemma":0.000022787815,"teacher_disagreement_score":0.39667925,"about_ca_system_score_codex":0.00009257794,"about_ca_system_score_gemma":0.000006524913,"threshold_uncertainty_score":0.46460283},"labels":[],"label_agreement":null},{"id":"W4386920290","doi":"10.1109/sas58821.2023.10254164","title":"Driving Behaviour Detection Using Smart Steering Wheel: Supervised and Unsupervised Classification","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Machine learning; Automotive engineering; Engineering","score_opus":0.023425760664387583,"score_gpt":0.22700222943588927,"score_spread":0.20357646877150168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386920290","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":[0.97850657,0.000049058268,0.017612636,0.000044605462,0.0002075418,0.00012859545,0.0000014235901,0.0029543242,0.0004952431],"genre_scores_gemma":[0.9988491,0.00006756945,0.0009101592,0.000009942355,0.00003230694,0.000016681752,0.0000066752955,0.000040491683,0.0000670563],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992579,0.000015956637,0.00018999279,0.00016693548,0.000076896526,0.0002923241],"domain_scores_gemma":[0.99969167,0.000031162876,0.000014711215,0.00018354208,0.00001718263,0.00006174786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001960666,0.00013329953,0.00012753009,0.00020486763,0.00016831148,0.000025861622,0.00007502327,0.00018338808,0.000022484857],"category_scores_gemma":[0.000014602047,0.00014665973,0.00003007368,0.00036631397,0.0000354077,0.00024592824,0.00004468208,0.0001835551,0.00003722534],"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":[0.0000067920314,0.000022424805,0.3016383,0.00009486891,0.00006276759,0.000007775828,0.00075376517,0.0070167556,0.61725914,0.0008201995,0.000047006924,0.07227021],"study_design_scores_gemma":[0.00016428079,0.000009501501,0.29873395,0.00001243179,0.000014676628,0.00001052394,0.00046496638,0.68310225,0.01718119,0.000043163287,0.0001067092,0.00015633432],"about_ca_topic_score_codex":0.000026743119,"about_ca_topic_score_gemma":0.000048747614,"teacher_disagreement_score":0.67608553,"about_ca_system_score_codex":0.00008019926,"about_ca_system_score_gemma":0.0000068873405,"threshold_uncertainty_score":0.598061},"labels":[],"label_agreement":null},{"id":"W4386948151","doi":"10.1016/j.aap.2023.107225","title":"Driving safety zone model oriented motion planning framework for autonomous truck platooning","year":2023,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Platoon; Collision; Collision avoidance; Simulation; Engineering; Truck; Stability (learning theory); Controller (irrigation); Control theory (sociology); String (physics); Computer science; Control engineering; Automotive engineering; Control (management); Artificial intelligence; Computer security","score_opus":0.01564959708787618,"score_gpt":0.2782830739179422,"score_spread":0.26263347683006605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386948151","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":[0.39900976,0.000076905315,0.5991147,0.000045330056,0.000120742414,0.00020483293,0.0000010344417,0.0013349642,0.0000916937],"genre_scores_gemma":[0.97840345,0.000061230894,0.020360397,0.000012078081,0.000074500334,0.00011473369,0.00041603457,0.000050397863,0.00050719077],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982567,0.000037531277,0.00063120044,0.0004067072,0.00019565923,0.0004722131],"domain_scores_gemma":[0.9991503,0.000150759,0.00016608859,0.0004073393,0.00005654854,0.00006898677],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00066974276,0.0002463256,0.00043373404,0.00080571737,0.00034316996,0.000045933586,0.00023535936,0.0003564068,0.00006500975],"category_scores_gemma":[0.000097806675,0.00028677145,0.00038128372,0.0017581043,0.000024259105,0.0003732393,0.00008247427,0.00033362312,0.000051695843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017833288,0.000018556018,0.065322034,0.0000073464585,0.00095957256,0.0000031940249,0.00036204586,0.90962183,0.0004104091,0.0056801853,0.0000788926,0.017518127],"study_design_scores_gemma":[0.00029421796,0.000024497507,0.05726812,0.000062798645,0.0008425633,0.0000013774637,0.00016598111,0.9273851,0.0008238097,0.012733247,0.00012664669,0.0002716714],"about_ca_topic_score_codex":0.0000061653705,"about_ca_topic_score_gemma":0.00013528972,"teacher_disagreement_score":0.5793937,"about_ca_system_score_codex":0.00022601092,"about_ca_system_score_gemma":0.000020422038,"threshold_uncertainty_score":0.99995846},"labels":[],"label_agreement":null},{"id":"W4386980319","doi":"10.1111/mice.13099","title":"Identifying dynamic interaction patterns in mandatory and discretionary lane changes using graph structure","year":2023,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality; Canadian Institute for Advanced Research","keywords":"Computer science; Hidden Markov model; Graph; Process (computing); Trajectory; Data mining; Artificial intelligence; Theoretical computer science","score_opus":0.005751290321224317,"score_gpt":0.20577965497049425,"score_spread":0.20002836464926993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386980319","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":[0.912882,0.00041050112,0.08526371,0.000033464697,0.00059669657,0.00010092222,0.00004061306,0.00066286814,0.0000092329965],"genre_scores_gemma":[0.99723464,0.0004093512,0.0021159598,0.00001813297,0.00010273626,0.0000057365846,0.00007005274,0.00004067485,0.0000026894709],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991265,0.000012798313,0.00020682307,0.0002679815,0.00008105106,0.00030485558],"domain_scores_gemma":[0.9997088,0.000039682833,0.00003192659,0.00014787137,0.000010078734,0.00006167536],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007140275,0.0002495598,0.00024320795,0.0005098425,0.00007570862,0.000045617777,0.000105601415,0.00019963467,0.00001637225],"category_scores_gemma":[0.0000048161396,0.00026048176,0.000025229283,0.00031013563,0.000033070424,0.00023126225,0.00013501958,0.000468492,9.89473e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012610804,0.0000062507665,0.025536977,0.0009595775,0.0001289836,0.000120517994,0.0014348726,0.879283,0.03625914,0.0006588066,0.00008240685,0.05551682],"study_design_scores_gemma":[0.00022420919,0.000017122311,0.2927842,0.00017360534,0.000009704989,0.00011551205,0.00008996321,0.7051582,0.000251919,0.0007969098,0.00014447601,0.00023420353],"about_ca_topic_score_codex":0.00000763761,"about_ca_topic_score_gemma":0.00008891694,"teacher_disagreement_score":0.26724723,"about_ca_system_score_codex":0.00007020421,"about_ca_system_score_gemma":0.0000052232413,"threshold_uncertainty_score":0.99998474},"labels":[],"label_agreement":null},{"id":"W4387408638","doi":"10.1016/j.inffus.2023.102046","title":"A multi-modal spatial–temporal model for accurate motion forecasting with visual fusion","year":2023,"lang":"en","type":"article","venue":"Information Fusion","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"King Saud University","keywords":"Computer science; Modal; Trajectory; Artificial intelligence; Displacement (psychology); Motion (physics); Feature (linguistics); Computer vision; Sensor fusion","score_opus":0.026698912770000267,"score_gpt":0.24367108151354583,"score_spread":0.21697216874354555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387408638","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":[0.38235486,0.0000025375489,0.6158769,0.000060403087,0.00009556804,0.00032603747,0.000021128044,0.0011082515,0.00015432299],"genre_scores_gemma":[0.9922542,0.000012043894,0.0070321197,0.00003779537,0.0000279589,0.00008693688,0.00047307432,0.000019746929,0.000056121953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918324,0.0000064874503,0.0003319442,0.00009306595,0.00013284403,0.00025241144],"domain_scores_gemma":[0.9996361,0.0000274665,0.00009372952,0.00011683348,0.00008401435,0.00004186329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022204794,0.00014662203,0.00012773486,0.00024075847,0.00022572414,0.0000321576,0.000091052105,0.00018146947,0.000010562703],"category_scores_gemma":[0.000031814132,0.00013087028,0.000040768133,0.00026556474,0.000022164122,0.0008972319,0.000051305353,0.00014430673,0.00009891732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009027889,0.000013130296,0.0003994573,0.000109169414,0.000011670381,0.000001094888,0.0014843643,0.75838935,0.001525613,0.00010967315,0.0002586186,0.23760758],"study_design_scores_gemma":[0.0009131431,0.00006460344,0.0017890234,0.000033515305,0.000007909007,0.0000052954974,0.00016947657,0.9949894,0.0012122118,0.000100502635,0.0005464801,0.00016843501],"about_ca_topic_score_codex":0.000022771284,"about_ca_topic_score_gemma":0.000066848595,"teacher_disagreement_score":0.60989934,"about_ca_system_score_codex":0.00006549917,"about_ca_system_score_gemma":0.000025583373,"threshold_uncertainty_score":0.5336735},"labels":[],"label_agreement":null},{"id":"W4387421427","doi":"10.1145/3594739.3612907","title":"SmoothLander: A Quadrotor Landing Control System with Smooth Trajectory Guarantee Based on Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Tsinghua Shenzhen International Graduate School","keywords":"Reinforcement learning; Trajectory; Computer science; Control theory (sociology); Control (management); Attitude control; Reinforcement; Control system; Control engineering; Aeronautics; Artificial intelligence; Engineering; Structural engineering; Physics","score_opus":0.0063108852980144625,"score_gpt":0.18613171216960375,"score_spread":0.1798208268715893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387421427","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":[0.40257812,0.000051779512,0.5221025,0.00027583766,0.0003086398,0.0011863768,0.0000066959615,0.017170675,0.056319393],"genre_scores_gemma":[0.9990664,0.0000056712515,0.000098495344,0.00006115167,0.000035326448,0.00012349969,0.000012589724,0.000047594334,0.0005492559],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899936,0.000029491313,0.00021833571,0.00019721399,0.00017317302,0.00038245157],"domain_scores_gemma":[0.9995617,0.0001173978,0.000035011864,0.0002292587,0.000015142713,0.000041469673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002668246,0.0002008464,0.0002528126,0.00023672974,0.00015403674,0.000018410545,0.00012905226,0.0001438704,0.00006517652],"category_scores_gemma":[0.000009500544,0.00015768128,0.000056481218,0.0002732511,0.000033014392,0.00004967392,0.000008949027,0.0003356989,0.0002305088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103766404,0.000006485793,0.0043535084,0.000105760046,0.000060710343,0.000053873297,0.000083706116,0.9916033,0.00042941445,0.0021532383,0.00018337028,0.0008628462],"study_design_scores_gemma":[0.0017917756,0.00024265071,0.0033551117,0.000118942386,0.000018055476,0.000004636291,0.00038552177,0.9918084,0.00057154265,0.0000037433424,0.0014673697,0.00023220942],"about_ca_topic_score_codex":0.00002417014,"about_ca_topic_score_gemma":0.00002582609,"teacher_disagreement_score":0.5964883,"about_ca_system_score_codex":0.0001550472,"about_ca_system_score_gemma":0.000024931373,"threshold_uncertainty_score":0.6430056},"labels":[],"label_agreement":null},{"id":"W4387623867","doi":"10.1109/tits.2023.3321309","title":"Toward Ensuring Safety for Autonomous Driving Perception: Standardization Progress, Research Advances, and Perspectives","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Standardization; Risk analysis (engineering); Perception; Systems engineering; Engineering; Computer science; Engineering ethics; Transport engineering; Business; Psychology; Neuroscience","score_opus":0.05234882342172713,"score_gpt":0.3049922760207986,"score_spread":0.25264345259907145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387623867","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":[0.071527444,0.0011577462,0.9233708,0.00016426534,0.00071086537,0.0010964045,0.00021139366,0.0016510113,0.00011011064],"genre_scores_gemma":[0.99428487,0.0040496713,0.0006840736,0.0000024298463,0.00006682001,0.0005945186,0.0000552964,0.00006208211,0.0002002685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830925,0.000058633,0.0005300196,0.00039702177,0.0003089506,0.00039614475],"domain_scores_gemma":[0.99924153,0.00018433049,0.000046989935,0.00020418108,0.00023673414,0.000086258355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067741296,0.00021365227,0.00026399564,0.0004964788,0.00049758423,0.000059027683,0.0001209807,0.00021830406,0.000029615889],"category_scores_gemma":[0.00000504408,0.00023212885,0.00009550852,0.00062382297,0.00017750013,0.000288189,4.3668823e-7,0.00036495435,0.000035224195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014767832,0.00006973175,0.00068040803,0.0008814445,0.00017023247,0.000009420528,0.013013116,0.8809747,0.0009180202,0.0024231651,0.00009097576,0.10062108],"study_design_scores_gemma":[0.0032027455,0.0012422543,0.02281389,0.0015227639,0.0002954501,0.0000622757,0.13094369,0.7029778,0.044046808,0.0010700079,0.08941382,0.00240855],"about_ca_topic_score_codex":0.000013876591,"about_ca_topic_score_gemma":0.00008341616,"teacher_disagreement_score":0.9227574,"about_ca_system_score_codex":0.00030608472,"about_ca_system_score_gemma":0.0000414247,"threshold_uncertainty_score":0.94659394},"labels":[],"label_agreement":null},{"id":"W4387693951","doi":"10.3390/vehicles5040078","title":"A Review of Deep Reinforcement Learning Algorithms for Mobile Robot Path Planning","year":2023,"lang":"en","type":"review","venue":"Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Reinforcement learning; Motion planning; Computer science; Mobile robot; Path (computing); Plan (archaeology); Artificial intelligence; Artificial neural network; Robot; Shortest path problem; Function (biology); Distributed computing; Human–computer interaction; Theoretical computer science; Computer network; Geography","score_opus":0.04751599244975764,"score_gpt":0.31945919341772816,"score_spread":0.27194320096797053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387693951","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.755227e-7,0.9813443,0.015981192,0.0000040631858,0.00014468741,0.0013484586,0.000016536222,0.00096618297,0.00019365158],"genre_scores_gemma":[0.000017728202,0.9969746,0.0012546744,0.000018017732,0.00008058034,0.0011542554,0.00020801325,0.00013497389,0.00015718935],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982452,0.00004212587,0.00092613057,0.0002796357,0.00012457217,0.00038234095],"domain_scores_gemma":[0.9989994,0.0002774962,0.00028345812,0.0003442114,0.00004494594,0.000050472016],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00049972814,0.00038319404,0.0016183404,0.00019416827,0.00008985967,0.000008473276,0.0003583273,0.00042420393,0.000024583791],"category_scores_gemma":[0.00007519115,0.0003466443,0.0004670664,0.00037306538,0.00004974397,0.00004833667,0.00010023806,0.0005648324,0.000068256646],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.457906e-7,0.0000045965285,5.017077e-7,0.19196154,0.00018684287,0.0000046533523,0.00003380839,0.011482363,8.0891533e-7,0.000061688916,0.0004530353,0.7958094],"study_design_scores_gemma":[0.00009300711,0.000085680746,4.232551e-7,0.11434931,0.0003735824,0.000009434687,0.000025574573,0.01904642,0.0000100346115,0.000032244847,0.86565197,0.0003223477],"about_ca_topic_score_codex":0.0000025791796,"about_ca_topic_score_gemma":3.4872153e-7,"teacher_disagreement_score":0.8651989,"about_ca_system_score_codex":0.000101203485,"about_ca_system_score_gemma":0.00005372137,"threshold_uncertainty_score":0.99989855},"labels":[],"label_agreement":null},{"id":"W4387870543","doi":"10.1109/icc45041.2023.10279653","title":"A Context-Aware Path Forecasting Method for Connected Autonomous Vehicles","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Canada Research Chairs","keywords":"Computer science; Context (archaeology); Reinforcement learning; Markov decision process; Process (computing); Kinematics; Path (computing); Partially observable Markov decision process; Task (project management); Artificial intelligence; Motion (physics); Markov process; State (computer science); Motion planning; Work (physics); Machine learning; Markov chain; Markov model; Robot; Engineering","score_opus":0.028867626311220973,"score_gpt":0.25478041838110876,"score_spread":0.2259127920698878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387870543","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":[0.2783868,0.00013818005,0.70190376,0.0007183957,0.0003810824,0.0007309276,0.00008849152,0.013713608,0.003938782],"genre_scores_gemma":[0.9815472,0.0000106107445,0.01726396,0.00012521388,0.000051202136,0.00017100792,0.000043825457,0.000058891306,0.00072812074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897075,0.000019029863,0.00027121563,0.00023119766,0.000059285,0.00044849905],"domain_scores_gemma":[0.99919057,0.00045833847,0.000031071326,0.00021413145,0.000047391386,0.000058506856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003349422,0.00018028935,0.00026052742,0.00014105094,0.00025531265,0.000013407813,0.00018134137,0.00023564798,0.000056688958],"category_scores_gemma":[0.000088079636,0.00017548123,0.00009518944,0.00033241493,0.00003523207,0.00008010974,0.00007347379,0.00018502178,0.000087197994],"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":[0.00005716658,0.000037488,0.00113708,0.0003016687,0.00036781238,0.000068599715,0.001667078,0.025650408,0.010278082,0.051494017,0.017371573,0.891569],"study_design_scores_gemma":[0.0005640078,0.000052599586,0.0006059558,0.000017564202,0.000018414783,0.000019473724,0.0005037727,0.96867234,0.011138563,0.0035573652,0.014599335,0.00025059484],"about_ca_topic_score_codex":0.00001862315,"about_ca_topic_score_gemma":0.000050859126,"teacher_disagreement_score":0.94302195,"about_ca_system_score_codex":0.000056695873,"about_ca_system_score_gemma":0.000026326288,"threshold_uncertainty_score":0.71559167},"labels":[],"label_agreement":null},{"id":"W4387946914","doi":"10.48550/arxiv.2310.15057","title":"Shareable Driving Style Learning and Analysis with a Hierarchical Latent Model","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut de Valorisation des Données; National Natural Science Foundation of China","keywords":"Latent Dirichlet allocation; Style (visual arts); Computer science; Driving factors; Mechanism (biology); Dimension (graph theory); Multilevel model; Feature (linguistics); Artificial intelligence; Machine learning; Topic model; China; Mathematics","score_opus":0.035964775269046095,"score_gpt":0.1668927280088548,"score_spread":0.1309279527398087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387946914","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":[0.7266462,0.000037517257,0.27110937,0.000027687582,0.000027780707,0.00010265527,0.000015054512,0.0013034235,0.0007303581],"genre_scores_gemma":[0.9959957,0.00037502582,0.00073026906,0.000004445189,0.0000114410705,0.0000017421323,0.000050360275,0.00004493686,0.0027860848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988751,0.000030427474,0.00013372657,0.0005873972,0.000048604277,0.0003247412],"domain_scores_gemma":[0.999405,0.000052464566,0.000056873923,0.00034740436,0.000032044976,0.0001061889],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013081825,0.00025857036,0.00039323562,0.00046668018,0.00015297293,0.000031228352,0.0002658233,0.00039544833,0.000027231492],"category_scores_gemma":[0.000010733893,0.0002845763,0.00012190837,0.0006590926,0.00011426332,0.00008392359,0.0005438123,0.0012050823,0.000023459485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013238975,0.000009963934,0.06879694,0.000050101793,0.0008051436,0.00013621434,0.000113577655,0.9270514,0.000014839637,0.002833063,0.000014966298,0.00016051107],"study_design_scores_gemma":[0.00019484767,0.000020528574,0.008432721,0.000047226382,0.00057405187,0.0000018491381,0.00006207559,0.98698175,0.000017529863,0.0033136532,0.000033670593,0.00032009717],"about_ca_topic_score_codex":0.000038043418,"about_ca_topic_score_gemma":0.00016444229,"teacher_disagreement_score":0.2703791,"about_ca_system_score_codex":0.00012675243,"about_ca_system_score_gemma":0.000038377355,"threshold_uncertainty_score":0.99996066},"labels":[],"label_agreement":null},{"id":"W4388100051","doi":"10.1145/3616392.3623419","title":"A Social-Aware Vehicle Path Forecasting Method using Graph Neural Networks","year":2023,"lang":"en","type":"article","venue":"","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":"University of Ottawa","funders":"","keywords":"Computer science; Graph; Path (computing); Kinematics; Artificial neural network; Motion (physics); Artificial intelligence; Theoretical computer science; Computer network","score_opus":0.04127831641494986,"score_gpt":0.27460055614493395,"score_spread":0.2333222397299841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388100051","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":[0.5184445,0.00006676337,0.47483245,0.00016619662,0.00021492774,0.0001255842,0.0000036055887,0.0047285324,0.0014174492],"genre_scores_gemma":[0.99565566,0.000008062264,0.0040324996,0.00006139474,0.000105719,0.000010563928,0.000008118078,0.00004636731,0.00007159535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905777,0.000030540432,0.00020246784,0.00017587327,0.000077127246,0.00045623607],"domain_scores_gemma":[0.9996957,0.00008169915,0.000026095597,0.00013547692,0.000019976202,0.00004102771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002652263,0.00015074052,0.0001879779,0.00012719132,0.00042557096,0.000014685442,0.00014740553,0.00022256335,0.000031367992],"category_scores_gemma":[0.000010339122,0.00015271106,0.00009017364,0.0007364375,0.000035857825,0.000102363585,0.00011658881,0.00032418457,0.000013489342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005064489,0.0000072027246,0.0020400367,0.000030695457,0.000062928804,0.000057820653,0.00039310878,0.9096796,0.00094960054,0.0016354378,0.00090625265,0.08423226],"study_design_scores_gemma":[0.00014946461,0.000009080794,0.0012523548,0.000006004115,0.000013827705,0.00001612418,0.0001645127,0.9966353,0.00033317096,0.001088377,0.00015390741,0.00017784344],"about_ca_topic_score_codex":0.000022429296,"about_ca_topic_score_gemma":0.000013606295,"teacher_disagreement_score":0.47721118,"about_ca_system_score_codex":0.00004086819,"about_ca_system_score_gemma":0.000007026113,"threshold_uncertainty_score":0.62273765},"labels":[],"label_agreement":null},{"id":"W4388106817","doi":"10.1145/3616392.3623418","title":"A Novel Multimodal Behavior Prediction Method for Automated Vehicles","year":2023,"lang":"en","type":"article","venue":"","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":"University of Ottawa","funders":"","keywords":"Computer science; GRASP; Artificial intelligence; Machine learning; Multimodality; Ranking (information retrieval); Anticipation (artificial intelligence)","score_opus":0.017549919451668068,"score_gpt":0.28028825456828244,"score_spread":0.2627383351166144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388106817","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":[0.30978626,0.000016597534,0.6479674,0.00018450008,0.00037867055,0.0005916888,0.00016578178,0.040007472,0.00090162887],"genre_scores_gemma":[0.8724091,0.000007055138,0.12653224,0.000023863387,0.00004074668,0.00041307748,0.00007030339,0.00004129674,0.00046233027],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941117,0.000005103396,0.00016172216,0.00014095836,0.000051336203,0.00022974149],"domain_scores_gemma":[0.99972075,0.00007178663,0.000012157195,0.00014179864,0.000021671598,0.00003185411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017551157,0.00009987977,0.00011535836,0.00011856404,0.00007751417,0.0000077713,0.00009489537,0.0002013617,0.000021476195],"category_scores_gemma":[0.000017709379,0.00009825396,0.000052145257,0.0002281137,0.000019341915,0.00006971198,0.0000227653,0.00010213401,0.000080570266],"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":[0.000032357922,0.0001344977,0.0028218166,0.00011021257,0.00017075939,0.000009463537,0.00035358302,0.15812896,0.61974853,0.0056809266,0.016065799,0.19674309],"study_design_scores_gemma":[0.00045262763,0.000026531452,0.038400955,0.0000035479459,0.000023027867,0.000007675365,0.00004633698,0.9450028,0.014200848,0.000090553694,0.0016452185,0.000099914614],"about_ca_topic_score_codex":0.000015579652,"about_ca_topic_score_gemma":0.00001077243,"teacher_disagreement_score":0.7868738,"about_ca_system_score_codex":0.000036622077,"about_ca_system_score_gemma":0.000008899156,"threshold_uncertainty_score":0.40066803},"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":"W4388212440","doi":"10.1109/issrew60843.2023.00052","title":"When Simulator Meets Natural Deviation: A Study on Deviations in Simulation-based ADS Testing","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"JST-Mirai Program; Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Computer science; Flexibility (engineering); Pipeline (software); Software deployment; Simulation; Reliability engineering; Engineering; Software engineering","score_opus":0.03105615114185753,"score_gpt":0.273592758316889,"score_spread":0.24253660717503148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388212440","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":[0.9906092,0.000018030223,0.004717661,0.00037975307,0.00013454922,0.00042932175,0.0000029424411,0.0027417883,0.0009667532],"genre_scores_gemma":[0.9991649,2.1619152e-7,0.0005456383,0.00009663003,0.000025616537,0.00004480531,0.000013349726,0.000024581534,0.000084310166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916935,0.000028718725,0.00027055206,0.00018001752,0.00012861915,0.00022272946],"domain_scores_gemma":[0.99866813,0.0010227496,0.000025068197,0.00021457401,0.000041184645,0.000028278468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021651531,0.00013370348,0.00014409208,0.00031175258,0.00009719838,0.0000152221055,0.00012093371,0.00010001584,0.000039382714],"category_scores_gemma":[0.00037704446,0.00013321414,0.000026802132,0.0008253273,0.000014016441,0.000091391885,0.000021325206,0.000228356,0.00020918131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003877256,0.00004044756,0.07206895,0.000006274179,0.000012933148,0.000007663987,0.00022421403,0.92479354,0.000056942197,0.00023827123,0.00004052206,0.002506331],"study_design_scores_gemma":[0.0004271018,0.00003758273,0.2334425,0.000011183429,0.0000049953164,9.129188e-8,0.00010302398,0.76540166,0.00009822122,0.00021643155,0.00014097641,0.000116244024],"about_ca_topic_score_codex":0.000009755897,"about_ca_topic_score_gemma":0.000117228454,"teacher_disagreement_score":0.16137354,"about_ca_system_score_codex":0.0001144593,"about_ca_system_score_gemma":0.00002869993,"threshold_uncertainty_score":0.5432315},"labels":[],"label_agreement":null},{"id":"W4388623088","doi":"10.1109/tse.2023.3331254","title":"Concretization of Abstract Traffic Scene Specifications Using Metaheuristic Search","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Software Engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Nemzeti Kutatási, Fejlesztési és Innovaciós Alap; Natural Sciences and Engineering Research Council of Canada; National Research, Development and Innovation Office; Innovációs és Technológiai Minisztérium; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal","keywords":"Computer science; Context (archaeology); Scalability; Constraint (computer-aided design); Metaheuristic; Set (abstract data type); Real-time computing; Artificial intelligence; Programming language; Database","score_opus":0.04044585752119628,"score_gpt":0.23861477820355717,"score_spread":0.1981689206823609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388623088","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":[0.35454667,0.000047254536,0.6430252,0.000008039361,0.00028733347,0.000116722316,0.000059229435,0.001892046,0.000017458833],"genre_scores_gemma":[0.9916367,0.000095272095,0.008107599,0.0000023522334,0.00002609789,0.000020935331,0.000013160996,0.00006578831,0.000032076307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990639,0.000008542651,0.00032080128,0.00017621176,0.00015508648,0.00027544625],"domain_scores_gemma":[0.99941695,0.00017453874,0.00002451694,0.00027495832,0.000048163685,0.00006089295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014758663,0.00017333656,0.00021454514,0.00047113476,0.00009632416,0.000010653099,0.0001474683,0.00015971115,0.000095688534],"category_scores_gemma":[0.000016348386,0.00020777796,0.00009380304,0.00087459385,0.00005052713,0.00010770572,0.0000010995126,0.00035404574,0.0000954294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051681855,0.000015156133,0.000015329157,0.00008763666,0.00006532957,0.000004510233,0.000101742364,0.9807527,0.011156107,0.000039484235,0.000017752825,0.0077390615],"study_design_scores_gemma":[0.00019806025,0.000021209815,0.0023851173,0.000062638064,0.000044384327,0.000009772158,0.00004426022,0.9247775,0.072076574,0.000005344771,0.00015368727,0.00022142916],"about_ca_topic_score_codex":0.000004963649,"about_ca_topic_score_gemma":0.0000021599756,"teacher_disagreement_score":0.63709,"about_ca_system_score_codex":0.000102457394,"about_ca_system_score_gemma":0.000033065364,"threshold_uncertainty_score":0.847294},"labels":[],"label_agreement":null},{"id":"W4388666767","doi":"10.1109/dsa59317.2023.00089","title":"Prediction of Autonomous Vehicle Trajectories in Turnaround Scenarios","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Ontario Tech University","funders":"","keywords":"Computer science; Trajectory; Workload; Context (archaeology); Smoothing; Artificial neural network; Graph; Simple (philosophy); Real-time computing; Artificial intelligence; Distributed computing; Theoretical computer science","score_opus":0.015432962717315184,"score_gpt":0.20634913407914435,"score_spread":0.19091617136182917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388666767","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":[0.99353695,0.00005482439,0.0004797867,0.00008659912,0.00019129195,0.00008974197,0.00001012434,0.0016629489,0.00388775],"genre_scores_gemma":[0.9994786,0.00004534296,0.00011971104,0.0000063755188,0.000018628727,0.000012870875,0.000007013763,0.000014420795,0.000297082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9994378,0.000007886474,0.00021692901,0.00009665123,0.000059014863,0.00018168607],"domain_scores_gemma":[0.99977326,0.00003797079,0.000014401243,0.00014412195,0.00001058729,0.00001966955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012659597,0.00007774829,0.00013112827,0.000175562,0.000023679075,0.0000037751597,0.00009464211,0.00013803387,0.000049077535],"category_scores_gemma":[0.000015196835,0.00007860546,0.000027704324,0.0004603384,0.00004775737,0.000099852004,0.00001995164,0.00015711105,0.000055749788],"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":[0.00005099714,0.00014730322,0.33298624,0.00035511507,0.00016733368,0.0000754094,0.0045142374,0.46253654,0.1092632,0.019623809,0.0030547814,0.06722504],"study_design_scores_gemma":[0.0005120198,0.000066217326,0.5460271,0.000022280383,0.000007294858,0.0000045948036,0.00039195557,0.42087153,0.02844927,0.0018535384,0.001642253,0.0001519632],"about_ca_topic_score_codex":0.000040837655,"about_ca_topic_score_gemma":0.00007006853,"teacher_disagreement_score":0.21304087,"about_ca_system_score_codex":0.000059169066,"about_ca_system_score_gemma":0.000016554746,"threshold_uncertainty_score":0.32054377},"labels":[],"label_agreement":null},{"id":"W4388675523","doi":"10.1109/dsa59317.2023.00090","title":"Towards a Benchmark for Trajectory Prediction of Autonomous Vehicles","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Ontario Tech University","funders":"","keywords":"Benchmarking; Computer science; Benchmark (surveying); Trajectory; Task (project management); Process (computing); Machine learning; Artificial intelligence; Motion (physics); Data mining; Software; Systems engineering; Engineering","score_opus":0.014642774017430709,"score_gpt":0.21709851505431732,"score_spread":0.2024557410368866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388675523","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":[0.9566256,0.00013996504,0.021432923,0.00017515788,0.0004058525,0.00035413518,0.00013038922,0.004468096,0.016267894],"genre_scores_gemma":[0.9978936,0.000042670697,0.0015416802,0.000010540028,0.00003508253,0.00006555983,0.000030269854,0.000019473413,0.00036115028],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994642,0.0000047298226,0.00019312817,0.00010479871,0.000052429525,0.0001806998],"domain_scores_gemma":[0.9997502,0.000043271706,0.000016829868,0.00014476424,0.000020453768,0.000024510724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014849925,0.00008325658,0.00013526545,0.00012777364,0.00003834754,0.0000026916898,0.000101894446,0.00014903636,0.000050992232],"category_scores_gemma":[0.000015038149,0.000082456114,0.00006585469,0.0002035795,0.000043329463,0.000054638185,0.000016283586,0.000080285055,0.000021646369],"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":[0.00010563093,0.00014598016,0.008545302,0.0010479408,0.0006301601,0.000011936128,0.0022607935,0.083492436,0.14075004,0.056080528,0.064834654,0.6420946],"study_design_scores_gemma":[0.0010271592,0.00026807332,0.14995615,0.000028906727,0.00005081839,0.000007183779,0.00037112957,0.6793372,0.13395391,0.007838222,0.026847322,0.00031392067],"about_ca_topic_score_codex":0.0000067773562,"about_ca_topic_score_gemma":0.00000642869,"teacher_disagreement_score":0.6417807,"about_ca_system_score_codex":0.000035518824,"about_ca_system_score_gemma":0.000024200139,"threshold_uncertainty_score":0.33624628},"labels":[],"label_agreement":null},{"id":"W4388720154","doi":"10.1109/iecon51785.2023.10311652","title":"Learning Adaptive Cruise Control for Autonomous Vehicles Using End-to-End Deep Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"","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":"York University","funders":"","keywords":"Reinforcement learning; Computer science; End-to-end principle; Partially observable Markov decision process; Cruise control; Markov decision process; Process (computing); Artificial intelligence; Task (project management); Cruise; Control (management); Real-time computing; Simulation; Machine learning; Markov process; Markov model; Markov chain; Engineering","score_opus":0.017217901682792715,"score_gpt":0.2360299339878158,"score_spread":0.21881203230502308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388720154","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":[0.27180386,0.00009865313,0.717414,0.00021379384,0.00023063968,0.00078874954,0.0000038151065,0.0050075664,0.004438903],"genre_scores_gemma":[0.9940217,0.000021810338,0.003757279,0.00009325307,0.00008723805,0.00014058172,0.000019300629,0.00008376571,0.001775063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983657,0.000037445127,0.000398107,0.0003259947,0.0001488279,0.0007239524],"domain_scores_gemma":[0.999272,0.0002717978,0.00006267493,0.00020394498,0.000063049665,0.00012654929],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044777823,0.00027685327,0.00035354763,0.00031956364,0.00042787465,0.000036094807,0.00021278817,0.00023775543,0.00016518346],"category_scores_gemma":[0.0001147281,0.00029409706,0.00012737974,0.00039219583,0.00006047251,0.00015068568,0.00008687029,0.0005116776,0.00027392263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000506014,0.0000056040562,0.00046836812,0.000023624045,0.00012043097,0.000010941095,0.0005845518,0.9539339,0.006587806,0.0028167975,0.00008440863,0.035313],"study_design_scores_gemma":[0.0008699721,0.0002555163,0.00076602836,0.000023182365,0.00004534211,0.0000062903314,0.0009941289,0.984828,0.0043349573,0.00024302151,0.007272238,0.00036134064],"about_ca_topic_score_codex":0.00003797094,"about_ca_topic_score_gemma":0.000022525253,"teacher_disagreement_score":0.72221786,"about_ca_system_score_codex":0.00023584066,"about_ca_system_score_gemma":0.00004291078,"threshold_uncertainty_score":0.9999511},"labels":[],"label_agreement":null},{"id":"W4388858142","doi":"10.1063/5.0151368","title":"Advanced drive assistant system for accident avoidance at speed limit zone using GPS technology","year":2023,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Global Positioning System; Speed limit; Limit (mathematics); Computer science; Aeronautics; Automotive engineering; Real-time computing; Engineering; Transport engineering; Telecommunications; Mathematics","score_opus":0.02037431488736658,"score_gpt":0.24035939698604417,"score_spread":0.2199850820986776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388858142","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":[0.9717368,0.00016851503,0.020640556,0.00052221876,0.00032056958,0.0006389518,0.000016704022,0.0048975507,0.0010581216],"genre_scores_gemma":[0.9961619,0.00009300912,0.0030446246,0.000019004598,0.000049695416,0.00015254092,0.00000931683,0.0000677439,0.00040219186],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825627,0.000003248381,0.0004154119,0.0004717037,0.0001558456,0.0006975508],"domain_scores_gemma":[0.9992559,0.00005015293,0.00013540313,0.00021470794,0.0002620948,0.000081732214],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022160781,0.00030878762,0.0004365498,0.0003623686,0.0002988992,0.00004493703,0.00046739503,0.00043741957,0.000014202275],"category_scores_gemma":[0.00008405164,0.00032968647,0.00008090729,0.0007912846,0.00014753765,0.00024874564,0.0002099875,0.0003481241,0.00014008157],"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":[0.0001233344,0.000028100656,0.032379076,0.0007513283,0.00018178095,0.00003186854,0.0011690682,0.001021507,0.9086531,0.040834494,0.0005601875,0.014266184],"study_design_scores_gemma":[0.0016835092,0.00024047427,0.009071082,0.0006857299,0.000114688475,0.00013425082,0.006176567,0.7482151,0.22375093,0.0028428743,0.0059979544,0.0010868625],"about_ca_topic_score_codex":0.0000044524454,"about_ca_topic_score_gemma":0.000015167979,"teacher_disagreement_score":0.7471936,"about_ca_system_score_codex":0.0005379674,"about_ca_system_score_gemma":0.00005281836,"threshold_uncertainty_score":0.99991554},"labels":[],"label_agreement":null},{"id":"W4389104180","doi":"10.2139/ssrn.4646895","title":"Vehicle Group Identification and Evolutionary Analysis Using Vehicle Trajectory Data","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Trajectory; Identification (biology); Group (periodic table); Computer science; Biology; Physics","score_opus":0.02490745859707374,"score_gpt":0.2562359709545379,"score_spread":0.23132851235746413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389104180","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":[0.8726364,0.0136224255,0.11203629,0.00020676693,0.00046517994,0.00017321201,0.00009814061,0.00073190907,0.000029683402],"genre_scores_gemma":[0.9894721,0.00940237,0.0004108168,0.000007210913,0.0002149253,0.000007652861,0.00026711848,0.00006817555,0.0001496253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99728525,0.00008615572,0.0005404685,0.00051701633,0.00022905429,0.0013420372],"domain_scores_gemma":[0.99875534,0.00005106083,0.00018966204,0.00088442763,0.00004741753,0.00007208468],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0017596976,0.00027523775,0.00039613934,0.0005815025,0.00029917285,0.00007412436,0.0008513105,0.00048608202,0.000010954943],"category_scores_gemma":[0.000034073888,0.0003118269,0.00014715483,0.0005342177,0.000100371035,0.0002753883,0.0005437217,0.004162275,0.000021362257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001126128,0.0002298498,0.05137616,0.00040809257,0.022711532,0.00007485523,0.00070416863,0.8000911,0.019190071,0.02037898,0.0006966135,0.08402598],"study_design_scores_gemma":[0.0002721582,0.000027613773,0.053258717,0.000035013247,0.0013055501,0.00010529017,0.00046824286,0.8499497,0.000053688833,0.093959585,0.00012528631,0.00043915733],"about_ca_topic_score_codex":0.00010882874,"about_ca_topic_score_gemma":0.0009117129,"teacher_disagreement_score":0.116835706,"about_ca_system_score_codex":0.0012813043,"about_ca_system_score_gemma":0.0005774918,"threshold_uncertainty_score":0.99993336},"labels":[],"label_agreement":null},{"id":"W4389302505","doi":"10.1109/cog57401.2023.10333163","title":"Efficient Ground Vehicle Path Following in Game AI","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Computer science; Path (computing); Unmanned ground vehicle; Focus (optics); Mathematical optimization; Benchmark (surveying); Quadratic equation; Curvature; Motion planning; Simulation; Robot; Algorithm; Artificial intelligence; Mathematics; Computer network","score_opus":0.006815858273737283,"score_gpt":0.21487095982074758,"score_spread":0.2080551015470103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389302505","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":[0.99068487,0.000063731226,0.0016895017,0.00024675514,0.00019893608,0.00007973241,9.3633554e-7,0.0022168388,0.0048186947],"genre_scores_gemma":[0.99951243,0.0000076165156,0.00006781443,0.00007514932,0.000012717848,0.000014675009,0.0000026477796,0.00001940891,0.00028752664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993612,0.000007752484,0.00014634384,0.00012591193,0.000070075774,0.00028868747],"domain_scores_gemma":[0.9997702,0.000031904485,0.000005583347,0.00016111198,0.000003701434,0.000027489408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016282585,0.0000871615,0.000115534946,0.00013584016,0.000029536635,0.000008731651,0.000107335516,0.00011335825,0.00003826039],"category_scores_gemma":[0.000009896463,0.000086594824,0.00004798318,0.00047804666,0.000016359487,0.000030321145,0.00004064824,0.00019921793,0.00059081375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009220446,0.00006958603,0.03750126,0.000052969957,0.00006856127,0.0004302138,0.0012722403,0.90569264,0.012511577,0.015891533,0.0012262168,0.025273992],"study_design_scores_gemma":[0.0003356929,0.000012372195,0.08230117,0.000013286228,0.000003273632,0.0000018079503,0.00015719363,0.9140891,0.0011476962,0.00078283873,0.0010036835,0.00015189844],"about_ca_topic_score_codex":0.000020730775,"about_ca_topic_score_gemma":0.000022708136,"teacher_disagreement_score":0.04479991,"about_ca_system_score_codex":0.000061581115,"about_ca_system_score_gemma":0.000007688815,"threshold_uncertainty_score":0.7593913},"labels":[],"label_agreement":null},{"id":"W4389356387","doi":"10.5194/isprs-annals-x-1-w1-2023-649-2023","title":"HYBRID DEEP LEARNING APPROACH FOR VEHICLE’S RELATIVE ATTITUDE ESTIMATION USING MONOCULAR CAMERA","year":2023,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Artificial intelligence; Computer vision; Computer science; Monocular; Orientation (vector space); Frame (networking); Feature (linguistics); Monocular vision; Artificial neural network; Matching (statistics); Mathematics","score_opus":0.03946208926567571,"score_gpt":0.288491572472207,"score_spread":0.24902948320653132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389356387","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":[0.23344089,0.00005443192,0.7655087,0.00016427683,0.00011246464,0.00022628995,0.000004945667,0.00019264827,0.00029531485],"genre_scores_gemma":[0.98533285,0.0000661448,0.014490619,0.00006475152,0.000015882619,5.2059045e-7,0.000013083718,0.0000084550375,0.00000769518],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989869,0.0000425119,0.0003567987,0.000124671,0.00021252528,0.00027659157],"domain_scores_gemma":[0.9994319,0.0000979659,0.0001819146,0.0001365309,0.00011123725,0.000040431114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008853886,0.00013266165,0.00018894089,0.00026378676,0.00055715046,0.00007881424,0.00015727346,0.00009036942,7.389606e-7],"category_scores_gemma":[0.0002486452,0.000107298314,0.0000894791,0.00060732145,0.0002900136,0.00035647428,0.00006244104,0.00018874147,0.0000021691828],"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":[0.00000523061,0.00000148842,0.00005548364,0.000031901454,0.000017340977,9.257779e-8,0.00040350494,0.39874303,0.00040337138,0.000010918731,0.00001480429,0.6003128],"study_design_scores_gemma":[0.00012657412,0.000043493685,0.0005889237,0.00003524452,0.000014277908,0.000008163876,0.0003210383,0.9826524,0.014544218,0.001314887,0.00022385757,0.00012689113],"about_ca_topic_score_codex":0.006106096,"about_ca_topic_score_gemma":0.00014576147,"teacher_disagreement_score":0.751892,"about_ca_system_score_codex":0.00001802916,"about_ca_system_score_gemma":0.000022351618,"threshold_uncertainty_score":0.9230631},"labels":[],"label_agreement":null},{"id":"W4389356553","doi":"10.5194/isprs-annals-x-1-w1-2023-635-2023","title":"VANISHING POINT AIDED LANE DETECTION USING A MULTI-SENSOR SYSTEM","year":2023,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"RANSAC; Artificial intelligence; Computer science; Hough transform; Computer vision; Benchmark (surveying); Outlier; Lidar; Vanishing point; Point (geometry); Point cloud; Suite; Line (geometry); Image (mathematics); Remote sensing; Geography; Mathematics","score_opus":0.05312428250316076,"score_gpt":0.28342024043065667,"score_spread":0.2302959579274959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389356553","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":[0.5430996,0.000021904585,0.45561355,0.00015400824,0.0003716619,0.00013769687,0.0000062967615,0.00038396457,0.00021131725],"genre_scores_gemma":[0.99824995,0.000033226308,0.0016144948,0.000066482455,0.000021813257,1.2117853e-7,0.00000169301,0.000007018142,0.0000052257537],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989683,0.000045060813,0.0003866713,0.00010595257,0.00022304276,0.00027095026],"domain_scores_gemma":[0.99947184,0.00006412005,0.00016370381,0.00016885345,0.00008698871,0.000044470748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008893912,0.00012504865,0.00018181544,0.0003012636,0.00043209305,0.00009697394,0.0001521625,0.00011557739,7.930754e-7],"category_scores_gemma":[0.00014465187,0.00009607464,0.000072264724,0.00089418725,0.00020377606,0.00025924604,0.00007041039,0.00016398945,0.000007035174],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008545074,0.000002088706,0.00010038734,0.00009354644,0.000020489924,9.038721e-7,0.00082817895,0.029123176,0.009231299,0.0000050047797,0.000015914742,0.96057045],"study_design_scores_gemma":[0.00011439032,0.000025896567,0.0012361739,0.00009142782,0.000007789621,0.000035241224,0.0018682774,0.92981803,0.06646679,0.00007655227,0.00015263387,0.00010680757],"about_ca_topic_score_codex":0.033346333,"about_ca_topic_score_gemma":0.003188335,"teacher_disagreement_score":0.96046364,"about_ca_system_score_codex":0.000024171,"about_ca_system_score_gemma":0.000018888808,"threshold_uncertainty_score":0.9730907},"labels":[],"label_agreement":null},{"id":"W4389544369","doi":"10.1109/vtc2023-fall60731.2023.10333777","title":"A Real-time Vehicle–Pedestrian Collision Avoidance System Exploiting Lightweight Smartphone App","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Western University","funders":"","keywords":"Pedestrian; Collision avoidance; Smartphone app; Computer science; Mobile apps; Smartphone application; Collision; Real-time computing; Embedded system; Human–computer interaction; Computer security; Multimedia; Engineering; Transport engineering; World Wide Web","score_opus":0.007403945398373674,"score_gpt":0.19353828372186965,"score_spread":0.186134338323496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389544369","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":[0.9475505,0.00007506322,0.0013499073,0.00019398895,0.00030069338,0.00025428963,0.000011408414,0.01572689,0.034537304],"genre_scores_gemma":[0.9954141,0.00021567268,0.00096259697,0.000011706328,0.00012034728,0.00006824784,0.000023324566,0.00007006457,0.003113935],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985803,0.000033489392,0.00039881654,0.00030413343,0.0001663966,0.0005168905],"domain_scores_gemma":[0.99925905,0.00013119135,0.0000478832,0.0004311529,0.000030860876,0.00009986928],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003485757,0.00023091953,0.00032184226,0.00020471822,0.0002229634,0.000030973788,0.0002725117,0.00029249582,0.000068373316],"category_scores_gemma":[0.000022678825,0.00022236115,0.000078713776,0.00082627323,0.00004603453,0.0001629516,0.00009048739,0.0002668301,0.004397585],"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":[0.00020718171,0.00017196141,0.0056532803,0.0023749669,0.0006540743,0.0016340923,0.0041372026,0.05671308,0.71327734,0.05796237,0.091257654,0.06595677],"study_design_scores_gemma":[0.001373166,0.0000914689,0.010226093,0.00034708067,0.00003952176,0.00006356686,0.0011055619,0.8356155,0.14154221,0.00034169832,0.008325205,0.0009289216],"about_ca_topic_score_codex":0.00003783623,"about_ca_topic_score_gemma":0.000013512736,"teacher_disagreement_score":0.7789024,"about_ca_system_score_codex":0.00017186576,"about_ca_system_score_gemma":0.000024679557,"threshold_uncertainty_score":0.9963776},"labels":[],"label_agreement":null},{"id":"W4389545453","doi":"10.1109/vtc2023-fall60731.2023.10333837","title":"Vehicular Multimodal Motion Forecasting via Conditional Score-based Modeling","year":2023,"lang":"en","type":"article","venue":"","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":"Queen's University","funders":"Technology Development; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Machine learning; Trajectory; Probabilistic logic","score_opus":0.026360723265308268,"score_gpt":0.2099982462000688,"score_spread":0.18363752293476052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389545453","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":[0.46085745,0.00000821817,0.53631425,0.00007571137,0.00006914378,0.00006680419,0.0000053109125,0.0020128023,0.0005902944],"genre_scores_gemma":[0.99659723,0.000001909288,0.0030868617,0.00004294424,0.000042542742,0.000026168294,0.00014503178,0.00002582785,0.00003146556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993268,0.000008208369,0.0001725049,0.00014492522,0.0000998351,0.0002477251],"domain_scores_gemma":[0.99976194,0.00003449186,0.000012805978,0.00012571864,0.000028066956,0.000036997433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014168229,0.00011147577,0.000104694096,0.0001404692,0.00011587395,0.000008966597,0.00008693172,0.00015240235,0.00009465971],"category_scores_gemma":[0.000016451697,0.000116384326,0.000054443346,0.00022793276,0.000028355864,0.0000923551,0.000020070263,0.0001813786,0.00023957694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024418334,0.0000061034925,0.00072958914,0.000017269012,0.000014246476,0.000012390875,0.000013000633,0.98986167,0.0018032901,0.00089570554,0.000052579508,0.0065917275],"study_design_scores_gemma":[0.000276887,0.000009059717,0.0010084177,0.000010116056,0.000006172801,0.0000055834976,0.000015685604,0.9915898,0.0038825604,0.0030321097,0.0000297604,0.0001338473],"about_ca_topic_score_codex":0.000010186616,"about_ca_topic_score_gemma":0.000009174701,"teacher_disagreement_score":0.5357398,"about_ca_system_score_codex":0.00005253341,"about_ca_system_score_gemma":0.000011345948,"threshold_uncertainty_score":0.4746015},"labels":[],"label_agreement":null},{"id":"W4389579861","doi":"10.1155/2023/5583901","title":"Automated Lane-Level Road Geometry Estimation Using Microscopic Trajectory Data","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Calibration; Tracking (education); Process (computing); Radar; Lidar; Cluster analysis; Computer vision; Artificial intelligence; Remote sensing; Mathematics; Geography","score_opus":0.03135638838417788,"score_gpt":0.28922120953243,"score_spread":0.25786482114825215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389579861","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":[0.95613056,0.00022065303,0.04232087,0.00003195231,0.00043386963,0.00006944007,0.00007723146,0.000705508,0.000009933831],"genre_scores_gemma":[0.9625617,0.00013544198,0.036998108,0.000007922267,0.000035607274,8.8176193e-7,0.00022918233,0.000023423987,0.000007697656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991508,0.000010198121,0.00044625002,0.00010046318,0.0001359713,0.00015627607],"domain_scores_gemma":[0.9995478,0.000028197443,0.00015048495,0.00018507481,0.0000513622,0.000037114813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002002891,0.00010484942,0.00018678061,0.00029484773,0.00005463194,0.000007883328,0.00020320591,0.00011198879,0.000010386907],"category_scores_gemma":[0.000019230423,0.00010826413,0.000037758597,0.00045481042,0.000024364861,0.00057364977,0.00000310122,0.00022708619,0.000010548368],"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":[0.000018923269,0.000011858843,0.00083582633,0.00006597735,0.00004750916,0.00004005069,0.00026100996,0.91415554,0.059998196,0.000017839591,0.00008865709,0.0244586],"study_design_scores_gemma":[0.00077735435,0.00004568797,0.53255296,0.000105277846,0.00006955037,0.000028625915,0.00021279167,0.4585359,0.007106436,0.0002071867,0.00019858347,0.0001596277],"about_ca_topic_score_codex":0.00000396555,"about_ca_topic_score_gemma":0.000019101766,"teacher_disagreement_score":0.5317172,"about_ca_system_score_codex":0.000060888076,"about_ca_system_score_gemma":0.00004016285,"threshold_uncertainty_score":0.44148833},"labels":[],"label_agreement":null},{"id":"W4389667163","doi":"10.1109/iros55552.2023.10342534","title":"Does Unpredictability Influence Driving Behavior?","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Feature (linguistics); Entropy (arrow of time); Artificial intelligence; Machine learning; Reinforcement learning","score_opus":0.0045956962336224805,"score_gpt":0.2079326536371045,"score_spread":0.203336957403482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389667163","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":[0.9893762,0.0000055318906,0.0005261708,0.000120091,0.00018471871,0.00008257939,0.000003121232,0.006668645,0.003032949],"genre_scores_gemma":[0.99936926,0.000021408265,0.0002016697,0.00001530957,0.00001480813,0.000036216545,0.0000028473412,0.000012658957,0.00032579433],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994731,0.0000057724096,0.00012997621,0.00012198776,0.000058988513,0.00021018571],"domain_scores_gemma":[0.99966145,0.000038707345,0.0000067686146,0.0002465376,0.000011709481,0.000034849094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012104484,0.00007876261,0.00008430873,0.000058908005,0.000057939684,0.0000072261887,0.00014376265,0.000113649214,0.0001322566],"category_scores_gemma":[0.000024078437,0.00005299518,0.000026963322,0.0002515781,0.00006339125,0.00010199109,0.00005565051,0.00016050845,0.00034600592],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010962558,0.000016359922,0.94277394,0.000028385295,0.000018160084,0.000024814875,0.00019632024,0.010552169,0.012297774,0.002274207,0.0005689938,0.031247783],"study_design_scores_gemma":[0.00006668401,0.0000067624014,0.9712309,0.000004953698,0.000006459688,0.0000016611709,0.00008199073,0.018199509,0.007765417,0.0010831543,0.001427142,0.00012536866],"about_ca_topic_score_codex":0.000009983719,"about_ca_topic_score_gemma":0.000035652934,"teacher_disagreement_score":0.031122414,"about_ca_system_score_codex":0.0000312673,"about_ca_system_score_gemma":0.0000064339088,"threshold_uncertainty_score":0.44473216},"labels":[],"label_agreement":null},{"id":"W4389667925","doi":"10.1155/2023/9768517","title":"Retracted: Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Point cloud; Computer science; Series (stratigraphy); Cloud computing; Time series; Point (geometry); State (computer science); Motion (physics); Artificial intelligence; Computer vision; Algorithm; Machine learning; Geology; Mathematics","score_opus":0.0033847103384663474,"score_gpt":0.22508700972324955,"score_spread":0.2217022993847832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389667925","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":[0.8722033,0.00013078777,0.12627086,0.00024791123,0.00046672497,0.00018639916,0.00007489363,0.00037309737,0.000046041627],"genre_scores_gemma":[0.96886396,0.0005378902,0.030356787,0.000014965137,0.000050533123,0.00000678084,0.0001334048,0.000016194923,0.00001950208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99909955,0.00003404366,0.00052319095,0.000074537864,0.000138237,0.0001304274],"domain_scores_gemma":[0.9993994,0.000078958816,0.00025489138,0.00006030199,0.00016532531,0.000041071413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037090128,0.00012550483,0.00019943094,0.00023829714,0.00007224651,0.000018605895,0.000051784104,0.00012310162,0.000007424379],"category_scores_gemma":[0.00006748172,0.00011650178,0.000054274773,0.00026027818,0.000023871391,0.002679973,0.0000024755243,0.00050693634,0.000007335138],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044862204,0.000030084318,0.001394222,0.00021852645,0.00017050336,0.000023388395,0.018963374,0.55865765,0.07021127,0.00021063529,0.000267226,0.3494045],"study_design_scores_gemma":[0.0018435505,0.00042041572,0.85884005,0.00039328402,0.00009955026,0.00009223277,0.00535851,0.08261038,0.045295816,0.0034009495,0.0013301284,0.0003151107],"about_ca_topic_score_codex":0.000004540697,"about_ca_topic_score_gemma":0.000022657297,"teacher_disagreement_score":0.85744584,"about_ca_system_score_codex":0.000073403105,"about_ca_system_score_gemma":0.000012257631,"threshold_uncertainty_score":0.47508046},"labels":[],"label_agreement":null},{"id":"W4390197266","doi":"10.18280/ijsse.130601","title":"Cost Effective Analysis of the Design of Safety Instrumented Systems Using Manta-Ray Foraging Optimization Algorithm","year":2023,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Foraging; Optimization algorithm; Computer science; Algorithm; Engineering; Mathematical optimization; Mathematics; Ecology; Biology","score_opus":0.009240168538549845,"score_gpt":0.2247290427198879,"score_spread":0.21548887418133805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390197266","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":[0.09705079,0.00026609012,0.9015835,0.000044674467,0.00065645797,0.00022686161,0.000095329764,0.00005944513,0.000016825012],"genre_scores_gemma":[0.99462974,0.0006279266,0.0046617,0.0000034987966,0.00004352568,0.0000031555435,0.000012469582,0.00001608018,0.0000019087918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879104,0.00004529758,0.0006548801,0.000089992085,0.0002774934,0.00014132602],"domain_scores_gemma":[0.9991722,0.00020765125,0.0002676551,0.0001049151,0.00021104737,0.0000365036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062970934,0.00013571445,0.0003861292,0.0006294787,0.000048588114,0.000014233824,0.0002729986,0.00011039141,0.0000057756433],"category_scores_gemma":[0.000062566374,0.00011705457,0.00014848064,0.0007636309,0.000049615774,0.0002036579,0.00007385996,0.00026613075,1.8784546e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005379774,0.000011723998,0.0007584794,0.000043025855,0.0016707915,0.000008501533,0.000529529,0.99130386,0.0011735581,0.00030487063,0.0000014706008,0.0041403784],"study_design_scores_gemma":[0.000456881,0.000021272946,0.0060272915,0.00020035313,0.00025572008,0.000039148395,0.00019826062,0.9911241,0.0014781238,0.000023346485,0.000079798374,0.00009569162],"about_ca_topic_score_codex":0.000013100235,"about_ca_topic_score_gemma":0.0000011371011,"teacher_disagreement_score":0.89757895,"about_ca_system_score_codex":0.0001688133,"about_ca_system_score_gemma":0.000023046614,"threshold_uncertainty_score":0.4773347},"labels":[],"label_agreement":null},{"id":"W4390224779","doi":"10.1109/dtpi59677.2023.10365324","title":"Digital Twin-Driven Asymmetric Driving Aggressiveness Assessment for Connected Autonomous Vehicles","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Perspective (graphical); Hazard; Collision avoidance; Driving simulator; Collision; Simulation; Focus (optics); Driving simulation; Computer security; Artificial intelligence","score_opus":0.010620042986804927,"score_gpt":0.24627334236735293,"score_spread":0.235653299380548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390224779","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":[0.8825845,0.00014919043,0.08877196,0.00043006503,0.00059004175,0.0006294937,0.000078691344,0.009913142,0.016852908],"genre_scores_gemma":[0.9967123,0.000030915573,0.0020620525,0.000025795614,0.00006181397,0.00014860065,0.00009502234,0.000064303094,0.00079919444],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878097,0.0000110405135,0.0003022363,0.0002813216,0.00012206701,0.0005023963],"domain_scores_gemma":[0.9991481,0.00037786816,0.00004931474,0.0002866004,0.000056922294,0.00008120993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013953546,0.00022035153,0.00028441602,0.00041026375,0.00016421384,0.00007547722,0.00029311344,0.00022610924,0.00003257159],"category_scores_gemma":[0.00006896055,0.000218748,0.00011288528,0.0008542381,0.00006068745,0.00023726591,0.00010192575,0.00021209178,0.00015778496],"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":[0.000018930614,0.00011452594,0.10157191,0.00021589771,0.0005419181,0.00010130761,0.00017757,0.059704177,0.0037211084,0.027634887,0.006885871,0.7993119],"study_design_scores_gemma":[0.001051952,0.00010749621,0.14830263,0.000049617673,0.000034429588,0.000014585609,0.00021281543,0.82791144,0.00598168,0.0017081565,0.014024621,0.00060060987],"about_ca_topic_score_codex":0.0000014085653,"about_ca_topic_score_gemma":0.000005514287,"teacher_disagreement_score":0.7987113,"about_ca_system_score_codex":0.00014929203,"about_ca_system_score_gemma":0.000050774885,"threshold_uncertainty_score":0.89202845},"labels":[],"label_agreement":null},{"id":"W4390429597","doi":"10.1016/j.aap.2023.107445","title":"How drivers perform under different scenarios: Ability-related driving style extraction for large-scale dataset","year":2023,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality; Ministry of Science and Technology of the People's Republic of China","keywords":"Intersection (aeronautics); Driving test; Computer science; Driving simulator; Poison control; Engineering; Simulation; Machine learning; Transport engineering","score_opus":0.010273822518888727,"score_gpt":0.2587898035496801,"score_spread":0.2485159810307914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390429597","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":[0.89821714,0.000052344683,0.09999882,0.0003895664,0.00016641465,0.00034700302,0.000019175455,0.00078674476,0.000022803528],"genre_scores_gemma":[0.9934985,0.00016550565,0.00024272976,0.000007672089,0.000032615008,0.00009041413,0.0052817906,0.000027569571,0.00065318524],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863636,0.000047796293,0.00036718743,0.0003752522,0.00018825721,0.00038514502],"domain_scores_gemma":[0.99922395,0.000082902196,0.00011821868,0.00047408885,0.000040296574,0.00006054194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005137469,0.00020722824,0.00032965257,0.0005198048,0.00027456257,0.00008082512,0.00021827659,0.000248648,0.00026494073],"category_scores_gemma":[0.000030881885,0.00021105839,0.00037510268,0.0009496053,0.00002367955,0.0005549598,0.00008021168,0.00025602276,0.000056283585],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003448298,0.00036153523,0.8053646,0.0000531088,0.0061622728,0.0000068647746,0.000810131,0.13579364,0.009224074,0.0010333403,0.0056304866,0.03552548],"study_design_scores_gemma":[0.0005621013,0.000038190316,0.4516798,0.000019154706,0.001531675,0.0000012456327,0.0010237817,0.540601,0.0012237106,0.0017421592,0.0013017588,0.00027540332],"about_ca_topic_score_codex":0.0000083522555,"about_ca_topic_score_gemma":0.0030922117,"teacher_disagreement_score":0.40480736,"about_ca_system_score_codex":0.00023472402,"about_ca_system_score_gemma":0.000009553953,"threshold_uncertainty_score":0.8606711},"labels":[],"label_agreement":null},{"id":"W4390457620","doi":"10.4271/2023-01-7109","title":"Game Theory-Based Lane Change Decision-Making Considering Vehicle’s Social Value Orientation","year":2023,"lang":"en","type":"article","venue":"SAE International Journal of Advances and Current Practices in Mobility","topic":"Autonomous Vehicle Technology and Safety","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":"Stackelberg competition; Prosocial behavior; Computer science; Value (mathematics); Trajectory; Obstacle; Orientation (vector space); Class (philosophy); Obstacle avoidance; Reinforcement learning; Artificial intelligence; Robot; Machine learning; Mathematics; Mobile robot; Microeconomics; Psychology; Social psychology; Economics","score_opus":0.03279868751756214,"score_gpt":0.3727208153438968,"score_spread":0.33992212782633463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390457620","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":[0.989268,0.003104132,0.0053311996,0.00035548303,0.0016600448,0.00009479037,0.000013275642,0.000074268675,0.000098784185],"genre_scores_gemma":[0.99664474,0.0022362198,0.0008565698,0.000034479614,0.00020470758,0.000009674361,0.0000043490463,0.000008501631,7.737844e-7],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99900305,0.00007391131,0.0004029154,0.0001293339,0.0002568076,0.00013398276],"domain_scores_gemma":[0.9986089,0.00078661443,0.00039837742,0.000066362525,0.00011447758,0.000025286065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001051276,0.00009827031,0.0001571983,0.00019975573,0.000048849204,0.000036803583,0.00019530693,0.00006033579,0.00002090748],"category_scores_gemma":[0.0005027023,0.00009188031,0.00005064957,0.00016924598,0.00007976391,0.0009247274,0.00004677333,0.00038600134,0.0000041024373],"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":[0.00027876432,0.00009143883,0.043622006,0.00008663238,0.000048223472,0.000046578993,0.0007704342,0.015245214,0.00010460559,0.0030496528,0.000011714144,0.93664473],"study_design_scores_gemma":[0.0030601341,0.00021528146,0.6203831,0.0011135684,0.00008105308,0.00011451101,0.0022043274,0.10195036,0.0004694167,0.22556144,0.044314653,0.0005321597],"about_ca_topic_score_codex":0.0000023919215,"about_ca_topic_score_gemma":0.00003442089,"teacher_disagreement_score":0.9361126,"about_ca_system_score_codex":0.00010400335,"about_ca_system_score_gemma":0.000029665704,"threshold_uncertainty_score":0.37467706},"labels":[],"label_agreement":null},{"id":"W4390457635","doi":"10.4271/2023-01-7110","title":"Vehicle Trajectory Prediction in Highway Merging Area Using Interactive Graph Attention Mechanism","year":2023,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Interpretability; Graph; Trajectory; Artificial intelligence; Machine learning; Data mining; Theoretical computer science","score_opus":0.01253879535157078,"score_gpt":0.22773574623043052,"score_spread":0.21519695087885973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390457635","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":[0.9775885,0.00013967561,0.000027291357,0.0006946903,0.00059673365,0.00078405003,0.00006954842,0.01208594,0.008013614],"genre_scores_gemma":[0.9978249,0.00031069224,0.00091788766,0.00018698169,0.000087793494,0.00028983707,0.00007619691,0.00016572504,0.00014000403],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9962186,0.00013260425,0.0010616528,0.0009877682,0.0005437303,0.0010556295],"domain_scores_gemma":[0.998399,0.00029965883,0.00014687455,0.0008954621,0.000058816873,0.00020015087],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007133599,0.00069557596,0.000790592,0.00084710296,0.00035587378,0.000053017397,0.00066944695,0.0011980691,0.0002128075],"category_scores_gemma":[0.00020157237,0.00070763566,0.00037962134,0.0018800951,0.0004866913,0.00073340925,0.00027036254,0.0019835862,0.00016107666],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013748425,0.00013567906,0.00047459183,0.000051060546,0.00006892113,0.000094601164,0.00006257212,0.0022304067,0.9872738,0.006236623,0.00043872208,0.002795531],"study_design_scores_gemma":[0.0008226443,0.000413153,0.9873937,0.00034456083,0.00007013225,0.000068301386,0.00033674447,0.00010082476,0.0014557723,0.0068136808,0.0014882762,0.0006922395],"about_ca_topic_score_codex":0.000039775747,"about_ca_topic_score_gemma":0.0183815,"teacher_disagreement_score":0.9869191,"about_ca_system_score_codex":0.0007417651,"about_ca_system_score_gemma":0.000051411644,"threshold_uncertainty_score":0.99953747},"labels":[],"label_agreement":null},{"id":"W4390575536","doi":"10.1139/dsa-2023-0036","title":"Assessing wind impact on semi-autonomous drone landings for in-contact power-line inspection","year":2024,"lang":"en","type":"article","venue":"Drone Systems and Applications","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Drone; Line (geometry); Controller (irrigation); Automotive engineering; Power (physics); Computer science; Marine engineering; Envelope (radar); Wind power; Aeronautics; Simulation; Engineering; Aerospace engineering; Electrical engineering","score_opus":0.010446024536051868,"score_gpt":0.2734978742988669,"score_spread":0.26305184976281504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390575536","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":[0.9252734,0.0032881582,0.06291441,0.00020587133,0.00026121046,0.0012228149,0.00009110274,0.0015514501,0.005191561],"genre_scores_gemma":[0.9992445,0.00004484049,0.000056299228,0.000006690103,0.0001490654,0.00032255985,0.000037104222,0.000034120487,0.00010478974],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926233,0.0000076125066,0.00024744743,0.00023346939,0.000047749705,0.00020139212],"domain_scores_gemma":[0.9996671,0.00007585127,0.00002849798,0.00017101443,0.000012972652,0.00004453624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017653589,0.00014942006,0.00020116019,0.00017655874,0.00012419335,0.00011322752,0.00006371555,0.00015959887,0.000004720022],"category_scores_gemma":[0.000003074692,0.00013448781,0.000056219164,0.0002205555,0.000023674917,0.00015992047,0.0000110412375,0.00021240867,0.000022964172],"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":[0.00006359746,0.00037164128,0.004746408,0.0020214103,0.000654566,0.000028381675,0.0018035246,0.09895637,0.08474666,0.7327784,0.0019621789,0.07186687],"study_design_scores_gemma":[0.0018277905,0.0006818219,0.03179081,0.0008002213,0.00015000324,0.00021254578,0.0015343846,0.86068624,0.0035590448,0.006033047,0.09139341,0.0013306455],"about_ca_topic_score_codex":0.00007048822,"about_ca_topic_score_gemma":0.000015127832,"teacher_disagreement_score":0.7617299,"about_ca_system_score_codex":0.00022630561,"about_ca_system_score_gemma":0.000025006257,"threshold_uncertainty_score":0.5484254},"labels":[],"label_agreement":null},{"id":"W4390646759","doi":"10.23977/jeis.2023.080610","title":"An Efficient Lidar-Based Algorithm for Autonomous Vehicle's Visual Detection","year":2023,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Lidar; Object detection; Feature (linguistics); Algorithm; Feature extraction; Computer vision; Orientation (vector space); Point cloud; Pattern recognition (psychology); Remote sensing; Mathematics","score_opus":0.004513126570235307,"score_gpt":0.24011085680723152,"score_spread":0.2355977302369962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390646759","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":[0.6438207,0.00007012808,0.35563618,0.00005928448,0.00015753332,0.00007717997,0.0000022736613,0.00011578034,0.000060952585],"genre_scores_gemma":[0.99774253,0.000068919464,0.002091761,0.000061123115,0.00002504638,0.0000039464826,0.0000014527064,0.0000041323447,0.0000011084742],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992222,0.000004789105,0.00030225376,0.000050650004,0.00017724965,0.00024285374],"domain_scores_gemma":[0.9995331,0.00002875971,0.00011758085,0.00006658954,0.00018701104,0.00006696584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010272918,0.000065163396,0.00009345925,0.00045511243,0.0002163123,0.00007816468,0.00015164755,0.00005998026,0.0000015069494],"category_scores_gemma":[0.00003044758,0.000058457932,0.000030736548,0.00060774636,0.00008241589,0.0013986556,0.000009702494,0.00016498972,0.0000047199323],"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":[0.0000125411425,0.000012072459,0.000018142162,0.000011501967,0.0000051249126,4.0936752e-7,0.00017657939,0.11353126,0.010678383,0.0009967656,0.000011469416,0.87454575],"study_design_scores_gemma":[0.00031053764,0.00045573525,0.001417125,0.0000045304355,0.0000047884887,0.000015541073,0.00007109762,0.9578475,0.03446977,0.00012904279,0.005206421,0.0000679487],"about_ca_topic_score_codex":4.0062054e-7,"about_ca_topic_score_gemma":6.343686e-7,"teacher_disagreement_score":0.8744778,"about_ca_system_score_codex":0.00013713649,"about_ca_system_score_gemma":0.00019789593,"threshold_uncertainty_score":0.23838452},"labels":[],"label_agreement":null},{"id":"W4390815511","doi":"10.3390/vehicles6010007","title":"Collision Risk in Autonomous Vehicles: Classification, Challenges, and Open Research Areas","year":2024,"lang":"en","type":"article","venue":"Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Collision; Reliability (semiconductor); Computer science; Hazard; Warning system; Open research; Field (mathematics); Collision avoidance; Stability (learning theory); Risk analysis (engineering); Computer security; Machine learning; Business; Telecommunications","score_opus":0.0725604630638158,"score_gpt":0.31593062204655636,"score_spread":0.24337015898274056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390815511","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":[0.941807,0.037934337,0.00008830801,0.0023133606,0.00015055762,0.0005271887,0.000023044955,0.0009890854,0.016167117],"genre_scores_gemma":[0.9742336,0.024947237,0.00036904847,0.000012208565,0.00004982062,0.00011637813,0.000007194944,0.000046810987,0.00021768786],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985501,0.00014037456,0.0003050796,0.00044339572,0.00016979378,0.000391227],"domain_scores_gemma":[0.9991188,0.0003326693,0.000018717948,0.00040641564,0.000042318483,0.00008104531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015650254,0.00016440435,0.00022496609,0.0003958578,0.00016123222,0.00014680602,0.00047919288,0.00030850142,0.000013464728],"category_scores_gemma":[0.00006900701,0.0001605506,0.000026722286,0.00046761116,0.00019675824,0.00031778353,0.00024685936,0.00083696545,0.000167154],"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":[0.000036753172,0.000085133484,0.0035186799,0.00021838253,0.000062915016,0.000087356784,0.002926373,0.00065714604,0.0021566488,0.087280616,0.00189302,0.901077],"study_design_scores_gemma":[0.0010825775,0.0001935307,0.40395015,0.00054024777,0.000026660984,0.000049618804,0.0025378575,0.31082755,0.0037831801,0.06283925,0.21350099,0.00066836877],"about_ca_topic_score_codex":0.00011440524,"about_ca_topic_score_gemma":0.00028673655,"teacher_disagreement_score":0.9004086,"about_ca_system_score_codex":0.00019028716,"about_ca_system_score_gemma":0.00007496106,"threshold_uncertainty_score":0.6547063},"labels":[],"label_agreement":null},{"id":"W4390825367","doi":"10.55417/fr.2024001","title":"Fast and Modular Autonomy Software for Autonomous Racing Vehicles","year":2024,"lang":"en","type":"article","venue":"Field Robotics","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Pittsburgh","keywords":"Modular design; Software deployment; Software; Computer science; Replicate; Autonomy; Domain (mathematical analysis); Competition (biology); Autonomous system (mathematics); Work (physics); Autonomous agent; Exploit; Systems engineering; Software engineering; Human–computer interaction; Engineering; Computer security; Artificial intelligence; Operating system","score_opus":0.007764652217397281,"score_gpt":0.21428787489733084,"score_spread":0.20652322267993356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390825367","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":[0.020078648,0.0028210431,0.9720205,0.0011566267,0.00063574704,0.00023181493,0.000015616532,0.002296053,0.0007439249],"genre_scores_gemma":[0.94352025,0.00007403632,0.05548154,0.000119771066,0.00012959652,0.000030070027,0.000010529542,0.000043643668,0.0005905599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935883,0.000004746532,0.00017055175,0.00018670954,0.00003946426,0.00023971937],"domain_scores_gemma":[0.99956566,0.0001889078,0.000010114573,0.00017269111,0.00001812725,0.000044473927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007876135,0.0001406895,0.0001573446,0.00007417008,0.00009448061,0.000049311802,0.00010307162,0.00024392612,0.00001598572],"category_scores_gemma":[0.000035078192,0.00014394142,0.00004866434,0.000091853704,0.00003264233,0.00010065672,0.000043563214,0.00026503397,0.00001810262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008050271,0.000015791882,0.00075098383,0.0007207186,0.0001813295,0.000043083255,0.00088524,0.5247204,0.00093728234,0.029986003,0.0028938395,0.43885732],"study_design_scores_gemma":[0.00016330053,0.00006932402,0.00041851724,0.00007744364,0.000047816346,0.00002733764,0.00005697395,0.9701039,0.00415167,0.0052203955,0.01938287,0.00028043584],"about_ca_topic_score_codex":0.0000029789046,"about_ca_topic_score_gemma":0.000004331473,"teacher_disagreement_score":0.9234416,"about_ca_system_score_codex":0.00004983848,"about_ca_system_score_gemma":0.00004148117,"threshold_uncertainty_score":0.58697605},"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":"W4390871908","doi":"10.1109/iccv51070.2023.00760","title":"Towards Zero Domain Gap: A Comprehensive Study of Realistic LiDAR Simulation for Autonomy Testing","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Lidar; Computer science; Leverage (statistics); Robustness (evolution); Autonomy; Simulation; Computer vision; Real-time computing; Artificial intelligence; Remote sensing; Geography","score_opus":0.06276687160313914,"score_gpt":0.29618988610331876,"score_spread":0.2334230145001796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390871908","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":[0.8879002,0.000012981685,0.105465546,0.000034638597,0.00008579564,0.0007786278,0.000014165992,0.0019072362,0.0038008122],"genre_scores_gemma":[0.9935924,7.784654e-7,0.006187424,0.000010677927,0.000019033043,0.000078905,0.000013983458,0.000025706147,0.000071122115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999259,0.000015228503,0.00030193044,0.00015021238,0.000072465635,0.00020118698],"domain_scores_gemma":[0.99920875,0.00043196956,0.00004361634,0.00020490536,0.00008453902,0.0000262319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011696058,0.000117572534,0.00021583404,0.00013450523,0.000075178345,0.000006171281,0.00011300952,0.00009747869,0.000010460327],"category_scores_gemma":[0.00008590402,0.00011765998,0.00003597341,0.00037646512,0.000029586654,0.000047760164,0.000044195207,0.00009447696,0.00001501333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026043013,0.000062633204,0.0015996492,0.000106874504,0.000096418844,0.000007091712,0.0013922083,0.9712582,0.0017343077,0.0029577091,0.00015311808,0.020605708],"study_design_scores_gemma":[0.0014089722,0.0002860528,0.032203887,0.000016206814,0.000030812924,0.000001408092,0.0012940867,0.9540204,0.00043607788,0.009442936,0.00067917374,0.00018003095],"about_ca_topic_score_codex":0.000047214,"about_ca_topic_score_gemma":0.000014630558,"teacher_disagreement_score":0.10569218,"about_ca_system_score_codex":0.00005828206,"about_ca_system_score_gemma":0.00002869257,"threshold_uncertainty_score":0.4798035},"labels":[],"label_agreement":null},{"id":"W4390874244","doi":"10.1109/iccv51070.2023.00770","title":"Learn TAROT with MENTOR: A Meta-Learned Self-supervised Approach for Trajectory Prediction","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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; Trajectory; Artificial intelligence; Machine learning; Graph; Task (project management); Supervised learning; Encoder; Theoretical computer science; Artificial neural network","score_opus":0.030365348402338108,"score_gpt":0.21579646629807378,"score_spread":0.1854311178957357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390874244","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":[0.23218243,0.00069908105,0.6544301,0.0008241229,0.0003453384,0.0034846917,0.00009379454,0.048017178,0.059923295],"genre_scores_gemma":[0.95726603,0.00008655251,0.03918491,0.00004044041,0.00005803847,0.0008699157,0.000104523184,0.000077547644,0.0023120546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991888,0.000016465772,0.00016128064,0.00022961498,0.00010388718,0.00029996756],"domain_scores_gemma":[0.99964833,0.000036824855,0.00001548362,0.00023047261,0.000020735946,0.00004812497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023851679,0.00016612266,0.00024255361,0.00012729072,0.00010008407,0.000014886377,0.00013091718,0.00017875036,0.000093181094],"category_scores_gemma":[0.0000056587787,0.00012837767,0.00010334934,0.00029566276,0.000030119203,0.00013195038,0.000017272245,0.0001854242,0.000042145184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006149333,0.00066052016,0.0032058617,0.0015733092,0.022862451,0.000024879058,0.006458688,0.8062205,0.017761702,0.013242159,0.03890784,0.0884672],"study_design_scores_gemma":[0.0010408248,0.00019469453,0.0007534928,0.0000032833627,0.00050709664,0.000007003192,0.0005121614,0.98418903,0.0028680144,0.00013948756,0.009550045,0.00023488348],"about_ca_topic_score_codex":0.0000051547786,"about_ca_topic_score_gemma":0.0000068893196,"teacher_disagreement_score":0.7250836,"about_ca_system_score_codex":0.000053604526,"about_ca_system_score_gemma":0.000018476227,"threshold_uncertainty_score":0.5235089},"labels":[],"label_agreement":null},{"id":"W4391033686","doi":"10.1007/s42154-023-00282-9","title":"Preface for Feature Topic on Human Driver Behaviours for Intelligent Vehicles","year":2024,"lang":"en","type":"article","venue":"Automotive Innovation","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":"University of Waterloo","funders":"","keywords":"Feature (linguistics); Computer science; Human–computer interaction","score_opus":0.03064501158305743,"score_gpt":0.3040794792269673,"score_spread":0.2734344676439099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391033686","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":[0.91068554,0.000117709606,0.08466372,0.0010338395,0.0005947794,0.0007966359,0.00011007205,0.0014630975,0.0005346347],"genre_scores_gemma":[0.99722326,0.0000038650905,0.0012056753,0.000100519675,0.00010672097,0.00016073478,0.00022367651,0.000036095116,0.00093945465],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99937105,0.0000057324737,0.0001814879,0.00020238919,0.000059652306,0.00017965928],"domain_scores_gemma":[0.99964225,0.00006887056,0.000024095003,0.00013240984,0.000121016135,0.000011354425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014580233,0.00013446501,0.0001170018,0.00021744726,0.00010425624,0.000028224076,0.00009888705,0.00020944478,0.000010669362],"category_scores_gemma":[0.000028466065,0.00013185643,0.00004997259,0.00032757537,0.00003544719,0.00010441851,0.0000128543725,0.00021534835,0.0000129081145],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000337664,0.00008373949,0.001741862,0.00027246977,0.00021998487,0.0000048818943,0.0011248385,0.0032329888,0.022802219,0.7398206,0.021898163,0.20876451],"study_design_scores_gemma":[0.0010606743,0.0012050347,0.11074482,0.00036160395,0.0001415908,0.000006722772,0.0002261678,0.0634533,0.66054827,0.065708384,0.09559786,0.00094555056],"about_ca_topic_score_codex":0.0000022359243,"about_ca_topic_score_gemma":0.000006460677,"teacher_disagreement_score":0.6741122,"about_ca_system_score_codex":0.00014833876,"about_ca_system_score_gemma":0.000015405529,"threshold_uncertainty_score":0.53769493},"labels":[],"label_agreement":null},{"id":"W4391248670","doi":"10.1109/tits.2024.3354102","title":"Bayesian Calibration of the Intelligent Driver Model","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport; Institut de Valorisation des Données","keywords":"Calibration; Bayesian probability; Computer science; Intelligent transportation system; Artificial intelligence; Machine learning; Engineering; Statistics; Mathematics; Transport engineering","score_opus":0.014533339891115628,"score_gpt":0.2221835723437384,"score_spread":0.20765023245262276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391248670","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":[0.03063881,0.00039177615,0.965473,0.000074050986,0.0019642592,0.00042525324,0.00014538215,0.0006930718,0.00019441784],"genre_scores_gemma":[0.9989574,0.000213808,0.00012963318,0.00001434624,0.000022231166,0.00009499185,0.0000100951875,0.00004454636,0.00051297515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873847,0.000029139333,0.00060276676,0.00022065017,0.00023312996,0.00017583385],"domain_scores_gemma":[0.9995172,0.000052958858,0.000039392657,0.0003019463,0.000040839448,0.000047667614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012511319,0.00019695441,0.00019595346,0.00020133164,0.000093208786,0.00002734924,0.00019369555,0.0002067916,0.0000640908],"category_scores_gemma":[6.1363005e-7,0.0001589202,0.00021006002,0.00041407987,0.00008274067,0.00016710536,2.0212792e-7,0.00035697586,0.00002279868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010305849,0.000033532422,0.000019722305,0.00021591937,0.000111166286,0.0000021180356,0.0022142313,0.98419654,0.0034127499,0.0031473252,0.000085742715,0.0065506203],"study_design_scores_gemma":[0.000045214903,0.00002183114,0.000021811753,0.00016428079,0.00005944474,0.0000030982567,0.00033651918,0.80688864,0.19185397,0.00014811874,0.00032766367,0.00012943608],"about_ca_topic_score_codex":0.00003637488,"about_ca_topic_score_gemma":0.00010341879,"teacher_disagreement_score":0.9683186,"about_ca_system_score_codex":0.0001080136,"about_ca_system_score_gemma":0.000049135473,"threshold_uncertainty_score":0.64805776},"labels":[],"label_agreement":null},{"id":"W4391395149","doi":"10.1007/978-3-031-52823-1_5","title":"Intruder Vehicle Detection During a Platoon Joining Maneuver","year":2024,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Platoon; Computer science; Aeronautics; Automotive engineering; Artificial intelligence; Engineering; Control (management)","score_opus":0.013261784116173575,"score_gpt":0.2245919348384983,"score_spread":0.21133015072232472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391395149","genre_codex":"other","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":[0.03536986,0.0026882645,0.0963074,0.0007406336,0.0014163089,0.0008186228,0.00003197283,0.0025010735,0.86012584],"genre_scores_gemma":[0.993923,0.0013433566,0.003770955,0.00008810024,0.000026780104,0.00001764898,0.000012930559,0.000015614538,0.0008016187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992271,0.0000036560077,0.00037046123,0.00012213556,0.0001286619,0.00014800402],"domain_scores_gemma":[0.9991379,0.00003689975,0.000055247,0.00068151817,0.000052833573,0.000035612517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002561554,0.00014143263,0.00013420565,0.00071194983,0.0003232624,0.00018538933,0.00057974516,0.00017499816,0.000010398892],"category_scores_gemma":[0.000005345563,0.00015304443,0.000024613099,0.00020993958,0.00046465956,0.0018539046,0.0005817247,0.00063379185,0.00011043724],"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":[0.000006324283,0.000008029014,0.00006610484,0.00029316478,0.000035575606,0.0000027437704,0.0040432266,0.014250565,0.00030110413,0.47570083,0.00007061367,0.5052217],"study_design_scores_gemma":[0.00015807597,0.000015774362,0.001176363,0.00020948549,0.000008396978,0.000044688084,0.000031666,0.93841547,0.00032848693,0.0063492055,0.05299537,0.00026701667],"about_ca_topic_score_codex":0.0000022039349,"about_ca_topic_score_gemma":0.00001672946,"teacher_disagreement_score":0.95855314,"about_ca_system_score_codex":0.0001761824,"about_ca_system_score_gemma":0.000036427155,"threshold_uncertainty_score":0.6240971},"labels":[],"label_agreement":null},{"id":"W4391508483","doi":"10.1016/j.trc.2024.104497","title":"Driver lane change intention prediction based on topological graph constructed by driver behaviors and traffic context for human-machine co-driving system","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; China Scholarship Council; National Natural Science Foundation of China","keywords":"Overfitting; Computer science; Graph; Artificial intelligence; Attention network; Machine learning; Generalization; Topology (electrical circuits); Data mining; Theoretical computer science; Artificial neural network; Engineering; Mathematics","score_opus":0.038676427670406584,"score_gpt":0.31841766200239213,"score_spread":0.27974123433198556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391508483","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":[0.9753834,0.0010203971,0.011913232,0.0006910365,0.0002300167,0.0010444238,0.0004678414,0.009185948,0.00006367538],"genre_scores_gemma":[0.998145,0.00029052226,0.00029909218,0.00000548612,0.000016685493,0.0007104239,0.00047195898,0.000037191014,0.00002363792],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984508,0.000047685146,0.00036894876,0.0004270754,0.0002688224,0.00043665754],"domain_scores_gemma":[0.9994077,0.00020167122,0.000034084245,0.00022559018,0.00008617439,0.000044769702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004606919,0.00022271318,0.0002531844,0.0006792605,0.00037363308,0.00004405001,0.00019644247,0.0004459724,0.000032770484],"category_scores_gemma":[0.00003293759,0.00020822705,0.00008117302,0.00046076812,0.0005922327,0.00017531862,0.000009070459,0.00074350083,0.0000067232886],"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":[0.0004247305,0.00055424776,0.14501564,0.0054728687,0.0008434979,0.00040210172,0.0035177148,0.008450652,0.073003575,0.29073557,0.017173577,0.4544058],"study_design_scores_gemma":[0.005360265,0.0034207494,0.07767332,0.004202788,0.00044211102,0.000029243845,0.02703209,0.7790784,0.06068317,0.0044914666,0.03536102,0.0022253252],"about_ca_topic_score_codex":0.000035973895,"about_ca_topic_score_gemma":0.00015701631,"teacher_disagreement_score":0.7706278,"about_ca_system_score_codex":0.00012075326,"about_ca_system_score_gemma":0.000012603143,"threshold_uncertainty_score":0.84912527},"labels":[],"label_agreement":null},{"id":"W4391516317","doi":"10.1007/978-981-99-8850-1_1","title":"MARS: An Instance-Aware, Modular and Realistic Simulator for Autonomous Driving","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Modular design; Mars Exploration Program; Simulation; Human–computer interaction; Computer graphics (images); Programming language; Astrobiology","score_opus":0.009681250064464578,"score_gpt":0.226166072874737,"score_spread":0.21648482281027243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391516317","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":[0.0024901594,0.0009594217,0.9937518,0.00009402828,0.0008656848,0.00036756726,0.00003102665,0.00075649424,0.0006837771],"genre_scores_gemma":[0.9602074,0.000067290224,0.039086524,0.00012413804,0.0002387194,0.00001537433,0.000012336403,0.00007940749,0.00016881034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983328,0.000004178484,0.00031074169,0.00076092634,0.00016996717,0.00042140216],"domain_scores_gemma":[0.9990777,0.00018760006,0.000047144786,0.0005433744,0.00004848951,0.00009573391],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028436305,0.00037461764,0.00038853544,0.0004049114,0.00013502083,0.00012493973,0.0005564249,0.00047246186,0.0000066209627],"category_scores_gemma":[0.000020649912,0.0003747172,0.000057069094,0.00015187499,0.00045900315,0.00019223752,0.00021207076,0.00063820864,0.000006723939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031244638,0.0000050025556,0.000026033347,0.0001648886,0.000019770861,0.000044519147,0.00022840546,0.6666367,0.00007725851,0.019484403,0.0000043656146,0.3133055],"study_design_scores_gemma":[0.000100536075,0.000072623974,0.000050372535,0.00022089716,0.000015747877,0.00002347879,1.8095656e-7,0.8495232,0.00015901616,0.1474907,0.0019832524,0.00036000396],"about_ca_topic_score_codex":0.0000050000463,"about_ca_topic_score_gemma":0.00009579269,"teacher_disagreement_score":0.95771724,"about_ca_system_score_codex":0.00025691662,"about_ca_system_score_gemma":0.00010013858,"threshold_uncertainty_score":0.9998705},"labels":[],"label_agreement":null},{"id":"W4391546653","doi":"10.1007/978-3-031-34027-7_42","title":"Needs of Autonomous Vehicles for Safe Operation on Existing Highways","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Professional Engineers Ontario; Toronto Metropolitan University","funders":"","keywords":"Transport engineering; Aeronautics; Computer science; Engineering","score_opus":0.013624262870429931,"score_gpt":0.2135054195870442,"score_spread":0.19988115671661427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391546653","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":[0.0032364626,0.011892221,0.7463793,0.00043704922,0.003695539,0.0019225171,0.00051596714,0.0071326047,0.22478832],"genre_scores_gemma":[0.99392945,0.0001354145,0.0032115695,0.000028024739,0.0003375807,0.00006633238,0.0000884002,0.00027804388,0.0019252003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986713,0.0000027410601,0.00055878976,0.00028269365,0.00012652898,0.00035797452],"domain_scores_gemma":[0.9991085,0.00040254532,0.00006106974,0.0003542481,0.00003115603,0.000042480468],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017011633,0.0005153482,0.0005906083,0.00073749776,0.000038667924,0.000022865204,0.00023880143,0.0009612597,0.000037486814],"category_scores_gemma":[0.00007616236,0.00055612414,0.00017513917,0.00011329956,0.000040044208,0.00005257141,0.00005030672,0.0010452527,0.00002676642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011206133,0.0000056053705,0.000003373941,0.00082050654,0.0001087435,0.000011235525,0.0002884698,0.870134,0.0032455414,0.11097566,0.00004874164,0.014346943],"study_design_scores_gemma":[0.00044602592,0.00018520668,0.000022401162,0.0018059048,0.00011196611,0.0000186323,0.0000050260414,0.91594803,0.023092924,0.022221604,0.035097383,0.0010448925],"about_ca_topic_score_codex":0.0000036479291,"about_ca_topic_score_gemma":0.000076104625,"teacher_disagreement_score":0.990693,"about_ca_system_score_codex":0.0003162773,"about_ca_system_score_gemma":0.000035854344,"threshold_uncertainty_score":0.99968904},"labels":[],"label_agreement":null},{"id":"W4391759959","doi":"10.2316/j.2024.201-0415","title":"ACTIVE COLLISION AVOIDANCE CONTROL BASED ON CONVOLUTIONAL NEURAL NETWORK FOR BLIND ZONE PERCEPTION OF AUTOMOTIVE SENSORS, 234-242.","year":2024,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Collision avoidance; Automotive industry; Convolutional neural network; Computer science; Perception; Control (management); Collision; Artificial intelligence; Psychology; Engineering; Neuroscience; Computer security; Aerospace engineering","score_opus":0.005594249633503307,"score_gpt":0.20498779036734396,"score_spread":0.19939354073384066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391759959","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":[0.2579475,0.0064065224,0.7299467,0.0005299987,0.0014813357,0.0021501079,0.0007044621,0.00069938914,0.00013401944],"genre_scores_gemma":[0.99906236,0.000032159784,0.00011929784,0.0000490086,0.00030135876,0.0002692359,0.000026081188,0.00003571986,0.0001048035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987214,0.00008859505,0.00036461154,0.0003065475,0.00015496218,0.00036384622],"domain_scores_gemma":[0.999145,0.00045017502,0.00007452462,0.00017839707,0.00009574157,0.000056219375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004340055,0.00022987055,0.00045823614,0.00010995693,0.00013343069,0.000029835757,0.00009075314,0.00027498667,0.000013843723],"category_scores_gemma":[0.00002308377,0.00020648289,0.00013176561,0.00012612609,0.00007586931,0.000098731776,0.0000073700326,0.0002784918,0.000008126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008302143,0.000031408643,0.00011533825,0.00027129412,0.00027068012,0.0000059440813,0.000106350744,0.97618175,0.0021392014,0.013521738,0.00038053223,0.006145571],"study_design_scores_gemma":[0.003957567,0.00038106306,0.0010908609,0.00021126182,0.00010927241,0.000011708119,0.00018766921,0.99205774,0.00014593266,0.00020764467,0.0014511051,0.00018815015],"about_ca_topic_score_codex":0.000023567818,"about_ca_topic_score_gemma":0.000009015744,"teacher_disagreement_score":0.74111485,"about_ca_system_score_codex":0.00020577328,"about_ca_system_score_gemma":0.000066828725,"threshold_uncertainty_score":0.8420128},"labels":[],"label_agreement":null},{"id":"W4391770047","doi":"10.1109/itsc57777.2023.10422252","title":"Investigating Safety of Evasion Maneuver Choices by Human-Driven Vehicles in Response to High-Density Truck Platoons Near Freeway Diverging Areas","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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 British Columbia","funders":"","keywords":"Truck; Evasion (ethics); Automotive engineering; Vehicle safety; Computer science; Vehicle dynamics; Aeronautics; Engineering","score_opus":0.011751749838869643,"score_gpt":0.22787342732923657,"score_spread":0.21612167749036693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391770047","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":[0.997398,0.000029269995,0.00019881602,0.00040930093,0.000079095356,0.00022358148,0.00003491325,0.0011714059,0.00045557902],"genre_scores_gemma":[0.99830025,0.000013624359,0.0012497713,0.000076526245,0.00001402013,0.000013524285,0.000024156008,0.000036657995,0.00027147905],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9985891,0.00008805122,0.00044257627,0.00029945115,0.00018893866,0.00039185726],"domain_scores_gemma":[0.9991631,0.00029664056,0.0000576271,0.0003538995,0.000027122893,0.00010160601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053914485,0.00020708503,0.00033384174,0.00027226488,0.0002222779,0.000016950851,0.00030558513,0.00025022132,0.000059052065],"category_scores_gemma":[0.00013514429,0.00021578628,0.000054153163,0.00068447314,0.00013596774,0.0001576528,0.00023946591,0.00035567424,0.00010260083],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020257646,0.000051557203,0.24506627,0.00012444227,0.00009226992,0.000053903233,0.0024042707,0.124357715,0.61581105,0.0021991932,0.0043761954,0.005260553],"study_design_scores_gemma":[0.00082425313,0.000078482444,0.87652534,0.00014647795,0.000014597795,0.0000034570537,0.0003493178,0.04002011,0.079799294,0.0009072723,0.0009557669,0.00037560432],"about_ca_topic_score_codex":0.00055844657,"about_ca_topic_score_gemma":0.0007056848,"teacher_disagreement_score":0.6314591,"about_ca_system_score_codex":0.00012808199,"about_ca_system_score_gemma":0.000025272928,"threshold_uncertainty_score":0.87995094},"labels":[],"label_agreement":null},{"id":"W4391883589","doi":"10.3390/app14041579","title":"Simultaneous Trajectory and Speed Planning for Autonomous Vehicles Considering Maneuver Variants","year":2024,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trajectory; Control theory (sociology); Sequential quadratic programming; Kinematics; Trajectory optimization; Mathematics; Position (finance); Acceleration; Tangent; Quadratic programming; Computer science; Mathematical optimization; Optimal control; Geometry; Physics; Classical mechanics; Artificial intelligence","score_opus":0.018960742478443884,"score_gpt":0.24480660705981055,"score_spread":0.22584586458136668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391883589","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":[0.978606,0.0018839457,0.0082038045,0.000105554594,0.00034600313,0.00028048648,0.000010277468,0.0016192727,0.008944686],"genre_scores_gemma":[0.996829,0.000017896691,0.00295406,0.000049445396,0.000050136772,0.000016768588,0.000001162889,0.000018290873,0.000063265325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991668,0.0000041067165,0.00015525353,0.00029424074,0.00007925091,0.00030034376],"domain_scores_gemma":[0.99942535,0.00042427256,0.000012628165,0.00008821398,0.000005578856,0.000043948963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000258415,0.00013549016,0.00014566057,0.000118208955,0.00023257776,0.00008923563,0.00014722577,0.00011116813,0.000013069773],"category_scores_gemma":[0.000015695745,0.0001259581,0.000024066365,0.00016450052,0.00028841948,0.00009670456,0.000031369254,0.0001520529,0.000016831775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000374355,0.000028899767,0.0005530147,0.0005892829,0.00020088135,0.00026809596,0.00598145,0.49495658,0.19725497,0.089599915,0.0011900973,0.20933938],"study_design_scores_gemma":[0.00024855311,0.00006314863,0.00058440154,0.000055446948,0.000029645264,0.00008038946,0.00089461665,0.9634072,0.020201271,0.0084752,0.0055659823,0.00039415172],"about_ca_topic_score_codex":0.0000023612502,"about_ca_topic_score_gemma":0.000003995306,"teacher_disagreement_score":0.4684506,"about_ca_system_score_codex":0.000029087758,"about_ca_system_score_gemma":0.000037847665,"threshold_uncertainty_score":0.51364225},"labels":[],"label_agreement":null},{"id":"W4391929272","doi":"10.1177/09544070241230126","title":"Integrating deep reinforcement learning and social-behavioral cues: A new human-centric cyber-physical approach in automated vehicle decision-making","year":2024,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering","topic":"Autonomous Vehicle Technology and Safety","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":"Concordia University","funders":"","keywords":"Human–computer interaction; Reinforcement learning; Reinforcement; Computer science; Cyber-physical system; Artificial intelligence; Psychology; Social psychology","score_opus":0.008269785077246008,"score_gpt":0.25107430283528964,"score_spread":0.24280451775804363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391929272","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":[0.91345125,0.0007051158,0.08446642,0.00003048293,0.0004105523,0.00023092638,0.0000013826239,0.00056004076,0.00014385144],"genre_scores_gemma":[0.99448115,0.000049099035,0.0053086644,0.0000021017145,0.0001077002,0.000008932196,8.902933e-7,0.000036377445,0.000005104338],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826306,0.0000071190375,0.00090013666,0.00017657777,0.00034809808,0.00030499752],"domain_scores_gemma":[0.99946094,0.000102289676,0.00019723237,0.00006474389,0.00010480862,0.00007001216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055267295,0.00025679203,0.0005223409,0.00046149048,0.00007868718,0.000038843995,0.00032062965,0.00022049452,0.000006321347],"category_scores_gemma":[0.0001481545,0.00021444235,0.00020311234,0.0007411882,0.000068735106,0.00039989233,0.000112256006,0.0010656923,6.734063e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021760869,0.000066885215,0.000096921045,0.0004877076,0.00010770841,0.0000074731597,0.0014772796,0.92485654,0.040381055,0.024998354,0.00005256097,0.007445756],"study_design_scores_gemma":[0.00046230448,0.00013720598,0.00013879481,0.0012447247,0.00007820731,0.000072384275,0.000475537,0.9852107,0.011518664,0.00028976233,0.00017563056,0.0001960833],"about_ca_topic_score_codex":0.000002984722,"about_ca_topic_score_gemma":4.1419491e-7,"teacher_disagreement_score":0.08102991,"about_ca_system_score_codex":0.00023342724,"about_ca_system_score_gemma":0.00006048943,"threshold_uncertainty_score":0.87447053},"labels":[],"label_agreement":null},{"id":"W4392025084","doi":"10.1109/aipr60534.2023.10440658","title":"Synthetic Dataset for Quadcopter Detection Based on Frequency Propeller Signature","year":2023,"lang":"en","type":"article","venue":"","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":"Defence Research and Development Canada; National Research Council Canada","funders":"","keywords":"Quadcopter; Computer science; Signature (topology); Propeller; Artificial intelligence; Engineering; Mathematics; Aerospace engineering; Marine engineering","score_opus":0.010682392965787114,"score_gpt":0.21653939051433396,"score_spread":0.20585699754854686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392025084","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":[0.18551102,0.00023801088,0.7299457,0.005444917,0.0029592477,0.0048027597,0.005124673,0.024744995,0.04122865],"genre_scores_gemma":[0.99827194,0.0000040872333,0.0008328748,0.00013816006,0.000026948694,0.00009704469,0.0003901993,0.000026651516,0.00021208105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994784,0.000007885965,0.00010516712,0.00016074997,0.000053769425,0.00019404602],"domain_scores_gemma":[0.99961865,0.000066689696,0.000009897342,0.00027067383,0.000010178831,0.000023904862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011908321,0.000104215775,0.000087298096,0.00011557913,0.000066108485,0.000007370159,0.000107868786,0.00018921126,0.0001641223],"category_scores_gemma":[0.000023433031,0.000086902226,0.00003357084,0.00016911456,0.000021292792,0.000045346358,0.00000958925,0.00017311201,0.00053514785],"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":[0.00023899143,0.00019694021,0.00037681308,0.0007133404,0.0002303117,0.00006800227,0.00018498533,0.29162002,0.14554247,0.007729264,0.19137809,0.36172077],"study_design_scores_gemma":[0.0004240753,0.00014893035,0.0005213105,0.000021306992,0.000017151548,0.000001980767,0.000023131244,0.8669489,0.089267716,0.0014378775,0.040910468,0.00027712516],"about_ca_topic_score_codex":0.0000036949368,"about_ca_topic_score_gemma":0.000022120614,"teacher_disagreement_score":0.81276095,"about_ca_system_score_codex":0.00003427092,"about_ca_system_score_gemma":0.000007937439,"threshold_uncertainty_score":0.68784213},"labels":[],"label_agreement":null},{"id":"W4392156457","doi":"10.1155/2024/6628559","title":"Evaluation of Preferred Automated Driving Patterns Based on a Driving Propensity Using Fuzzy Inference System","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Korean National Police Agency; University of Seoul","keywords":"Inference; Fuzzy inference system; Fuzzy inference; Fuzzy logic; Computer science; Inference system; Artificial intelligence; Adaptive neuro fuzzy inference system; Fuzzy control system","score_opus":0.024647934065151974,"score_gpt":0.2785044512397021,"score_spread":0.2538565171745501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392156457","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":[0.93591523,0.000101624246,0.06288409,0.0000144646,0.00038942418,0.00020140273,0.000007638066,0.00042975746,0.00005638656],"genre_scores_gemma":[0.99705356,0.00001246469,0.00286727,0.000001973565,0.000026664464,0.00000473859,0.000009329736,0.000023392378,5.912472e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998652,0.000057496763,0.0005903879,0.00011362053,0.0004598418,0.00012665767],"domain_scores_gemma":[0.99925125,0.00007053911,0.00020170482,0.00011507099,0.00032661288,0.00003482012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078691455,0.00012902875,0.00024829753,0.00026766933,0.00004007861,0.0000136564695,0.00008929466,0.00010679924,0.000005820487],"category_scores_gemma":[0.000039554467,0.000119501485,0.00008282909,0.00023661168,0.000017484796,0.00039378862,0.0000013970413,0.00026714656,9.925039e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019347768,0.000019397252,0.0048750155,0.00031188986,0.00006166505,0.000018980194,0.000406226,0.95237887,0.028625283,0.0001035371,7.979434e-7,0.013178981],"study_design_scores_gemma":[0.0004062111,0.00008011171,0.20614399,0.0018000951,0.0002001026,0.000007258185,0.00014234721,0.7779456,0.01309661,0.000081567865,0.0000030121453,0.000093119466],"about_ca_topic_score_codex":0.0000032481357,"about_ca_topic_score_gemma":0.00003975442,"teacher_disagreement_score":0.20126897,"about_ca_system_score_codex":0.00030593498,"about_ca_system_score_gemma":0.0001436527,"threshold_uncertainty_score":0.4873129},"labels":[],"label_agreement":null},{"id":"W4392158141","doi":"10.1109/globecom54140.2023.10437373","title":"GMP: Goal-Based Multimodal Motion Prediction for Automated Vehicles","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Motion (physics); Artificial intelligence","score_opus":0.008784383872264639,"score_gpt":0.22435822663562463,"score_spread":0.21557384276336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392158141","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":[0.76137096,0.000020532934,0.18772468,0.00044818316,0.00042914803,0.00048157372,0.00008457766,0.048146706,0.0012936692],"genre_scores_gemma":[0.99656874,0.0000053445615,0.002902274,0.000027316333,0.00003696613,0.000104902974,0.00013717831,0.000028068358,0.00018919114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942917,0.0000072311173,0.00015575724,0.00013275922,0.000056734105,0.00021832337],"domain_scores_gemma":[0.99973816,0.00005428098,0.000013394677,0.00014113453,0.0000238194,0.000029231634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001308711,0.000097380645,0.00009658884,0.00013299892,0.00008871825,0.000008381843,0.00007725435,0.00019805631,0.000026379665],"category_scores_gemma":[0.000020560905,0.00009780515,0.000048444643,0.00023409202,0.000027108945,0.00007584254,0.000010399808,0.0000887837,0.0001692857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003447903,0.000045514353,0.0047240374,0.00013729428,0.000073142,0.000004411815,0.00009010684,0.8761797,0.035496343,0.0019268128,0.017808747,0.063479416],"study_design_scores_gemma":[0.0004981784,0.00003151525,0.03825202,0.000006195116,0.000008585735,7.65681e-7,0.000022307624,0.941088,0.018423125,0.0002715733,0.0013066684,0.00009105017],"about_ca_topic_score_codex":0.0000061517862,"about_ca_topic_score_gemma":0.000007078778,"teacher_disagreement_score":0.23519783,"about_ca_system_score_codex":0.000052927644,"about_ca_system_score_gemma":0.0000097551665,"threshold_uncertainty_score":0.39883783},"labels":[],"label_agreement":null},{"id":"W4392267336","doi":"10.1016/j.inffus.2024.102309","title":"Reliable trajectory prediction in scene fusion based on spatio-temporal Structure Causal Model","year":2024,"lang":"en","type":"article","venue":"Information Fusion","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"King Saud University","keywords":"Trajectory; Computer science; Context (archaeology); Inference; Artificial intelligence; Counterfactual thinking; Representation (politics); Causal inference; Sensor fusion; Machine learning; Data mining; Econometrics; Mathematics","score_opus":0.005438057640589472,"score_gpt":0.19170620440538225,"score_spread":0.18626814676479278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392267336","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":[0.96276736,0.00004762613,0.027740499,0.00010790114,0.0005929789,0.00026252444,0.000054581677,0.0014977998,0.006928734],"genre_scores_gemma":[0.9983879,0.000016089532,0.00097082497,0.00007297124,0.000025991114,0.000014807156,0.00042974943,0.000014595198,0.0000670965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992186,0.000008374422,0.00033400007,0.000099986595,0.00017497229,0.00016408082],"domain_scores_gemma":[0.99974066,0.000014026987,0.000027696375,0.00016149013,0.000023313125,0.000032840264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014033858,0.00013679026,0.00010339091,0.00042175714,0.0000761689,0.000032376152,0.000077386765,0.00028478846,0.0002129673],"category_scores_gemma":[0.000009137039,0.0001311161,0.000030806135,0.00028981798,0.000014788465,0.0006918666,0.000013813215,0.00037243238,0.00010314048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053129574,0.000008178392,0.0006971597,0.00010672827,0.0000030105207,0.000002179451,0.00052288466,0.9737668,0.0013692279,0.00039100455,0.0010520086,0.022027666],"study_design_scores_gemma":[0.00036858435,0.000045442554,0.0038311789,0.00011829614,0.0000044147,0.0000022095908,0.000041335494,0.98744005,0.0048674117,0.0003452084,0.0028159504,0.00011992989],"about_ca_topic_score_codex":0.000021010457,"about_ca_topic_score_gemma":0.000055111617,"teacher_disagreement_score":0.03562052,"about_ca_system_score_codex":0.00019933263,"about_ca_system_score_gemma":0.000057656096,"threshold_uncertainty_score":0.5346759},"labels":[],"label_agreement":null},{"id":"W4392791562","doi":"10.32920/25412809","title":"Evaluation of the Effects of Autonomous Vehicles on Highway Geometric Design","year":2024,"lang":"en","type":"preprint","venue":"","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":"Toronto Metropolitan University","funders":"Southeast University","keywords":"Lidar; Toolbox; Computer science; Geometric design; Automation; MATLAB; Real-time computing; Radar; Simulation; Field (mathematics); Range (aeronautics); Systems engineering; Transport engineering; Engineering; Remote sensing; Telecommunications; Aerospace engineering","score_opus":0.02080574578262156,"score_gpt":0.2407087807745299,"score_spread":0.21990303499190833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392791562","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":[0.9592562,0.0069452487,0.014141147,0.000141653,0.0027431624,0.0019517425,0.000022139166,0.0012731036,0.013525614],"genre_scores_gemma":[0.9990819,0.00008802407,0.00053154194,0.000008360084,0.000026661153,0.00009854679,0.000002544272,0.000038177826,0.00012423322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986045,0.00015362151,0.00037473414,0.00024391562,0.00046340117,0.00015986737],"domain_scores_gemma":[0.9986888,0.00038823063,0.00010401416,0.0006772083,0.0001225332,0.000019174196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010122148,0.00022632109,0.0003479163,0.00043376407,0.000024504989,0.0000071904224,0.0004331751,0.0005497629,0.00003592485],"category_scores_gemma":[0.00026419654,0.00016166033,0.00016835172,0.00046244042,0.00008564948,0.000013024979,0.0003668813,0.0007628822,0.000033732445],"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":[0.000009662868,0.00008156494,0.000060695857,0.002202114,0.00065662,0.000002253661,0.00019717612,0.7314373,0.009619415,0.006542303,0.001321385,0.24786946],"study_design_scores_gemma":[0.0002812742,0.00010052322,0.0066884025,0.00047754322,0.0006048775,0.0000014708666,0.000007661933,0.34992546,0.5972988,0.044338267,0.000052004434,0.00022375441],"about_ca_topic_score_codex":0.000018459517,"about_ca_topic_score_gemma":0.0000032075102,"teacher_disagreement_score":0.5876794,"about_ca_system_score_codex":0.00026173162,"about_ca_system_score_gemma":0.00019781502,"threshold_uncertainty_score":0.65923166},"labels":[],"label_agreement":null},{"id":"W4392791572","doi":"10.32920/25412806.v1","title":"Effect of Autonomous Vehicles on Various Sight Distance Requirements","year":2024,"lang":"en","type":"preprint","venue":"","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":"Toronto Metropolitan University","funders":"","keywords":"Sight; Braking distance; Geometric design; Computer science; Design speed; Simulation; Automotive engineering; Engineering; Transport engineering","score_opus":0.0073815470918252,"score_gpt":0.24097565894004305,"score_spread":0.23359411184821785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392791572","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":[0.85707176,0.0036236874,0.005432843,0.00023941456,0.0025591,0.0010403898,0.0001422403,0.0056567825,0.12423376],"genre_scores_gemma":[0.9979679,0.0001125894,0.00025932293,0.000017743876,0.000057181474,0.00008910141,0.000026089681,0.00008298626,0.0013870836],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99855375,0.00004595777,0.00045925457,0.00045262653,0.0001766522,0.00031176533],"domain_scores_gemma":[0.998973,0.00013914624,0.00006846635,0.00075110665,0.00001787006,0.00005041012],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031453636,0.00043126568,0.0005900127,0.00018479717,0.00003633395,0.000022475318,0.00045216075,0.00069787825,0.00007563207],"category_scores_gemma":[0.00001879643,0.00036737672,0.00019368324,0.00012052065,0.00010291525,0.000023165601,0.0004845402,0.0012340613,0.0002664882],"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":[0.0005102747,0.00032953316,0.0015235762,0.020917222,0.0036144685,0.00059859734,0.0010926294,0.26037243,0.014352513,0.12459994,0.013297105,0.5587917],"study_design_scores_gemma":[0.00137588,0.0016215527,0.0011956461,0.002440103,0.00065976847,0.000019206738,0.000017550918,0.28152305,0.63339716,0.059156496,0.016346127,0.0022474318],"about_ca_topic_score_codex":0.000020387191,"about_ca_topic_score_gemma":0.000010327603,"teacher_disagreement_score":0.61904466,"about_ca_system_score_codex":0.0002575782,"about_ca_system_score_gemma":0.000042902037,"threshold_uncertainty_score":0.9998778},"labels":[],"label_agreement":null},{"id":"W4392791833","doi":"10.32920/25412809.v1","title":"Evaluation of the Effects of Autonomous Vehicles on Highway Geometric Design","year":2024,"lang":"en","type":"preprint","venue":"","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":"Toronto Metropolitan University","funders":"Southeast University","keywords":"Lidar; Toolbox; Computer science; Geometric design; Automation; MATLAB; Real-time computing; Radar; Field (mathematics); Simulation; Range (aeronautics); Transport engineering; Engineering; Remote sensing; Telecommunications; Aerospace engineering","score_opus":0.02080574578262156,"score_gpt":0.2407087807745299,"score_spread":0.21990303499190833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392791833","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":[0.9592562,0.0069452487,0.014141147,0.000141653,0.0027431624,0.0019517425,0.000022139166,0.0012731036,0.013525614],"genre_scores_gemma":[0.9990819,0.00008802407,0.00053154194,0.000008360084,0.000026661153,0.00009854679,0.000002544272,0.000038177826,0.00012423322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986045,0.00015362151,0.00037473414,0.00024391562,0.00046340117,0.00015986737],"domain_scores_gemma":[0.9986888,0.00038823063,0.00010401416,0.0006772083,0.0001225332,0.000019174196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010122148,0.00022632109,0.0003479163,0.00043376407,0.000024504989,0.0000071904224,0.0004331751,0.0005497629,0.00003592485],"category_scores_gemma":[0.00026419654,0.00016166033,0.00016835172,0.00046244042,0.00008564948,0.000013024979,0.0003668813,0.0007628822,0.000033732445],"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":[0.000009662868,0.00008156494,0.000060695857,0.002202114,0.00065662,0.000002253661,0.00019717612,0.7314373,0.009619415,0.006542303,0.001321385,0.24786946],"study_design_scores_gemma":[0.0002812742,0.00010052322,0.0066884025,0.00047754322,0.0006048775,0.0000014708666,0.000007661933,0.34992546,0.5972988,0.044338267,0.000052004434,0.00022375441],"about_ca_topic_score_codex":0.000018459517,"about_ca_topic_score_gemma":0.0000032075102,"teacher_disagreement_score":0.5876794,"about_ca_system_score_codex":0.00026173162,"about_ca_system_score_gemma":0.00019781502,"threshold_uncertainty_score":0.65923166},"labels":[],"label_agreement":null},{"id":"W4392791889","doi":"10.32920/25412806","title":"Effect of Autonomous Vehicles on Various Sight Distance Requirements","year":2024,"lang":"en","type":"preprint","venue":"","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":"Toronto Metropolitan University","funders":"","keywords":"Sight; Braking distance; Geometric design; Design speed; Computer science; Simulation; Automotive engineering; Engineering; Transport engineering","score_opus":0.0073815470918252,"score_gpt":0.24097565894004305,"score_spread":0.23359411184821785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392791889","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":[0.85707176,0.0036236874,0.005432843,0.00023941456,0.0025591,0.0010403898,0.0001422403,0.0056567825,0.12423376],"genre_scores_gemma":[0.9979679,0.0001125894,0.00025932293,0.000017743876,0.000057181474,0.00008910141,0.000026089681,0.00008298626,0.0013870836],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99855375,0.00004595777,0.00045925457,0.00045262653,0.0001766522,0.00031176533],"domain_scores_gemma":[0.998973,0.00013914624,0.00006846635,0.00075110665,0.00001787006,0.00005041012],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031453636,0.00043126568,0.0005900127,0.00018479717,0.00003633395,0.000022475318,0.00045216075,0.00069787825,0.00007563207],"category_scores_gemma":[0.00001879643,0.00036737672,0.00019368324,0.00012052065,0.00010291525,0.000023165601,0.0004845402,0.0012340613,0.0002664882],"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":[0.0005102747,0.00032953316,0.0015235762,0.020917222,0.0036144685,0.00059859734,0.0010926294,0.26037243,0.014352513,0.12459994,0.013297105,0.5587917],"study_design_scores_gemma":[0.00137588,0.0016215527,0.0011956461,0.002440103,0.00065976847,0.000019206738,0.000017550918,0.28152305,0.63339716,0.059156496,0.016346127,0.0022474318],"about_ca_topic_score_codex":0.000020387191,"about_ca_topic_score_gemma":0.000010327603,"teacher_disagreement_score":0.61904466,"about_ca_system_score_codex":0.0002575782,"about_ca_system_score_gemma":0.000042902037,"threshold_uncertainty_score":0.9998778},"labels":[],"label_agreement":null},{"id":"W4392846039","doi":"10.2139/ssrn.4760696","title":"Safety Aware Neural Network for Connected and Automated Vehicle Operations","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial neural network; Computer science; Vehicle safety; Artificial intelligence; Automotive engineering; Engineering","score_opus":0.006155221671525006,"score_gpt":0.22941270901978755,"score_spread":0.22325748734826253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392846039","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":[0.87004846,0.049952,0.06261211,0.0040282845,0.0028987098,0.0013716184,0.00024379927,0.008469771,0.00037524384],"genre_scores_gemma":[0.99435955,0.0041564344,0.00046120875,0.00004391647,0.00048854994,0.000057560203,0.00009071202,0.000096456526,0.00024559966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99730974,0.000043687774,0.00045411213,0.00030680673,0.00009087026,0.0017948002],"domain_scores_gemma":[0.99949706,0.000067422545,0.000050303075,0.0002328538,0.000075317876,0.00007702276],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007034292,0.00032899124,0.00038338004,0.00012273884,0.00036403158,0.000111327456,0.00028148567,0.0005808448,0.000011971834],"category_scores_gemma":[0.000026240748,0.000323041,0.00014608775,0.00015165783,0.000063951025,0.000061867904,0.00022046916,0.0054235794,0.000012269805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057706562,0.00001676182,0.0001365017,0.00016430016,0.0011015391,0.00001154156,0.00014055402,0.8821257,0.00017889646,0.09382541,0.0014804971,0.0207606],"study_design_scores_gemma":[0.000415076,0.00009197613,0.0002950798,0.000080010526,0.00014044039,0.00028713298,0.00011410928,0.85733867,0.000034689376,0.14017203,0.0007138305,0.00031692346],"about_ca_topic_score_codex":0.000019363832,"about_ca_topic_score_gemma":0.000995896,"teacher_disagreement_score":0.1243111,"about_ca_system_score_codex":0.0007921842,"about_ca_system_score_gemma":0.00094971346,"threshold_uncertainty_score":0.99992216},"labels":[],"label_agreement":null},{"id":"W4393194030","doi":"10.2139/ssrn.4773028","title":"A Benchmark for Cycling Close Pass Detection from Video Streams","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"STREAMS; Benchmark (surveying); Cycling; Computer science; Real-time computing; Computer network; Geography; Cartography; Forestry","score_opus":0.004646870998062779,"score_gpt":0.21562306737665804,"score_spread":0.21097619637859527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393194030","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":[0.81276333,0.02357803,0.1581744,0.0003919095,0.0028027303,0.00046568544,0.00010350994,0.0011963536,0.00052403554],"genre_scores_gemma":[0.9915154,0.006395321,0.0005293042,0.000015060017,0.001117409,0.000089639354,0.000053429154,0.00011841272,0.00016603216],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.996959,0.000034264194,0.0005288989,0.00042861956,0.0001536248,0.0018956105],"domain_scores_gemma":[0.99927807,0.00010641953,0.0001344586,0.00035945125,0.000049714374,0.00007187538],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0009045931,0.00039861054,0.00042356167,0.00024419933,0.0001855063,0.00009446896,0.0004377858,0.0009119594,0.00002022514],"category_scores_gemma":[0.000043802025,0.00040531773,0.00038035406,0.00011589512,0.00003806103,0.00006651935,0.00021087893,0.009275642,0.000041604242],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012326433,0.000046966637,0.00011200465,0.00022725367,0.0028516443,0.000021953772,0.00032803405,0.028537946,0.0054955315,0.02134091,0.0002062967,0.9407082],"study_design_scores_gemma":[0.00036757864,0.00011352627,0.00006061185,0.00016298534,0.00027362848,0.00010600063,0.0002570489,0.06840687,0.003450485,0.9247724,0.0016024827,0.00042638357],"about_ca_topic_score_codex":0.0000827345,"about_ca_topic_score_gemma":0.0017145764,"teacher_disagreement_score":0.9402818,"about_ca_system_score_codex":0.0021642281,"about_ca_system_score_gemma":0.0008782138,"threshold_uncertainty_score":0.99983984},"labels":[],"label_agreement":null},{"id":"W4393234434","doi":"10.34133/research.0349","title":"Parallel Driving with Big Models and Foundation Intelligence in Cyber–Physical–Social Spaces","year":2024,"lang":"en","type":"article","venue":"Research","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Construct (python library); Big data; Cornerstone; Foundation (evidence); Embodied cognition; Cyber-physical system; Space (punctuation); Data science; Artificial intelligence; Artificial general intelligence; Human–computer interaction","score_opus":0.05394978099134844,"score_gpt":0.33747460293324316,"score_spread":0.2835248219418947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393234434","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":[0.9599497,0.0004658551,0.034770858,0.0005920156,0.000027247073,0.00013205603,6.7564264e-7,0.00027766885,0.0037839392],"genre_scores_gemma":[0.9991471,0.00025655684,0.00038705987,0.0000014150705,0.00005427018,0.00002716163,0.0000014033327,0.000014846753,0.000110177054],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993724,0.000029031824,0.000068903886,0.00014989298,0.00014248789,0.0002372613],"domain_scores_gemma":[0.999777,0.00010139011,0.0000030288302,0.000075932774,0.000019186054,0.000023462291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003139958,0.000061426814,0.0000825478,0.00019375882,0.00007680333,0.00006206041,0.00008672929,0.000069406764,0.0000054745374],"category_scores_gemma":[0.000010100939,0.000053628355,0.000010098044,0.00042342363,0.00013901449,0.00013777193,0.000047981674,0.00046060502,0.000032978834],"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":[0.00001935369,0.0000378462,0.0008631761,0.00020995377,0.00005354147,0.00007338572,0.006120697,0.03730903,0.002510247,0.20345826,0.000100104575,0.7492444],"study_design_scores_gemma":[0.000051830866,0.000038256458,0.002742338,0.00006762041,0.0000017019283,0.0000038140483,0.00033014812,0.9591063,0.00068351964,0.036277447,0.00060203584,0.00009497717],"about_ca_topic_score_codex":0.0000337525,"about_ca_topic_score_gemma":0.00019757997,"teacher_disagreement_score":0.9217973,"about_ca_system_score_codex":0.00007370332,"about_ca_system_score_gemma":0.000029217226,"threshold_uncertainty_score":0.21869008},"labels":[],"label_agreement":null},{"id":"W4393242443","doi":"10.1109/camad59638.2023.10478377","title":"Assessment of Adversarial Attacks on Traffic Sign Detection for Connected and Autonomous Vehicles","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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 Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adversarial system; Computer science; Computer security; Sign (mathematics); Traffic sign; Traffic sign recognition; Artificial intelligence; Mathematics","score_opus":0.01180864821800608,"score_gpt":0.24864801175092707,"score_spread":0.23683936353292098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393242443","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":[0.9820253,0.000012629391,0.014776243,0.00007414943,0.0001926817,0.00028407548,0.000013894742,0.0015343744,0.0010866316],"genre_scores_gemma":[0.9991691,0.000016681168,0.000628361,0.000011153848,0.000027230983,0.0000422901,0.0000096915255,0.000018905957,0.0000765459],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994699,0.000010619658,0.00016985125,0.00013732485,0.0000504943,0.00016181378],"domain_scores_gemma":[0.99964964,0.0001704629,0.000023956383,0.00011157557,0.000018807257,0.000025524663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014288253,0.00009761439,0.0001558697,0.00012744812,0.000061469094,0.0000052446253,0.000057200185,0.00016112418,0.000015242409],"category_scores_gemma":[0.000017518236,0.000096180345,0.000038376416,0.00015157294,0.00004211661,0.000039434202,0.000013660533,0.000109002496,0.000007164561],"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":[0.00013493992,0.00009033796,0.0006070721,0.00024444258,0.00030116027,0.0000069151374,0.00031158092,0.40722376,0.11705239,0.0151795745,0.00091034465,0.45793748],"study_design_scores_gemma":[0.0010555931,0.00036268868,0.037048258,0.0000103554175,0.000023643255,0.0000022415416,0.00013643422,0.9204429,0.03955898,0.00033547936,0.00086162024,0.00016179543],"about_ca_topic_score_codex":0.00000343634,"about_ca_topic_score_gemma":0.000023844928,"teacher_disagreement_score":0.5132191,"about_ca_system_score_codex":0.000044916633,"about_ca_system_score_gemma":0.00001595513,"threshold_uncertainty_score":0.39221206},"labels":[],"label_agreement":null},{"id":"W4393253057","doi":"10.1109/tits.2024.3373531","title":"A Driver-Vehicle Model for ADS Scenario-Based Testing","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","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":"Exploratory Research for Advanced Technology; Japan Science and Technology Agency; Gran Sasso Science Institute; Natural Sciences and Engineering Research Council of Canada; Knut och Alice Wallenbergs Stiftelse","keywords":"Computer science; Vehicle safety; Automotive engineering; Simulation; Engineering","score_opus":0.034851545965025515,"score_gpt":0.24751981566535455,"score_spread":0.21266826970032904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393253057","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":[0.049800213,0.00028971885,0.9450568,0.000056936435,0.0011881446,0.0006622611,0.00032367607,0.0025148618,0.00010740717],"genre_scores_gemma":[0.9969578,0.000028199154,0.0020099988,0.000026740923,0.00003791302,0.0005242379,0.00002617523,0.00008094565,0.00030800662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986623,0.00001394122,0.00052888517,0.0003233911,0.00017179758,0.0002996845],"domain_scores_gemma":[0.99938047,0.00020424447,0.000028752891,0.0002171442,0.00008900637,0.00008040472],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001715289,0.00024707674,0.00023041826,0.00029457075,0.00017091387,0.000056844972,0.00012916507,0.00024604396,0.000023582197],"category_scores_gemma":[0.0000017663363,0.0002589186,0.00018358334,0.00040229838,0.000049359664,0.00015969851,7.7705096e-8,0.00033107112,0.000076501965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002600985,0.00003713428,0.000043158187,0.00043914476,0.000090333226,0.0000066233106,0.00053363695,0.97977716,0.0035109187,0.0007534369,0.000083272054,0.014699154],"study_design_scores_gemma":[0.00020497444,0.0000763998,0.000045952456,0.00025702314,0.000085706204,0.0000027985573,0.0001177664,0.9709583,0.02709656,0.00011151634,0.00078284397,0.00026015806],"about_ca_topic_score_codex":0.00003741017,"about_ca_topic_score_gemma":0.00011988013,"teacher_disagreement_score":0.94715756,"about_ca_system_score_codex":0.0001579572,"about_ca_system_score_gemma":0.00008153855,"threshold_uncertainty_score":0.9999863},"labels":[],"label_agreement":null},{"id":"W4393607455","doi":"10.5281/zenodo.7304210","title":"A Survey on Automated Driving System Testing: Landscapes and Trends [Supplementary material]","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science","score_opus":0.022005047088899075,"score_gpt":0.22468304064954406,"score_spread":0.20267799356064498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393607455","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043675504,0.00003514476,0.000010612658,0.00004098058,0.00021798648,0.00020997248,0.9882496,0.0049042962,0.0019638753],"genre_scores_gemma":[0.047190905,0.00006814864,0.000024805217,0.00001687286,0.000060287715,1.7287222e-7,0.9516601,0.00093428337,0.000044408465],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983194,0.00034948988,0.00031425033,0.0004061558,0.00024138798,0.00036929987],"domain_scores_gemma":[0.99917954,0.000048846985,0.00009503841,0.0004961547,0.000080918915,0.000099478806],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007113077,0.00026186492,0.00028108328,0.0004950707,0.0015065252,0.00031720728,0.00081978354,0.00017214596,0.046624172],"category_scores_gemma":[0.00015399794,0.00029233567,0.000028258306,0.0005381089,0.000064624124,0.000060733426,0.0011411275,0.00054965034,0.00046959988],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016189742,0.00003116676,0.000003794647,0.00018510826,0.00008095071,0.000041602743,0.000036212674,0.00030065028,0.000085456115,0.00002258951,0.9887597,0.01043661],"study_design_scores_gemma":[0.00025948737,0.00020767693,0.00222318,0.000053617536,0.00002945362,0.0001090757,0.000055878387,0.0036749912,0.000027148473,0.0000013551165,0.9930793,0.00027881342],"about_ca_topic_score_codex":0.000091309565,"about_ca_topic_score_gemma":0.000008227295,"teacher_disagreement_score":0.046154574,"about_ca_system_score_codex":0.0002724327,"about_ca_system_score_gemma":0.0000024277251,"threshold_uncertainty_score":0.99995285},"labels":[],"label_agreement":null},{"id":"W4394086848","doi":"10.6084/m9.figshare.1481062","title":"Road Characteristics Best Predict the Probability of Vehicle Collisions with a Non-native Ungulate","year":2015,"lang":"en","type":"dataset","venue":"Figshare","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ungulate; Geography; Statistics; Mathematics; Ecology; Biology; Habitat","score_opus":0.020935867854183367,"score_gpt":0.2282498243568151,"score_spread":0.20731395650263174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394086848","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014031222,0.00010482021,0.0000013752982,0.000022178428,0.00005488867,0.0006730286,0.99741334,0.00015033016,0.00017692534],"genre_scores_gemma":[0.012391188,0.0000129215305,0.000023891544,0.000011997342,0.000060926155,0.00030252425,0.9871342,0.000029454084,0.00003287467],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989405,0.000031597723,0.0003218543,0.00023843773,0.00021895076,0.00024861257],"domain_scores_gemma":[0.9985832,0.00010277411,0.00017874893,0.00083200337,0.00023431155,0.00006895171],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009499632,0.00028884882,0.00041496372,0.00005973729,0.00008688373,0.000015934918,0.00062431494,0.0004899848,0.005172677],"category_scores_gemma":[0.000357161,0.00019748716,0.000059641137,0.000245791,0.00006752901,0.00006368412,0.00018367874,0.0007703146,0.0004398298],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016928088,0.000047723854,0.000022576314,0.00039973034,0.00010178549,0.000014137236,0.00005480807,0.0005826902,0.0000015097571,7.6139156e-7,0.99829566,0.0004616954],"study_design_scores_gemma":[0.0003423917,0.00022762326,0.0060000285,0.0019587367,0.00011139817,0.000012443311,0.00003081347,0.006281726,0.00010447827,0.00003183856,0.98452383,0.00037470422],"about_ca_topic_score_codex":0.000023099752,"about_ca_topic_score_gemma":0.0000782723,"teacher_disagreement_score":0.013771837,"about_ca_system_score_codex":0.00011070346,"about_ca_system_score_gemma":0.00020836765,"threshold_uncertainty_score":0.9957367},"labels":[],"label_agreement":null},{"id":"W4394560986","doi":"10.6084/m9.figshare.1481062.v9","title":"Road Characteristics Best Predict the Probability of Vehicle Collisions with a Non-native Ungulate","year":2016,"lang":"en","type":"dataset","venue":"Figshare","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Ungulate; Geography; Environmental science; Statistics; Ecology; Biology; Mathematics; Habitat","score_opus":0.0136922564565514,"score_gpt":0.21576894246276335,"score_spread":0.20207668600621195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394560986","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010682006,0.00006631631,0.0000033890549,0.00003768485,0.000053605847,0.00064520957,0.9978332,0.00014721502,0.00014520904],"genre_scores_gemma":[0.014188961,0.000024394896,0.000014401898,0.000012871574,0.00006943018,0.00034391164,0.98526585,0.000030010253,0.000050166556],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99896646,0.000028848406,0.0003200053,0.00024894066,0.00017254119,0.00026323023],"domain_scores_gemma":[0.99860865,0.00016178087,0.00018257211,0.00085288804,0.0001424293,0.000051680418],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006482675,0.00028777722,0.00038793893,0.00005925823,0.00010432457,0.0000130835115,0.00061478413,0.00047261827,0.010538998],"category_scores_gemma":[0.00027290132,0.00017059143,0.00007022511,0.00017986588,0.00007586857,0.0000654416,0.00016928183,0.0005796433,0.0004686463],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016886795,0.000039429036,0.000027005779,0.00041892426,0.00011587303,0.000012537916,0.000029143586,0.0000930452,0.0000058371575,0.0000015435453,0.99811953,0.0011202386],"study_design_scores_gemma":[0.00036972258,0.00019320264,0.010242737,0.0038581055,0.00009761126,0.000010895615,0.000012877924,0.0016453938,0.0003026627,0.00003452388,0.98283017,0.0004020743],"about_ca_topic_score_codex":0.000011368596,"about_ca_topic_score_gemma":0.00004550447,"teacher_disagreement_score":0.015289342,"about_ca_system_score_codex":0.00008781609,"about_ca_system_score_gemma":0.00012764317,"threshold_uncertainty_score":0.9903655},"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":"W4394618478","doi":"10.4271/2024-01-1978","title":"The Effectiveness of Forward Collision Warning Systems in Detecting Real-World Passenger and Nonpassenger Vehicles Relative to a Surrogate Vehicle Target","year":2024,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Autonomous Vehicle Technology and Safety","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":true,"ca_institutions":"Transport Canada","funders":"","keywords":"Computer science; Collision; Warning system; Aeronautics; Computer security; Engineering; Telecommunications","score_opus":0.006757004209833853,"score_gpt":0.23495364347086106,"score_spread":0.2281966392610272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394618478","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":[0.982446,0.0024521595,0.000011703921,0.0009022278,0.00035056245,0.0014529363,0.000033701934,0.0031175716,0.009233148],"genre_scores_gemma":[0.9976865,0.00067265175,0.0007468417,0.000040591684,0.00005406865,0.00053039356,0.0000074537215,0.00014462357,0.000116847696],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9962401,0.00046119007,0.0010763603,0.0008882364,0.00047056642,0.0008635635],"domain_scores_gemma":[0.99421763,0.0046006916,0.00010882402,0.0007844565,0.00008248451,0.00020592361],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0025383525,0.000617558,0.00095182715,0.00043849414,0.00039013568,0.00011877314,0.00056953786,0.00075985695,0.000010592657],"category_scores_gemma":[0.0007788033,0.0004805291,0.00023121774,0.0016244951,0.0006346043,0.0004501491,0.00036744657,0.0018875302,0.000030470954],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042429537,0.000055872326,0.0011702958,0.00037737307,0.000103607366,0.00008068333,0.00010209561,0.0027277379,0.9629201,0.027503323,0.00011910229,0.0044154935],"study_design_scores_gemma":[0.00048431716,0.0005998865,0.9813489,0.0015972567,0.000058680638,0.000035271187,0.00022713396,0.00006797092,0.0024855274,0.0034153664,0.009103203,0.0005764927],"about_ca_topic_score_codex":0.00013126543,"about_ca_topic_score_gemma":0.028699772,"teacher_disagreement_score":0.9801786,"about_ca_system_score_codex":0.0005195165,"about_ca_system_score_gemma":0.00007491833,"threshold_uncertainty_score":0.9997646},"labels":[],"label_agreement":null},{"id":"W4394687223","doi":"10.4271/2024-01-2643","title":"STEAM &amp; MoSAFE: SOTIF Error-and-Failure Model &amp; Analysis for AI-Enabled Driving Automation","year":2024,"lang":"en","type":"article","venue":"SAE International Journal of Advances and Current Practices in Mobility","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":"University of Waterloo","funders":"","keywords":"Automation; Computer science; Reliability engineering; Engineering; Mechanical engineering","score_opus":0.024114062485408906,"score_gpt":0.35901773846357493,"score_spread":0.334903675978166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394687223","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":[0.74673855,0.016348911,0.23441945,0.0013766888,0.00079460524,0.00014273655,0.000030100266,0.00008648868,0.00006247294],"genre_scores_gemma":[0.98625934,0.005366264,0.00820053,0.000013394519,0.00009622115,0.000015442614,0.00001648232,0.000009957501,0.00002239056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885106,0.000038323207,0.00054796954,0.00020501108,0.00021639193,0.00014126417],"domain_scores_gemma":[0.9990155,0.0003405174,0.00030760944,0.000112377165,0.00017719109,0.00004678917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000910023,0.00013618132,0.00024852104,0.0003296374,0.00005056003,0.00010324157,0.00020368774,0.00008655949,0.000022812086],"category_scores_gemma":[0.00032551363,0.00012109301,0.00011378735,0.000214912,0.00006456164,0.0018184544,0.000051924235,0.0005017263,0.0000013310755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012444756,0.00017652307,0.033703707,0.00042536398,0.0009467978,0.0000052136934,0.0007526259,0.73378325,0.000607594,0.0023849995,0.00017538018,0.22691408],"study_design_scores_gemma":[0.0004615205,0.00003769224,0.012840247,0.00021370087,0.00029795134,0.000047232683,0.00018423673,0.88795143,0.00006398549,0.0221075,0.075597025,0.00019746117],"about_ca_topic_score_codex":0.000003823914,"about_ca_topic_score_gemma":0.0006074223,"teacher_disagreement_score":0.23952076,"about_ca_system_score_codex":0.00013634136,"about_ca_system_score_gemma":0.000051467203,"threshold_uncertainty_score":0.49380296},"labels":[],"label_agreement":null},{"id":"W4394841947","doi":"10.1109/qicar61538.2024.10496644","title":"Simulation to Reality Semantic Segmentation for Humanoid Soccer Robots","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Humanoid robot; Human–computer interaction; Segmentation; Artificial intelligence; Robot; Computer vision; Virtual reality; Natural language processing","score_opus":0.02433456901910291,"score_gpt":0.30483958051676857,"score_spread":0.28050501149766566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394841947","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":[0.06993386,0.00006189955,0.9253907,0.00045643264,0.00021701264,0.00032246296,0.000006496712,0.0017603484,0.001850756],"genre_scores_gemma":[0.99505144,0.0000031080788,0.0040542786,0.00007055197,0.000046986515,0.000044131255,0.000019644258,0.000017352175,0.0006924973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960625,0.000004085856,0.000120399,0.0001167328,0.000037163638,0.0001153834],"domain_scores_gemma":[0.99979997,0.000066846966,0.000004307085,0.000094862065,0.00001349065,0.00002055426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009377174,0.000066645545,0.00006584901,0.0000628508,0.000045075572,0.000019692648,0.000042842057,0.00007761532,0.0000662622],"category_scores_gemma":[0.0000073615593,0.00006478495,0.00003127991,0.00009530554,0.0000075079693,0.00009038023,0.000010082169,0.000055187185,0.00011091499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035417424,0.0000042187635,0.00004207059,0.000088224915,0.000024281751,8.6678705e-7,0.00016690468,0.95865583,0.0045126956,0.00621232,0.0013519736,0.028937051],"study_design_scores_gemma":[0.00007299851,0.000020804206,0.0009098549,0.000010820588,0.00001368025,5.3158664e-7,0.00002333268,0.9838148,0.0094981035,0.0033126646,0.0022259185,0.000096513286],"about_ca_topic_score_codex":0.0000043235573,"about_ca_topic_score_gemma":0.000014776432,"teacher_disagreement_score":0.9251176,"about_ca_system_score_codex":0.00006438035,"about_ca_system_score_gemma":0.000006882749,"threshold_uncertainty_score":0.26418537},"labels":[],"label_agreement":null},{"id":"W4395017560","doi":"10.1109/trs.2024.3392439","title":"Toward Land Vehicle Ego-Velocity Estimation Using Deep Learning and Automotive Radars","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Radar Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Id, ego and super-ego; Automotive industry; Computer science; Artificial intelligence; Acoustics; Remote sensing; Engineering; Geography; Aerospace engineering; Psychology; Physics","score_opus":0.012466007708337909,"score_gpt":0.2277908915549433,"score_spread":0.2153248838466054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395017560","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":[0.1943395,0.0010812306,0.8015352,0.0000536911,0.0009371563,0.00018813623,0.00001767692,0.0016172684,0.0002301626],"genre_scores_gemma":[0.99804914,0.0001050045,0.0016161129,0.0000056003405,0.000036339556,0.00001631195,0.0000027407482,0.000044097844,0.00012467109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999122,0.000052644726,0.00023787792,0.00023602943,0.00012648996,0.00022490497],"domain_scores_gemma":[0.9996917,0.00009046495,0.000022265447,0.00011240662,0.000023541654,0.00005958611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018124843,0.00018254027,0.00021094694,0.00019101407,0.00022634945,0.00007918286,0.0000657477,0.00020905602,0.000017053628],"category_scores_gemma":[0.0000031851384,0.00018745127,0.000055652832,0.0002507179,0.00006221225,0.0002036902,0.0000011345544,0.00052719977,0.00004971297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013978499,0.000021851416,0.000051673072,0.00042581744,0.00021087677,0.00004898729,0.0012980402,0.8865747,0.0020813057,0.00019222929,0.000023745904,0.10905682],"study_design_scores_gemma":[0.00019110968,0.000042402215,0.00010446982,0.00014001776,0.00005470787,0.000116109295,0.00025113078,0.9945128,0.0035155057,0.000037650523,0.0008383791,0.00019571923],"about_ca_topic_score_codex":0.00010779101,"about_ca_topic_score_gemma":0.000018939276,"teacher_disagreement_score":0.8037096,"about_ca_system_score_codex":0.00019789684,"about_ca_system_score_gemma":0.000025239013,"threshold_uncertainty_score":0.7644041},"labels":[],"label_agreement":null},{"id":"W4395484067","doi":"10.1155/2024/4780586","title":"A Precrash Scenario Analysis Comparing Safety Performance across Autonomous Vehicle Driving Modes","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Vehicle safety; Computer science; Automotive engineering; Transport engineering; Engineering","score_opus":0.006850338550284868,"score_gpt":0.23544283065018143,"score_spread":0.22859249209989657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395484067","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":[0.92708653,0.00092636485,0.071096145,0.00005219546,0.00026496622,0.00007130882,0.000009118957,0.00034020926,0.00015318245],"genre_scores_gemma":[0.995636,0.00051243615,0.0037099759,0.0000061237165,0.000055004428,0.0000037006662,0.000018851177,0.000027677637,0.000030245903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99874496,0.000010717314,0.00066513696,0.00014788032,0.00017129879,0.00026001438],"domain_scores_gemma":[0.9995576,0.00006032645,0.0001117872,0.0001324701,0.00007252037,0.00006526514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026976102,0.00016159567,0.00036773065,0.00024815957,0.00011538761,0.000037156722,0.00016474412,0.00011526345,0.000017311453],"category_scores_gemma":[0.000006800372,0.0001576484,0.0002134978,0.0006599404,0.000045317647,0.00081647246,0.0000035099015,0.00049165095,0.000006190792],"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":[0.000041127503,0.000015602156,0.028524566,0.00008771891,0.00045071714,0.00003120526,0.002051655,0.9227933,0.004874023,0.00015837999,0.0000045611428,0.040967125],"study_design_scores_gemma":[0.00035435092,0.000059239734,0.52829087,0.00019626872,0.00031498153,0.000013621991,0.00033481995,0.46458924,0.0048771575,0.000117200965,0.00067162473,0.00018063866],"about_ca_topic_score_codex":0.000002861035,"about_ca_topic_score_gemma":0.00013545295,"teacher_disagreement_score":0.4997663,"about_ca_system_score_codex":0.00017618093,"about_ca_system_score_gemma":0.00003906054,"threshold_uncertainty_score":0.64287156},"labels":[],"label_agreement":null},{"id":"W4396213275","doi":"10.61091/jcmcc119-28","title":"Research on the Application of Computer Big Data Technology in Smart Travel","year":2024,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Computer science; Trajectory; Obstacle avoidance; Process (computing); Dimension (graph theory); Acceleration; Path (computing); Obstacle; Trajectory optimization; Computer vision; Mathematical optimization; Artificial intelligence; Algorithm; Simulation; Optimal control; Mathematics; Robot; Mobile robot","score_opus":0.041038459763970636,"score_gpt":0.2968662370507313,"score_spread":0.25582777728676065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396213275","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":[0.94642997,0.001093932,0.039540533,0.0010446041,0.010508631,0.00040524622,0.0000058039363,0.00013020125,0.0008410992],"genre_scores_gemma":[0.9983236,0.00006712562,0.00093465793,0.000004625679,0.0006383035,0.0000026960888,0.0000011875238,0.000026762138,0.000001008349],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983016,0.000078830235,0.0008091504,0.00017933668,0.0003779806,0.00025310082],"domain_scores_gemma":[0.9979274,0.0012325338,0.00015418157,0.00047573884,0.00016925026,0.000040897605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033888216,0.00015695108,0.0004385634,0.00060328905,0.00010573523,0.000059563896,0.0008259754,0.00025429836,0.000001358893],"category_scores_gemma":[0.0001232347,0.00011800762,0.00005150789,0.00091660215,0.00019357452,0.000081122875,0.0003017339,0.0012461117,0.0000035898274],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017439379,0.00014741538,0.0000643216,0.0002015085,0.00006861487,0.000015434918,0.00031588192,0.00027440747,0.0006002421,0.9726599,0.00025625905,0.025378563],"study_design_scores_gemma":[0.00079665065,0.00037003736,0.00016020701,0.00053862395,0.000024231786,0.00005369606,0.00039905374,0.3077588,0.0018696005,0.6868621,0.0010287378,0.00013825021],"about_ca_topic_score_codex":0.000004991494,"about_ca_topic_score_gemma":7.3566696e-7,"teacher_disagreement_score":0.30748442,"about_ca_system_score_codex":0.000078880206,"about_ca_system_score_gemma":0.0000839553,"threshold_uncertainty_score":0.54138047},"labels":[],"label_agreement":null},{"id":"W4396713572","doi":"10.18280/ria.380106","title":"An Efficient Deep Learning Model Based on Driver Behaviour Detection Within CAN-BUS Signals","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Distraction; Convolutional neural network; Deep learning; Artificial intelligence; CAN bus; Artificial neural network; Real-time computing; Machine learning; Computer security","score_opus":0.014833743459003719,"score_gpt":0.23756113283336078,"score_spread":0.22272738937435707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396713572","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":[0.3842075,0.00013052867,0.6134551,0.00005150759,0.0002570863,0.00013487609,0.00000385102,0.0011753517,0.00058420247],"genre_scores_gemma":[0.998753,0.000016980037,0.0008094547,0.000034312874,0.00004249748,0.000038969276,0.000008798438,0.000056028726,0.00023993308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876016,0.000038620336,0.0003387068,0.00039477303,0.00013522983,0.00033247742],"domain_scores_gemma":[0.9994131,0.00007971461,0.000030595886,0.00035116132,0.000037040198,0.00008843318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030142895,0.00021307163,0.00017001097,0.00023633425,0.0001951153,0.000061657614,0.00021293825,0.0002206907,0.00011478415],"category_scores_gemma":[0.000025668138,0.00022031988,0.000089367044,0.00038239566,0.00007228979,0.000072047675,0.000017379562,0.0006659278,0.00032914212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009310258,0.000048168127,0.00006201889,0.000038584152,0.000009507923,0.000026890368,0.0009982262,0.933699,0.018845519,0.00062603515,0.0000051383163,0.045631573],"study_design_scores_gemma":[0.00001612737,0.000112000744,0.000021289656,0.0000663367,0.000017289269,0.0000072836046,0.00025649145,0.76351875,0.23550472,0.0002274462,0.000055625896,0.00019661585],"about_ca_topic_score_codex":0.000014681037,"about_ca_topic_score_gemma":0.00004120885,"teacher_disagreement_score":0.6145455,"about_ca_system_score_codex":0.00016733777,"about_ca_system_score_gemma":0.000028227538,"threshold_uncertainty_score":0.8984384},"labels":[],"label_agreement":null},{"id":"W4396766647","doi":"10.1109/tvt.2024.3398661","title":"A Collective and Continuous Rule Learning Framework Towards Enhanced Driving Safety and Decision Transparency","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transparency (behavior); Computer science; Knowledge management; Computer security","score_opus":0.004004860779006518,"score_gpt":0.2143396666630265,"score_spread":0.21033480588401998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396766647","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":[0.38555807,0.0015670221,0.6098451,0.0002355966,0.00022577328,0.00017813372,0.000007980382,0.0021711376,0.00021119505],"genre_scores_gemma":[0.99052286,0.0019544654,0.00724488,0.000015974992,0.000013707069,0.00008088163,0.0000012365985,0.000057334568,0.00010867317],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99870604,0.000025184643,0.00031595986,0.000476189,0.000112988906,0.00036362364],"domain_scores_gemma":[0.99944043,0.00019770014,0.0000218934,0.00024330015,0.000032444023,0.000064254236],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015666787,0.00028276708,0.00037802794,0.00060850574,0.00034429118,0.000041434472,0.00014198695,0.0008881355,0.000047951613],"category_scores_gemma":[0.000020296386,0.0002905695,0.00007602185,0.0007810532,0.00027902596,0.00011859653,0.0000041969424,0.001595682,0.000023336164],"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":[0.000047696598,0.00004268542,0.00007333023,0.00007836408,0.00024554643,0.000077657685,0.00090596674,0.025506001,0.015511801,0.0019234531,0.000005218097,0.95558226],"study_design_scores_gemma":[0.0023184398,0.0014756676,0.0022029672,0.0020753192,0.0005431635,0.00087961927,0.0021360875,0.44342145,0.4494172,0.08435567,0.009047439,0.0021269543],"about_ca_topic_score_codex":0.0000057394577,"about_ca_topic_score_gemma":0.000028461784,"teacher_disagreement_score":0.9534553,"about_ca_system_score_codex":0.00018228879,"about_ca_system_score_gemma":0.000058843758,"threshold_uncertainty_score":0.99995464},"labels":[],"label_agreement":null},{"id":"W4398233646","doi":"10.3390/electronics13112041","title":"Using the Buckingham π Theorem for Multi-System Transfer Learning: A Case-Study with 3 Vehicles Sharing a Database","year":2024,"lang":"en","type":"article","venue":"Electronics","topic":"Autonomous Vehicle Technology and Safety","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":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Buckingham; Dimensionless quantity; Computer science; High fidelity; Experimental data; Fidelity; Vehicle dynamics; Artificial intelligence; Simulation; Machine learning; Engineering; Mathematics; Physics; Aerospace engineering","score_opus":0.032208663654950655,"score_gpt":0.2697978437062377,"score_spread":0.23758918005128707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398233646","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":[0.7726032,0.0052455007,0.21991146,0.000025948373,0.00007087254,0.0006043542,0.0000078635585,0.0014637772,0.00006698542],"genre_scores_gemma":[0.99847186,0.000042904903,0.0011560261,0.0000072529942,0.000056675224,0.00011143208,0.000005414643,0.00008956154,0.000058866757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989207,0.000031120137,0.00019282027,0.00030219305,0.00008946378,0.00046371465],"domain_scores_gemma":[0.9995381,0.00009472559,0.00001102037,0.0003020884,0.000023051485,0.00003098863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047772581,0.00020688449,0.0001792321,0.000080281374,0.00035244683,0.00007595733,0.00021367637,0.000102208556,0.0000039863066],"category_scores_gemma":[0.000007461435,0.00014447288,0.0000629384,0.00026533235,0.000049146303,0.00011380749,0.00003077478,0.00072974584,0.0000045530937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005135524,0.0005218935,0.0068366113,0.0025979786,0.0042038495,0.007865446,0.034702793,0.6012573,0.037258957,0.21095309,0.00012268969,0.09316583],"study_design_scores_gemma":[0.000491832,0.00023804512,0.000009550802,0.00007870689,0.00015993058,0.0015552086,0.003972804,0.98547375,0.0030394564,0.000044066663,0.0047083544,0.00022830517],"about_ca_topic_score_codex":0.000021343109,"about_ca_topic_score_gemma":0.0003159955,"teacher_disagreement_score":0.38421643,"about_ca_system_score_codex":0.00021499602,"about_ca_system_score_gemma":0.000068225214,"threshold_uncertainty_score":0.58914334},"labels":[],"label_agreement":null},{"id":"W4398245383","doi":"10.1155/2024/4719669","title":"An IoT‐Based Automatic Vehicle Accident Detection and Visual Situation Reporting System","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Accident (philosophy); Computer science; Internet of Things; Computer security; Transport engineering; Real-time computing; Engineering","score_opus":0.005996978159309222,"score_gpt":0.2527909342433491,"score_spread":0.24679395608403987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398245383","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":[0.8110753,0.0003358184,0.18776698,0.000024532152,0.00032163755,0.000074225514,8.391192e-7,0.00039241437,0.0000082091865],"genre_scores_gemma":[0.99691176,0.000015843716,0.002976265,0.0000046427645,0.000060008577,0.000004780164,0.0000051840348,0.000020414433,0.0000010903652],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883246,0.000015493793,0.0008226229,0.000095467534,0.00013590716,0.00009807418],"domain_scores_gemma":[0.99948686,0.00003518333,0.0003158776,0.000054898395,0.00006223421,0.000044942743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037080346,0.00009214719,0.0001653312,0.0001832821,0.000056428285,0.000025493473,0.000035452424,0.000093195216,0.0000034471027],"category_scores_gemma":[0.000017293394,0.00008901172,0.000052222073,0.00016653955,0.000013796143,0.0004431813,4.8075157e-7,0.00021660594,0.0000014672656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000309212,0.00001743766,0.0017717964,0.00040562826,0.000046274126,0.00012942334,0.00081851304,0.4612494,0.23797254,0.0000963485,8.4390877e-7,0.29746085],"study_design_scores_gemma":[0.00030189476,0.00018553426,0.2463198,0.00028513526,0.000079721074,0.00006611108,0.0006844292,0.7012909,0.05052716,0.00012487093,0.000029542207,0.000104919876],"about_ca_topic_score_codex":0.0000018528892,"about_ca_topic_score_gemma":0.000027810253,"teacher_disagreement_score":0.29735595,"about_ca_system_score_codex":0.00011741281,"about_ca_system_score_gemma":0.00002673373,"threshold_uncertainty_score":0.36297926},"labels":[],"label_agreement":null},{"id":"W4399144556","doi":"10.1109/vrw62533.2024.00258","title":"Prototyping Autonomous Vehicle Lane Detection for Snow in VR","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de Recherche du Québec-Société et Culture; Canada Research Chairs","keywords":"Autopilot; Computer science; Snow; Feature (linguistics); Virtual reality; Real-time computing; Simulation; Artificial intelligence; Engineering; Aerospace engineering; Meteorology","score_opus":0.007627342414749654,"score_gpt":0.21957009821551862,"score_spread":0.21194275580076896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399144556","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":[0.57295126,0.0017003512,0.3848032,0.00070201023,0.0010198741,0.0024252832,0.000012936157,0.010749583,0.025635477],"genre_scores_gemma":[0.99806035,0.000017164075,0.0011398052,0.000019055782,0.00004504827,0.00031110566,0.0000029781481,0.000026679118,0.00037784613],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994762,0.0000048976826,0.00014618097,0.00014463247,0.000027882812,0.00020019166],"domain_scores_gemma":[0.99981433,0.000051365732,0.0000047961535,0.000104388455,0.0000064477304,0.000018682229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000132447,0.00008761745,0.00009556195,0.00012883345,0.00003307963,0.000018849332,0.00006793956,0.00014664157,0.00003985561],"category_scores_gemma":[0.00001189362,0.00008607624,0.000035581175,0.0001700416,0.00001562159,0.000097563905,0.000011878353,0.00019096411,0.000078958736],"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":[0.00001773991,0.000016071719,0.00019358295,0.00026573945,0.00003576479,0.000015544529,0.00024624614,0.0048343306,0.016048895,0.010510825,0.00014273722,0.9676725],"study_design_scores_gemma":[0.00022421594,0.000059228998,0.001300788,0.00005412523,0.0000067440546,0.000012670425,0.000043623753,0.8689478,0.08181894,0.004018075,0.04332768,0.0001861235],"about_ca_topic_score_codex":0.000015710812,"about_ca_topic_score_gemma":0.00018588602,"teacher_disagreement_score":0.9674864,"about_ca_system_score_codex":0.00010826891,"about_ca_system_score_gemma":0.000014280592,"threshold_uncertainty_score":0.3510087},"labels":[],"label_agreement":null},{"id":"W4399207440","doi":"10.1109/access.2024.3408052","title":"Enhanced Vehicle Movement Counting at Intersections via a Self-Learning Fisheye Camera System","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Autonomous Vehicle Technology and Safety","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":"McMaster University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Computer graphics (images); Movement (music); Physics","score_opus":0.006239565262442857,"score_gpt":0.22552064302075858,"score_spread":0.21928107775831573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399207440","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":[0.91748375,0.00032512035,0.06747319,0.000072047165,0.0015518668,0.00017111718,0.0000044727785,0.00569789,0.0072205826],"genre_scores_gemma":[0.9992563,0.000035637364,0.00008080485,0.00004736677,0.00013825805,0.00008303008,0.000004034883,0.000048713053,0.00030584878],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905455,0.000020229025,0.0002541294,0.0002536642,0.0001101394,0.00030728214],"domain_scores_gemma":[0.9996488,0.00005786993,0.000029408124,0.00019260788,0.000027008939,0.00004432533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014295659,0.00016552192,0.00016876773,0.00013270187,0.00021947263,0.000114555405,0.00026060158,0.00014781678,0.00007717085],"category_scores_gemma":[0.0000050389394,0.00017375399,0.00006744953,0.0003106569,0.000025752915,0.00033246801,0.00009190006,0.0004439832,0.000273789],"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":[0.000056590365,0.00013632725,0.011951473,0.004584607,0.0020243085,0.0004336525,0.009416644,0.3888724,0.48517913,0.0031613088,0.0073032086,0.08688033],"study_design_scores_gemma":[0.00019208775,0.000032238393,0.0009954574,0.00022933797,0.000053064847,0.000022318296,0.00030769603,0.8443535,0.14791682,0.00007108117,0.0054972065,0.00032920178],"about_ca_topic_score_codex":0.00009339342,"about_ca_topic_score_gemma":0.00011965115,"teacher_disagreement_score":0.45548105,"about_ca_system_score_codex":0.000560313,"about_ca_system_score_gemma":0.000015519863,"threshold_uncertainty_score":0.7085482},"labels":[],"label_agreement":null},{"id":"W4399364361","doi":"10.2514/1.d0398","title":"Aircraft Categorization Approach Using Machine Learning to Analyze Aircraft Behavior","year":2024,"lang":"en","type":"article","venue":"Journal of Air Transportation","topic":"Autonomous Vehicle Technology and Safety","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":"Royal Military College of Canada","funders":"","keywords":"Categorization; Computer science; Artificial intelligence; Machine learning; Aeronautics; Engineering","score_opus":0.009371517834152066,"score_gpt":0.23252187299665295,"score_spread":0.22315035516250087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399364361","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":[0.55738556,0.00041375798,0.44155604,0.00008123499,0.00022314406,0.00007824112,0.0000069044895,0.00021011518,0.00004501428],"genre_scores_gemma":[0.98308516,0.000063912914,0.016614381,0.000015592384,0.0000931156,0.000004346349,0.000047655,0.000038050188,0.00003777016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990628,0.000020880712,0.00047203765,0.00011468676,0.00017414209,0.00015548],"domain_scores_gemma":[0.9996823,0.000019232823,0.00006412637,0.00007129738,0.0000924721,0.000070546266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028377844,0.0001337288,0.00019825579,0.00038377586,0.00006527983,0.000019713421,0.00010119079,0.00013999367,0.000023676772],"category_scores_gemma":[0.000008941512,0.00012968293,0.00009926303,0.0005593278,0.000016991784,0.00033917328,0.0000016883284,0.00047629478,0.000007240028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018628036,0.00003576005,0.010331917,0.00010901291,0.00008652268,0.00009346986,0.0014397816,0.9569441,0.021726744,0.0003050044,0.000026437501,0.00888264],"study_design_scores_gemma":[0.0004611129,0.00022222172,0.077812,0.00015723726,0.00055400416,0.00017752804,0.00046822114,0.9027704,0.013748103,0.0001909461,0.0030234077,0.00041481477],"about_ca_topic_score_codex":0.000020229058,"about_ca_topic_score_gemma":0.000030353473,"teacher_disagreement_score":0.42569962,"about_ca_system_score_codex":0.00011352219,"about_ca_system_score_gemma":0.000050391474,"threshold_uncertainty_score":0.52883166},"labels":[],"label_agreement":null},{"id":"W4399500931","doi":"10.1016/j.engappai.2024.108767","title":"Exploring Contextual Representation and Multi-modality for End-to-end Autonomous Driving","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Finnish Center for Artificial Intelligence; Ministry of Culture, Sports and Tourism; Academy of Finland","keywords":"End-to-end principle; Computer science; Representation (politics); Modality (human–computer interaction); End user; Artificial intelligence; Human–computer interaction; Computer vision; World Wide Web","score_opus":0.07495864507117614,"score_gpt":0.30327670931508915,"score_spread":0.22831806424391302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399500931","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":[0.09725609,0.00030691014,0.900585,0.00020900056,0.00018921167,0.00049656717,0.00002722547,0.00089650817,0.000033494154],"genre_scores_gemma":[0.96011776,0.00006222615,0.038781647,0.0000035584053,0.00007533395,0.00089951395,0.000009759768,0.000034275243,0.00001590028],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904555,0.000004940497,0.00038890037,0.00028333097,0.00006986236,0.00020738685],"domain_scores_gemma":[0.99934447,0.0002581214,0.000020724436,0.0002646563,0.000045028,0.000066985536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002183485,0.00014264231,0.00017045421,0.00022624597,0.00007162681,0.000037575985,0.00016244962,0.00007724694,0.000009935413],"category_scores_gemma":[0.00007294027,0.00017025869,0.00005386781,0.00038346715,0.000058998052,0.0001869011,0.00004197179,0.00016456672,0.000023794093],"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":[0.0000043027103,0.000022172097,0.000024532299,0.00015807235,0.000049505757,9.292398e-7,0.00088227197,0.2866786,0.039450154,0.105176695,0.000009945979,0.5675428],"study_design_scores_gemma":[0.0000147021165,0.00002134875,0.0003412449,0.00004456238,0.000016965676,0.0000041072353,0.00023276631,0.8337577,0.16201563,0.0013988289,0.001984032,0.00016812515],"about_ca_topic_score_codex":0.000022470782,"about_ca_topic_score_gemma":0.000017447857,"teacher_disagreement_score":0.8628617,"about_ca_system_score_codex":0.00006161752,"about_ca_system_score_gemma":0.000016717831,"threshold_uncertainty_score":0.69429475},"labels":[],"label_agreement":null},{"id":"W4399672849","doi":"10.3390/s24123859","title":"Enhancing Autonomous Vehicle Decision-Making at Intersections in Mixed-Autonomy Traffic: A Comparative Study Using an Explainable Classifier","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Classifier (UML); Autonomy; Collision; Artificial intelligence; Machine learning; Simulation; Transport engineering; Engineering; Computer security","score_opus":0.025402008759786376,"score_gpt":0.28831996749348604,"score_spread":0.26291795873369966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399672849","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":[0.9925831,0.00028126794,0.0034540212,0.000016749289,0.00069672166,0.00043654328,0.000006634503,0.0014531228,0.001071843],"genre_scores_gemma":[0.9984133,0.0000043084215,0.0012784834,0.000008936305,0.000058287784,0.000038476603,0.000002767479,0.00005824561,0.00013716774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832165,0.000087256674,0.00049437623,0.00048493847,0.00012393079,0.00048782257],"domain_scores_gemma":[0.99926686,0.00025011526,0.00003353849,0.0003489201,0.000026055317,0.00007451305],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003165116,0.00028601743,0.000370671,0.00050514925,0.0002516525,0.00007441966,0.00019086886,0.00020340127,0.000088891524],"category_scores_gemma":[0.000017347616,0.00030374466,0.000084545085,0.0005142913,0.00008051482,0.0003245744,0.00009105325,0.00058771024,0.000108184824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003895596,0.00019959148,0.00094587816,0.00003816988,0.00012287719,0.00034092143,0.03139919,0.9441214,0.0038021118,0.00015582611,0.000026844715,0.018808212],"study_design_scores_gemma":[0.00029864814,0.00011147444,0.0036582015,0.00019797747,0.000033369262,0.000063968226,0.021569418,0.97012,0.0026071498,0.00017461028,0.0007991245,0.00036601964],"about_ca_topic_score_codex":0.00006473116,"about_ca_topic_score_gemma":0.0050452123,"teacher_disagreement_score":0.025998617,"about_ca_system_score_codex":0.0012914705,"about_ca_system_score_gemma":0.00008665088,"threshold_uncertainty_score":0.99994147},"labels":[],"label_agreement":null},{"id":"W4399739229","doi":"10.5772/intechopen.1005628","title":"Road Maps and Sensor Integration for the Enhancement of Lane-Keeping Assistants","year":2024,"lang":"en","type":"book-chapter","venue":"Civil engineering.","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":"University of New Brunswick","funders":"","keywords":"Computer science; Geography; Transport engineering; Engineering","score_opus":0.009338012668046704,"score_gpt":0.19479772099398351,"score_spread":0.1854597083259368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399739229","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":[0.0010817258,0.06811011,0.57357347,0.0006598135,0.004814182,0.0026790677,0.0012631876,0.0034934815,0.34432495],"genre_scores_gemma":[0.8604042,0.0043631545,0.0031204175,0.000037888338,0.00047175205,0.00022032752,0.00019967239,0.00040480908,0.13077778],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993007,0.0000010465027,0.00027230586,0.00017987218,0.0000823878,0.0001636879],"domain_scores_gemma":[0.9995766,0.00008904941,0.000040813822,0.00024106204,0.000026943728,0.000025532463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012024955,0.00024912556,0.0002789147,0.00011985951,0.00003087507,0.000013904129,0.00011911099,0.0003069198,0.000036376212],"category_scores_gemma":[0.000014249093,0.0002082253,0.000085060674,0.00002725477,0.000042443255,0.0000276344,0.000037995727,0.00036329107,0.000014255916],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004695463,0.000019533256,0.0000057944226,0.004773158,0.0021307054,0.000033637236,0.00076716265,0.02460114,0.014337859,0.7648711,0.0059760106,0.18243693],"study_design_scores_gemma":[0.00051807624,0.0001617748,0.00020125289,0.0019496328,0.00054620445,0.000039307717,0.000056123863,0.44454852,0.00994196,0.010675731,0.5302002,0.0011612426],"about_ca_topic_score_codex":0.0000018078813,"about_ca_topic_score_gemma":0.00002816886,"teacher_disagreement_score":0.8593225,"about_ca_system_score_codex":0.00007060884,"about_ca_system_score_gemma":0.000010680236,"threshold_uncertainty_score":0.8491181},"labels":[],"label_agreement":null},{"id":"W4400467634","doi":"10.1007/s12293-024-00416-4","title":"Driver profiling using trajectories on arbitrary roads by clustering roads and drivers successively","year":2024,"lang":"en","type":"article","venue":"Memetic Computing","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Research (Canada)","funders":"Agency for Science, Technology and Research","keywords":"Profiling (computer programming); Cluster analysis; Computer science; Complex system; Data mining; Artificial intelligence; Transport engineering; Engineering","score_opus":0.008627332995099194,"score_gpt":0.22438542896562447,"score_spread":0.21575809597052528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400467634","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":[0.935178,0.0014730991,0.059934657,0.000062358784,0.00061380095,0.00014779234,0.0000054459692,0.0013375328,0.001247351],"genre_scores_gemma":[0.9938631,0.000033529,0.0058630477,0.000031707998,0.00011469534,0.000002619022,0.0000053110793,0.00005161132,0.000034353194],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988553,0.000026378224,0.00027066126,0.0003443373,0.00012968451,0.0003736689],"domain_scores_gemma":[0.9996215,0.00012254705,0.000028566645,0.00015189916,0.000014551359,0.000060927883],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020517976,0.00024259403,0.00024351067,0.00017120659,0.00021277893,0.00009055324,0.00014729728,0.00015794451,0.000015771455],"category_scores_gemma":[0.0000157651,0.0002517622,0.0000547557,0.00027341128,0.00010069647,0.00013844458,0.00010424059,0.00048235868,0.000008769032],"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":[0.000044623255,0.00008537501,0.021863062,0.0018911952,0.0008358439,0.00048618767,0.009123191,0.37560293,0.16067539,0.0075876866,0.00067511766,0.42112938],"study_design_scores_gemma":[0.00017652831,0.000038913884,0.0014324936,0.00025369966,0.00003649909,0.000038581522,0.00020062778,0.97912765,0.017612914,0.00017353245,0.000601702,0.00030688877],"about_ca_topic_score_codex":0.0000154642,"about_ca_topic_score_gemma":0.0000032189955,"teacher_disagreement_score":0.6035247,"about_ca_system_score_codex":0.000119394186,"about_ca_system_score_gemma":0.000024390063,"threshold_uncertainty_score":0.99999344},"labels":[],"label_agreement":null},{"id":"W4400585973","doi":"10.1007/978-3-031-65172-4_11","title":"Enhancing EV Charging Station Security Using a Multi-dimensional Dataset: CICEVSE2024","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science","score_opus":0.014471241260348053,"score_gpt":0.2493842731970783,"score_spread":0.23491303193673024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400585973","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":[0.0031750584,0.001401437,0.9925677,0.00007606132,0.0016171916,0.00022221872,0.0002253055,0.0004973957,0.00021760333],"genre_scores_gemma":[0.83446676,0.0000504457,0.16415347,0.00035487532,0.00048511106,0.0000074864224,0.0002630673,0.00011762355,0.00010118681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817735,0.0000072137186,0.0003692483,0.0006987782,0.00032085032,0.0004265557],"domain_scores_gemma":[0.9992357,0.00013732954,0.00006548386,0.0004484259,0.00004669109,0.00006635277],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045093492,0.0003654977,0.00031458677,0.00059010234,0.00016254935,0.00009716541,0.000488414,0.000381033,0.000041585972],"category_scores_gemma":[0.000021058471,0.00037066694,0.00005631609,0.00027445177,0.0002998721,0.0002380734,0.0002744205,0.0011710124,0.000080445534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007264827,0.000019098172,0.000023464496,0.00045456423,0.000069413,0.00030155905,0.0016032446,0.8933096,0.012442521,0.005042618,0.00003360256,0.08669305],"study_design_scores_gemma":[0.00009757538,0.000015964775,0.000011342152,0.00052378443,0.000018298815,0.0000570031,3.7113466e-7,0.9670476,0.006512764,0.024569934,0.00075366494,0.00039172056],"about_ca_topic_score_codex":0.000022967095,"about_ca_topic_score_gemma":0.00018164229,"teacher_disagreement_score":0.8312917,"about_ca_system_score_codex":0.00042286448,"about_ca_system_score_gemma":0.00016289765,"threshold_uncertainty_score":0.99987453},"labels":[],"label_agreement":null},{"id":"W4400647798","doi":"10.1109/iv55156.2024.10588761","title":"Exploring Realism in Virtual Testing: Towards a Scalable Platform Using Open-Source Solutions for Automated Driving Systems","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Transport Canada","keywords":"Open source; Scalability; Computer science; Human–computer interaction; Realism; Open source software; Open platform; Embedded system; Software engineering; Operating system; Software","score_opus":0.16729480372755243,"score_gpt":0.2887944611645899,"score_spread":0.12149965743703747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400647798","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":[0.68488765,0.00049232075,0.29022497,0.00009914713,0.0010670153,0.000937695,0.000020375692,0.01489908,0.00737179],"genre_scores_gemma":[0.99340415,0.000017182625,0.0058612633,0.0000072758658,0.000064952495,0.00025154048,0.000008738937,0.00007405816,0.00031085603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986378,0.000010785207,0.00039634536,0.0002786273,0.00008430438,0.00059214124],"domain_scores_gemma":[0.999483,0.00018354847,0.000021132475,0.00022228244,0.000030701918,0.00005937222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058527297,0.00019131847,0.00026778682,0.00028891745,0.00022394636,0.00020473334,0.00030498157,0.00017898565,0.000007975636],"category_scores_gemma":[0.00008737864,0.00019414184,0.00004229352,0.000649538,0.000037536734,0.00084648986,0.0002269745,0.0002800675,0.000016373026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050354734,0.000021653836,0.00013732589,0.0002118721,0.00007022492,0.000022148142,0.00060833205,0.94144315,0.0060247383,0.03029277,0.00048962503,0.020673145],"study_design_scores_gemma":[0.00023266906,0.00003357908,0.00024056209,0.00044175214,0.000016766018,0.000050745555,0.00066057936,0.9946917,0.00067322375,0.00016873382,0.0025429903,0.00024669117],"about_ca_topic_score_codex":0.00082567317,"about_ca_topic_score_gemma":0.000119431905,"teacher_disagreement_score":0.3085165,"about_ca_system_score_codex":0.0003982535,"about_ca_system_score_gemma":0.000093738076,"threshold_uncertainty_score":0.7916875},"labels":[],"label_agreement":null},{"id":"W4401014362","doi":"10.1016/b978-0-323-99545-0.00002-6","title":"Artificial intelligence for autonomous vehicles: Comprehensive outlook","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.024685752515480542,"score_gpt":0.24351316752160745,"score_spread":0.2188274150061269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401014362","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000066098444,0.004925724,0.0013225788,0.00011418445,0.0013765984,0.0010645612,0.00020973236,0.002217767,0.9887028],"genre_scores_gemma":[0.011399461,0.00018545476,0.0012212407,0.00015511182,0.00055565446,0.00019017112,0.000057868754,0.0003294142,0.98590565],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99807763,0.000005424335,0.00074261904,0.0005564581,0.00014881365,0.0004690374],"domain_scores_gemma":[0.9990013,0.00013528905,0.00008796029,0.0005895308,0.00008416388,0.0001017468],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011992057,0.0006215889,0.000697081,0.00025604683,0.00012440362,0.000052761425,0.00043104863,0.0009910382,0.0001076077],"category_scores_gemma":[0.0000060119955,0.00065850414,0.00039120574,0.000019139628,0.00028094446,0.000034806035,0.0001289289,0.0010511742,0.00141243],"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":[0.000010921556,0.0000032595674,1.1676709e-7,0.00031370125,0.00024254495,0.000037063026,0.0001646573,0.0002574294,0.00007386089,0.16479185,0.00017085973,0.8339337],"study_design_scores_gemma":[0.000029422896,0.000060044193,6.325514e-7,0.0002354686,0.00015316281,0.000019781959,0.000019955198,0.004571972,0.0009714992,0.2567398,0.73665726,0.0005410196],"about_ca_topic_score_codex":1.7396697e-7,"about_ca_topic_score_gemma":0.000012533423,"teacher_disagreement_score":0.83339274,"about_ca_system_score_codex":0.0002255566,"about_ca_system_score_gemma":0.000083412655,"threshold_uncertainty_score":0.99958664},"labels":[],"label_agreement":null},{"id":"W4401024759","doi":"10.24963/ijcai.2024/285","title":"Learning Optimal Oblique Decision Trees with (Max)SAT","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Université du Québec à Montréal","funders":"","keywords":"Trajectory; Computer science; Physics","score_opus":0.003044026244280377,"score_gpt":0.19083582949482242,"score_spread":0.18779180325054204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401024759","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":[0.75672704,0.00073361356,0.22108516,0.00015888101,0.000105570754,0.00006081215,7.771204e-7,0.0042053033,0.016922848],"genre_scores_gemma":[0.98878103,0.00007452389,0.0096525615,0.000014252856,0.000026840697,0.000008003225,0.000003115555,0.000029652076,0.0014100329],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952006,0.0000056786935,0.00009940072,0.00014292423,0.00006283823,0.00016910565],"domain_scores_gemma":[0.99977225,0.00007124015,0.0000041402764,0.000113813534,0.000008254323,0.000030284562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008088611,0.00010573059,0.00009720979,0.00010034596,0.000050584465,0.000031974178,0.00008913931,0.0001264727,0.00021763315],"category_scores_gemma":[0.0000072436083,0.00007811335,0.000028804223,0.00017797465,0.0000336825,0.0001181753,0.0000246649,0.00035517788,0.00031640404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038561266,0.0000136264025,0.0023400509,0.000051744115,0.00012583107,0.00021469941,0.00048520125,0.5417827,0.0027420495,0.019424774,0.0018598984,0.4309209],"study_design_scores_gemma":[0.00026131314,0.0002545636,0.0044267583,0.0001334615,0.000024686848,0.00013162829,0.00027626537,0.9119055,0.010004441,0.00087713473,0.07132354,0.00038067956],"about_ca_topic_score_codex":0.00000370766,"about_ca_topic_score_gemma":0.00002882352,"teacher_disagreement_score":0.43054023,"about_ca_system_score_codex":0.000036602985,"about_ca_system_score_gemma":0.000015085759,"threshold_uncertainty_score":0.40668395},"labels":[],"label_agreement":null},{"id":"W4401113673","doi":"10.1109/wfpst58552.2024.00020","title":"Deep Learning-based Road Object Detection for Collision Avoidance in Autonomous Driving","year":2024,"lang":"en","type":"article","venue":"","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":true,"ca_institutions":"Thales (Canada); Royal Military College of Canada; Université du Québec à Chicoutimi","funders":"","keywords":"Collision avoidance; Computer science; Object detection; Artificial intelligence; Object (grammar); Computer vision; Collision; Deep learning; Pattern recognition (psychology); Computer security","score_opus":0.0047453253931263595,"score_gpt":0.2124609636059868,"score_spread":0.20771563821286043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401113673","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":[0.36665305,0.00063417753,0.6282039,0.00007839444,0.0003278436,0.00024881389,7.764168e-7,0.0027224056,0.0011305826],"genre_scores_gemma":[0.99825895,0.000024501574,0.0012123793,0.000015071292,0.000035288973,0.00010905335,0.000004270028,0.00003864254,0.000301873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992506,0.000016852278,0.00020499987,0.00021909049,0.00005223592,0.0002562298],"domain_scores_gemma":[0.99969745,0.00012998747,0.00001229489,0.00012108846,0.00001306938,0.0000261002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023540593,0.00013254082,0.00014228439,0.00021539765,0.00008524513,0.00003148148,0.000090700036,0.00021252163,0.000027791682],"category_scores_gemma":[0.000049055856,0.00013538812,0.00006346785,0.00029708797,0.000023897317,0.00011183166,0.0000138659525,0.0003734532,0.0000527981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009885642,0.000010043168,0.0005159055,0.00009067128,0.000013620092,0.000009264321,0.00010351264,0.8155201,0.0053064623,0.0003098669,0.00001931587,0.17809136],"study_design_scores_gemma":[0.000207025,0.00006698178,0.0038372146,0.000047445425,0.000006338862,0.0000039870592,0.000040263825,0.95389056,0.036783546,0.00023284048,0.0047251647,0.00015861182],"about_ca_topic_score_codex":0.000021631895,"about_ca_topic_score_gemma":0.0007265014,"teacher_disagreement_score":0.63160586,"about_ca_system_score_codex":0.00023639039,"about_ca_system_score_gemma":0.000026514263,"threshold_uncertainty_score":0.5520967},"labels":[],"label_agreement":null},{"id":"W4401170725","doi":"10.3390/aerospace11080625","title":"Bidirectional Long Short-Term Memory Development for Aircraft Trajectory Prediction Applications to the UAS-S4 Ehécatl","year":2024,"lang":"en","type":"article","venue":"Aerospace","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trajectory; Computer science; Long short term memory; Perspective (graphical); Artificial intelligence; Artificial neural network; Key (lock); Machine learning; Term (time); Recurrent neural network","score_opus":0.013108942039665072,"score_gpt":0.23775740429037875,"score_spread":0.2246484622507137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401170725","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":[0.17495434,0.002176847,0.8123586,0.0021023327,0.0011479455,0.001435465,0.000060525384,0.0039599487,0.0018039901],"genre_scores_gemma":[0.99258345,0.000043626183,0.00334679,0.000057611567,0.00024044681,0.0016170416,0.000039255046,0.000040770905,0.002031016],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993216,0.0000066290763,0.00015620739,0.00021886459,0.00009111158,0.0002055928],"domain_scores_gemma":[0.9996438,0.00006825705,0.000007135147,0.0002098523,0.000023418983,0.00004755965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001664605,0.00012469685,0.00009008568,0.00007236866,0.00021299048,0.000027552918,0.00015223335,0.00012060167,0.000023929282],"category_scores_gemma":[0.0000055241,0.000107952554,0.000052147512,0.00022153022,0.000033834363,0.00007008073,0.000026883083,0.0001914533,0.00013954878],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049802282,0.00016770791,0.008978659,0.00068629405,0.0009573873,0.000012647169,0.006158812,0.09348685,0.03545604,0.0072548296,0.111393854,0.7353971],"study_design_scores_gemma":[0.00024300582,0.00007653102,0.1008331,0.00010696832,0.00009668764,0.000059901675,0.00030117933,0.029057277,0.06564576,0.0002191852,0.802765,0.0005953572],"about_ca_topic_score_codex":0.0000021666829,"about_ca_topic_score_gemma":0.00011824386,"teacher_disagreement_score":0.8176291,"about_ca_system_score_codex":0.00019784787,"about_ca_system_score_gemma":0.00005892697,"threshold_uncertainty_score":0.44021776},"labels":[],"label_agreement":null},{"id":"W4401174304","doi":"10.1155/2024/7504378","title":"Research on Intelligent Vehicle Operation Risk Assessment and Early Warning Based on Predictive Risk Field","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Warning system; Risk assessment; Computer science; Field (mathematics); Simulation; Engineering; Risk analysis (engineering); Computer security; Telecommunications; Business","score_opus":0.012661889351856215,"score_gpt":0.3111997239543661,"score_spread":0.29853783460250993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401174304","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":[0.8739653,0.00027693482,0.124951825,0.00019139111,0.00023235881,0.00011845302,0.000011032599,0.000088385736,0.00016429165],"genre_scores_gemma":[0.996864,0.0008551793,0.0021734172,0.000013023006,0.00005790543,0.00000857542,0.0000042681445,0.000016949185,0.0000066634543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991336,0.000063354404,0.0003058729,0.00012213564,0.00024696323,0.00012806398],"domain_scores_gemma":[0.99936664,0.00035815197,0.00006286666,0.0000803817,0.00008682675,0.000045154822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062307226,0.00009047525,0.00012292861,0.00028092874,0.000113196744,0.000028595981,0.000059425776,0.00010259487,0.000013935913],"category_scores_gemma":[0.000025866704,0.000079319994,0.000048201353,0.0001685599,0.000025718831,0.00026156043,0.0000011834564,0.001148463,0.0000036403628],"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":[0.00017360205,0.000026881338,0.006788481,0.000029864683,0.00004332867,0.0000290485,0.00088709145,0.90276825,0.00096413883,0.00046674002,0.000021568932,0.08780098],"study_design_scores_gemma":[0.0006072389,0.0027298075,0.609217,0.00040317676,0.00006323579,0.0000021249416,0.0006937445,0.36711335,0.01676615,0.0017154217,0.0005547154,0.00013404219],"about_ca_topic_score_codex":0.0000065809295,"about_ca_topic_score_gemma":0.00001274973,"teacher_disagreement_score":0.6024285,"about_ca_system_score_codex":0.00012591493,"about_ca_system_score_gemma":0.000040276536,"threshold_uncertainty_score":0.4989564},"labels":[],"label_agreement":null},{"id":"W4401414548","doi":"10.1109/icra57147.2024.10610614","title":"A Novel Benchmarking Paradigm and a Scale- and Motion-Aware Model for Egocentric Pedestrian Trajectory Prediction","year":2024,"lang":"en","type":"article","venue":"","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)","funders":"","keywords":"Pedestrian; Trajectory; Benchmarking; Computer science; Scale (ratio); Motion (physics); Artificial intelligence; Computer vision; Geography; Transport engineering; Engineering; Cartography","score_opus":0.012051407692781213,"score_gpt":0.20661646534146436,"score_spread":0.19456505764868315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401414548","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":[0.1647171,0.0011335715,0.8324132,0.00013446376,0.00012134077,0.00022417605,0.000051899253,0.0009341713,0.00027010049],"genre_scores_gemma":[0.9960815,0.0002429868,0.0034431918,0.0000121794565,0.000054778,0.000041607294,0.000013307682,0.000019939022,0.00009055385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994845,0.0000026356518,0.00012960586,0.00018676289,0.000038302554,0.00015823529],"domain_scores_gemma":[0.99982274,0.00004689862,0.0000068947793,0.00007358659,0.0000061191918,0.00004374434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008926351,0.00010454777,0.00009951613,0.000115064875,0.00008213338,0.000029934154,0.000033185937,0.00014590521,0.000007277542],"category_scores_gemma":[0.000003789704,0.00010127254,0.000026557205,0.00009765564,0.00003520135,0.00013068852,0.000013299381,0.00012737724,9.085177e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008834557,0.00013368718,0.014133995,0.002861787,0.00045993386,0.000017741035,0.0033464453,0.2708943,0.005570627,0.040497944,0.0025264341,0.65946877],"study_design_scores_gemma":[0.00028164734,0.000023319015,0.0029866656,0.000027840804,0.000030689214,0.00003564134,0.00003403428,0.9947575,0.00010346731,0.0013671522,0.00025332236,0.00009875331],"about_ca_topic_score_codex":0.00000725389,"about_ca_topic_score_gemma":0.0000423194,"teacher_disagreement_score":0.8313644,"about_ca_system_score_codex":0.000044463937,"about_ca_system_score_gemma":0.000015198101,"threshold_uncertainty_score":0.41297743},"labels":[],"label_agreement":null},{"id":"W4401415859","doi":"10.1109/icra57147.2024.10610911","title":"CRITERIA: a New Benchmarking Paradigm for Evaluating Trajectory Prediction Models for Autonomous Driving","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Huawei Technologies (Canada); University of Toronto","funders":"","keywords":"Benchmarking; Trajectory; Computer science; Artificial intelligence","score_opus":0.03318974438343467,"score_gpt":0.28721460443464886,"score_spread":0.25402486005121416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401415859","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":[0.027647883,0.0010550966,0.96389645,0.0002243456,0.0011321252,0.000792137,0.000032337444,0.0034814281,0.001738205],"genre_scores_gemma":[0.9313054,0.0000270471,0.06729711,0.000029658537,0.00043912756,0.00035601016,0.000036696376,0.000070712544,0.00043824912],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989053,0.000009834116,0.00032953965,0.0003112294,0.00007610834,0.00036801278],"domain_scores_gemma":[0.9994572,0.00026679516,0.000018390361,0.00017674705,0.000018626139,0.00006225233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042052363,0.00018731189,0.0001994276,0.00014466846,0.00014478786,0.00006883944,0.00012625402,0.00023099827,0.000066203356],"category_scores_gemma":[0.000026546228,0.00019378398,0.00013738126,0.00011520961,0.000021385975,0.00034293014,0.000019528421,0.00017628021,0.0000052097653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033498414,0.000020065512,0.000039553506,0.0006008231,0.00030318904,0.0000038843095,0.0012413239,0.47950587,0.010099533,0.06333498,0.011652884,0.4331644],"study_design_scores_gemma":[0.00031517446,0.00013323048,0.0000807435,0.00008819289,0.00006782614,0.000011209573,0.00003797152,0.9589778,0.0013386966,0.03460054,0.0041579106,0.00019069047],"about_ca_topic_score_codex":0.000007671035,"about_ca_topic_score_gemma":0.000019895402,"teacher_disagreement_score":0.9036575,"about_ca_system_score_codex":0.0001835159,"about_ca_system_score_gemma":0.00010353169,"threshold_uncertainty_score":0.7902281},"labels":[],"label_agreement":null},{"id":"W4401417197","doi":"10.1109/icra57147.2024.10611124","title":"DESTINE: Dynamic Goal Queries with Temporal Transductive Alignment for Trajectory Prediction","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Huawei Technologies (Canada); York University","funders":"","keywords":"Trajectory; Computer science; Artificial intelligence; Machine learning","score_opus":0.0041649680788965904,"score_gpt":0.19758071618204645,"score_spread":0.19341574810314985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401417197","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":[0.40930718,0.0005062713,0.58321935,0.00028367864,0.0003240858,0.00042625147,0.000076574965,0.0036945655,0.0021620523],"genre_scores_gemma":[0.9937533,0.00002121315,0.0054948344,0.000008403452,0.000031002844,0.00014336847,0.000028824516,0.00003165495,0.00048738893],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950767,0.0000046291075,0.00011782654,0.00015737237,0.000052410112,0.000160111],"domain_scores_gemma":[0.99984354,0.000026687308,0.0000053430836,0.00008918678,0.000010439447,0.000024790594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006436281,0.000118576754,0.000100095385,0.000060297818,0.000047254525,0.000012267953,0.00005068291,0.000099998084,0.000037179387],"category_scores_gemma":[0.0000015542556,0.00009535846,0.000037427722,0.00008833374,0.00007037639,0.00012160564,0.0000033703707,0.00012138099,0.0000075336275],"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":[0.0014493706,0.00046736392,0.010951406,0.004610509,0.004555738,0.00022508299,0.015232148,0.26188472,0.1459921,0.16665922,0.013757227,0.37421513],"study_design_scores_gemma":[0.0012226151,0.0011117071,0.010506844,0.00017648918,0.00021590664,0.00013617756,0.0014549325,0.8988228,0.057107083,0.0039768713,0.024526732,0.0007418506],"about_ca_topic_score_codex":0.0000066785956,"about_ca_topic_score_gemma":0.00008504876,"teacher_disagreement_score":0.6369381,"about_ca_system_score_codex":0.00011025093,"about_ca_system_score_gemma":0.000029146315,"threshold_uncertainty_score":0.38886052},"labels":[],"label_agreement":null},{"id":"W4401911926","doi":"10.1007/978-3-031-69042-6_1","title":"Formal Analysis of Vehicular Crash Severity Using KeYmaera X","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Concordia University","funders":"","keywords":"Computer science; Crash; Computer security; Programming language","score_opus":0.009913959001156842,"score_gpt":0.22179564892951867,"score_spread":0.21188168992836182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401911926","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":[0.020319512,0.00070927176,0.97576267,0.000036910358,0.00054368837,0.00011129643,0.000019236368,0.00028109792,0.0022163505],"genre_scores_gemma":[0.9707791,0.000035456782,0.028959703,0.00005936969,0.000076955446,0.0000012821902,0.0000065363465,0.000028163997,0.000053420128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985254,0.0000044457606,0.0003668692,0.00045692973,0.00028562854,0.0003607256],"domain_scores_gemma":[0.9992401,0.000073180636,0.00006643344,0.00051558646,0.000058314374,0.000046358127],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033204793,0.00028624086,0.00053780933,0.001154382,0.000077452016,0.000039948703,0.0006173999,0.00044689365,0.000046792913],"category_scores_gemma":[0.000009244616,0.0002772131,0.00019947441,0.00090813404,0.00045598936,0.0001610879,0.00032593682,0.0007854337,0.000009021294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013314311,0.0000039630286,0.00014477238,0.00006910187,0.00022392631,0.000039252645,0.00015790848,0.90819055,0.00031518773,0.0023727713,0.0000010552504,0.08848015],"study_design_scores_gemma":[0.0000424236,0.000017832579,0.00051138503,0.00009937556,0.00024583752,0.000013523953,1.1222424e-7,0.9805598,0.0016965157,0.016339323,0.00019770613,0.00027612902],"about_ca_topic_score_codex":0.000011896271,"about_ca_topic_score_gemma":0.000069857466,"teacher_disagreement_score":0.9504596,"about_ca_system_score_codex":0.00021969294,"about_ca_system_score_gemma":0.0000860459,"threshold_uncertainty_score":0.999968},"labels":[],"label_agreement":null},{"id":"W4402159103","doi":"10.1109/icc51166.2024.10622921","title":"A Novel Transformer-Based Model for Motion Forecasting in Connected Automated Vehicles","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Transformer; Artificial intelligence; Engineering; Electrical engineering; Voltage","score_opus":0.03046484222947822,"score_gpt":0.23887117455085688,"score_spread":0.20840633232137867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402159103","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":[0.21792495,0.00007511064,0.77471495,0.0002484732,0.00006170361,0.00025567878,0.000028432238,0.006044018,0.00064665417],"genre_scores_gemma":[0.9854094,0.000003025742,0.014345767,0.00003379174,0.000009644714,0.00009443866,0.000028091577,0.00003598584,0.000039881237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932754,0.000003110139,0.00022094771,0.00015926789,0.000043162476,0.00024597155],"domain_scores_gemma":[0.99978185,0.00009123337,0.000006807758,0.000080980106,0.000015749492,0.000023408926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013513169,0.00012288924,0.0001323222,0.00019084205,0.000036308997,0.00001701162,0.00007143071,0.00018692532,0.000010863973],"category_scores_gemma":[0.000015254364,0.000119589065,0.000054779634,0.00025263047,0.000022344248,0.00011336542,0.0000029704747,0.0001531541,0.000006068488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014194336,0.000025330013,0.000041292285,0.00021576391,0.000022797129,0.0000031613797,0.00018397547,0.93045086,0.036125142,0.0054894397,0.00012717013,0.027300848],"study_design_scores_gemma":[0.0005309741,0.000020040843,0.00013758538,0.000059675614,0.000010303668,0.0000044625936,0.000022579128,0.98402303,0.014109267,0.0008566914,0.000080726844,0.00014468221],"about_ca_topic_score_codex":0.000010190501,"about_ca_topic_score_gemma":0.00019897053,"teacher_disagreement_score":0.7674844,"about_ca_system_score_codex":0.00008420382,"about_ca_system_score_gemma":0.0000326046,"threshold_uncertainty_score":0.48767006},"labels":[],"label_agreement":null},{"id":"W4402438818","doi":"10.11159/htff24.105","title":"Design and Analysis of Drone for Foreign Object Debris (FOD) Detection in the Airport","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Drone; Aeronautics; Computer science; Debris; Object detection; Aerospace engineering; Engineering; Artificial intelligence; Meteorology; Geography","score_opus":0.006978660437861211,"score_gpt":0.20088686452911522,"score_spread":0.193908204091254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402438818","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":[0.99789184,0.00006607961,0.0010967343,0.00008078908,0.00023811607,0.00037733995,0.00001748434,0.00017549316,0.000056119807],"genre_scores_gemma":[0.99813294,0.00007181115,0.0016221522,0.00001057764,0.000031330135,0.000094360184,0.0000018541903,0.000023515266,0.000011468673],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992442,0.0000031599998,0.00029343762,0.00019073884,0.0000918639,0.00017659378],"domain_scores_gemma":[0.9996683,0.00014380901,0.00004539612,0.00009214356,0.00002627694,0.000024076597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003570599,0.00015679913,0.00031406403,0.00023888556,0.000032740612,0.000038991475,0.00019778662,0.00011623756,0.0000032184207],"category_scores_gemma":[0.00004574414,0.00011067188,0.00007849993,0.0004934874,0.000052429685,0.00006316766,0.000051814503,0.00016789972,9.006977e-8],"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":[0.00006641572,0.00001200093,0.000008522894,0.00042010186,0.00022016157,4.5451483e-7,0.000034234996,0.0017502651,0.9880956,0.00756727,0.000027314796,0.0017976514],"study_design_scores_gemma":[0.00012854766,0.0000336322,0.000031040505,0.000110837515,0.00021262094,0.0000037897462,0.000030497957,0.22244892,0.77604055,0.00071554934,0.00014967655,0.00009432056],"about_ca_topic_score_codex":0.0000031133156,"about_ca_topic_score_gemma":0.0000020900677,"teacher_disagreement_score":0.22069864,"about_ca_system_score_codex":0.000027172875,"about_ca_system_score_gemma":0.000004772276,"threshold_uncertainty_score":0.45130682},"labels":[],"label_agreement":null},{"id":"W4402473652","doi":"10.1109/ccece59415.2024.10667116","title":"Improvement of Image-based Lane Marker Detection Algorithm using Sensor Fusion","year":2024,"lang":"en","type":"article","venue":"","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":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Image fusion; Artificial intelligence; Fusion; Sensor fusion; Computer vision; Image (mathematics); Pattern recognition (psychology); Algorithm","score_opus":0.005367276760195232,"score_gpt":0.20708521636940355,"score_spread":0.2017179396092083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402473652","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":[0.35234627,0.000120357836,0.6451827,0.00003700804,0.0002402687,0.00009322203,0.000007546494,0.0008833636,0.0010892354],"genre_scores_gemma":[0.968075,0.000011341265,0.031722177,0.000012319657,0.000027443197,0.0000039883453,0.0000030120282,0.000019859457,0.00012486392],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995583,0.0000052503096,0.00014784157,0.00010658263,0.000060125534,0.00012187686],"domain_scores_gemma":[0.99981767,0.000019826628,0.000010020207,0.00011914239,0.00001594062,0.000017411337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010210432,0.00008376487,0.00008958137,0.000103271996,0.00002675153,0.000008315328,0.000039553724,0.000103246406,0.00017823113],"category_scores_gemma":[0.0000028217075,0.00007537521,0.00004191347,0.00013326737,0.00002569388,0.000051365412,0.000015863941,0.00012641269,0.00002440536],"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":[0.0000036367312,0.000007281316,0.00001586679,0.00008051346,0.000022462324,0.000008810446,0.000009836693,0.0014244898,0.6372523,0.000027755359,0.000021878544,0.3611252],"study_design_scores_gemma":[0.000073191564,0.000024497476,0.00016028374,0.000014846094,0.000010665867,0.0000041159224,0.000015923528,0.60313696,0.39600524,0.00005905468,0.00043806186,0.000057161196],"about_ca_topic_score_codex":0.00003819573,"about_ca_topic_score_gemma":0.000007644699,"teacher_disagreement_score":0.61572874,"about_ca_system_score_codex":0.00006842269,"about_ca_system_score_gemma":0.0000118563585,"threshold_uncertainty_score":0.30737117},"labels":[],"label_agreement":null},{"id":"W4402475488","doi":"10.1109/ccece59415.2024.10667209","title":"An Intelligent Emotional Real-Time Learning Controller for High-Speed Lane Keeping Assist System in Autonomous Vehicles<sup>*</sup>","year":2024,"lang":"en","type":"article","venue":"","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":"Carleton University","funders":"","keywords":"Controller (irrigation); Computer science; Real-time computing; Control engineering; Simulation; Engineering","score_opus":0.007890727306213981,"score_gpt":0.22118288968647365,"score_spread":0.21329216238025966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402475488","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":[0.86546695,0.00045912096,0.11371976,0.00038688286,0.00036175252,0.00093213783,0.00004481168,0.009007443,0.009621138],"genre_scores_gemma":[0.9964094,0.000020825204,0.0021095574,0.000024647605,0.00015853909,0.00005800742,0.00008282576,0.000079203026,0.0010570004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998402,0.00006074229,0.0005341535,0.00040407592,0.00012774792,0.0004712763],"domain_scores_gemma":[0.9993905,0.00023972774,0.000031822492,0.0002118295,0.00003755643,0.000088580535],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00059809594,0.000263763,0.00043017275,0.0003101402,0.00012896801,0.000082610815,0.00022181595,0.00034251856,0.00012893349],"category_scores_gemma":[0.000023083288,0.00026165834,0.0001055078,0.0002395713,0.000050242936,0.00018730514,0.000034219775,0.0004948108,0.00031912452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006588963,0.00006023123,0.0008737891,0.00042926986,0.00022554645,0.00006927913,0.00054288364,0.82996434,0.0054371017,0.118237734,0.0002769706,0.043816995],"study_design_scores_gemma":[0.0005423135,0.00010598907,0.002074693,0.00016779319,0.000026935813,0.00002892242,0.00035940853,0.993261,0.0010327306,0.00051887403,0.0015826903,0.00029869878],"about_ca_topic_score_codex":0.00010306798,"about_ca_topic_score_gemma":0.000016093783,"teacher_disagreement_score":0.16329664,"about_ca_system_score_codex":0.0005186943,"about_ca_system_score_gemma":0.000051704938,"threshold_uncertainty_score":0.99998355},"labels":[],"label_agreement":null},{"id":"W4402475740","doi":"10.1109/isci62787.2024.10668140","title":"Comparative Analysis of Machine Learning Models for Vision-Based Line Following Rovers","year":2024,"lang":"en","type":"article","venue":"","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Line (geometry); Artificial intelligence; Machine vision; Machine learning","score_opus":0.01841894959911017,"score_gpt":0.2731020711082675,"score_spread":0.25468312150915734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402475740","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":[0.10831865,0.0008010042,0.88617027,0.00008809352,0.00007825443,0.000111341,0.000025982477,0.00090035837,0.003506032],"genre_scores_gemma":[0.99444306,0.000007994153,0.005195652,0.000013906525,0.000006187495,0.000012209472,0.000057467703,0.000012273767,0.00025122816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999466,0.000009141871,0.00019692373,0.00013735262,0.00006109027,0.00012947046],"domain_scores_gemma":[0.9995947,0.00024547146,0.000014156345,0.00010731981,0.00001639408,0.000021967402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014746358,0.00010124289,0.0003122398,0.00032841414,0.00004267836,0.0000098227865,0.00007905716,0.00008761491,0.000046697016],"category_scores_gemma":[0.000008024746,0.00008997617,0.00026662095,0.00058755954,0.000020367494,0.000087264634,0.00001198863,0.00015682119,0.0000039615957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008543365,0.000008712444,0.00012209706,0.00003188025,0.0011736852,0.0000017176468,0.00015052258,0.99177974,0.0013250693,0.0033472988,0.000055739998,0.0019950117],"study_design_scores_gemma":[0.00015221824,0.0000517294,0.000051988125,0.000016733607,0.00045577897,8.397215e-8,0.00005641802,0.9914459,0.00658981,0.00040527107,0.00067780976,0.00009625252],"about_ca_topic_score_codex":0.000020071706,"about_ca_topic_score_gemma":0.000043338066,"teacher_disagreement_score":0.88612443,"about_ca_system_score_codex":0.00003650878,"about_ca_system_score_gemma":0.000017172533,"threshold_uncertainty_score":0.3669122},"labels":[],"label_agreement":null},{"id":"W4402513592","doi":"10.1155/2024/6126204","title":"A Lane Change Strategy to Enhance Traffic Safety in the Coexistence of Autonomous Vehicles and Manual Vehicles","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Korea Agency for Infrastructure Technology Advancement; Ministry of Land, Infrastructure and Transport","keywords":"Transport engineering; Computer science; Automotive engineering; Engineering; Aeronautics","score_opus":0.01122541407069507,"score_gpt":0.2648914074022929,"score_spread":0.25366599333159784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402513592","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":[0.99502236,0.0030674157,0.0011141935,0.00040476763,0.000109287044,0.00016160887,0.000022283208,0.000057085676,0.000040996587],"genre_scores_gemma":[0.9978141,0.0008277539,0.0012579185,0.000029647592,0.000038735747,0.000008878602,0.0000045599572,0.000012414028,0.0000059910335],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991757,0.000018619212,0.00045126904,0.000098657496,0.00012405285,0.00013166624],"domain_scores_gemma":[0.99970603,0.00008645714,0.00006648565,0.00007763212,0.000032393,0.00003098124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027129456,0.000104386076,0.00019881774,0.00015169365,0.000024229459,0.000009808153,0.00012673791,0.00007670804,0.0000036284125],"category_scores_gemma":[0.000005230838,0.000082539234,0.00004409612,0.00024984675,0.000046044017,0.00027745022,0.0000014370075,0.00028767824,0.000001357469],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022001144,0.00005544642,0.0004271491,0.00038785164,0.000055354034,0.00021944812,0.020152308,0.34720755,0.018391073,0.0022520171,0.000016389453,0.6106154],"study_design_scores_gemma":[0.0011797477,0.0016032221,0.94987094,0.0012114823,0.00013810593,0.00018704814,0.008411816,0.011532308,0.019173596,0.0023287286,0.0038337435,0.0005292758],"about_ca_topic_score_codex":0.0000047110866,"about_ca_topic_score_gemma":0.00016422861,"teacher_disagreement_score":0.94944376,"about_ca_system_score_codex":0.000034886623,"about_ca_system_score_gemma":0.000022040584,"threshold_uncertainty_score":0.33658522},"labels":[],"label_agreement":null},{"id":"W4402685928","doi":"10.2514/6.2024-4553","title":"Long-Term Trajectory Prediction Enhancement by Embedding Attention Mechanism in Long Short-Term Memory Model; Application to the UAS-S4 Ehécatl","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Term (time); Trajectory; Computer science; Mechanism (biology); Embedding; Artificial intelligence; Physics","score_opus":0.00743585527703631,"score_gpt":0.23769368463455345,"score_spread":0.23025782935751715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402685928","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":[0.4096432,0.00026059907,0.58785015,0.00017274597,0.00020737996,0.000460556,0.000008425461,0.000796004,0.0006009401],"genre_scores_gemma":[0.9979915,0.00014725761,0.00043602756,0.000045461817,0.000066603316,0.00047345468,0.000084032494,0.000041200605,0.0007144608],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883217,0.000018694622,0.00033949607,0.00036048077,0.0001577749,0.0002913596],"domain_scores_gemma":[0.9995737,0.000020090589,0.00001540575,0.00032831362,0.000014605389,0.000047899986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031152138,0.0001928739,0.00014453231,0.00017103991,0.00009060804,0.000039904076,0.00021312157,0.0002078909,0.000057332672],"category_scores_gemma":[0.0000024007961,0.00016850777,0.000060787785,0.00024478076,0.000021950978,0.00020666693,0.000052578325,0.00033565713,0.00009651051],"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":[0.000027437345,0.00014894204,0.002507354,0.00038866093,0.0001772796,0.000016981214,0.001369295,0.22061875,0.5535774,0.0059352065,0.001808646,0.21342403],"study_design_scores_gemma":[0.00010437373,0.000033745844,0.0056360043,0.000080448764,0.000031108175,0.000007838419,0.000061249775,0.9456797,0.047814008,0.00030104915,0.000048789887,0.00020168234],"about_ca_topic_score_codex":0.000007122906,"about_ca_topic_score_gemma":0.00011147624,"teacher_disagreement_score":0.72506094,"about_ca_system_score_codex":0.00034753492,"about_ca_system_score_gemma":0.000016855578,"threshold_uncertainty_score":0.68715477},"labels":[],"label_agreement":null},{"id":"W4402698361","doi":"10.1109/cvpr52733.2024.01427","title":"T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-Specific Token Memory","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Autoencoder; Security token; Trajectory; Computer science; Artificial intelligence; Test (biology); Encoder; Speech recognition; Deep learning; Computer network","score_opus":0.008074224029788525,"score_gpt":0.17907428837699746,"score_spread":0.17100006434720894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402698361","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":[0.8854812,0.0031738763,0.059398763,0.00028475738,0.0007677079,0.00038629805,0.0000781368,0.007547169,0.042882144],"genre_scores_gemma":[0.9969814,0.00012102262,0.0018084083,0.000012790644,0.00006205539,0.000010717228,0.000006584398,0.00003276319,0.00096428767],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992919,0.000010316881,0.0002384231,0.00018886605,0.00008295663,0.00018752052],"domain_scores_gemma":[0.99965215,0.00012385006,0.000013351879,0.00015693823,0.000011096198,0.00004258909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015582453,0.00013770521,0.00018444558,0.00014041705,0.000037785292,0.000012785341,0.000083334926,0.0002048634,0.00042941526],"category_scores_gemma":[0.00000788368,0.0001306068,0.000046412104,0.00014307904,0.00009343949,0.0001583952,0.000020353382,0.00025520995,0.00004571115],"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":[0.000027125529,0.000053866832,0.0006503504,0.0004350389,0.0003072367,0.0000481421,0.0067621088,0.0077817785,0.6167961,0.0018121398,0.0052026655,0.3601235],"study_design_scores_gemma":[0.00039450516,0.00013280772,0.020318827,0.00009089861,0.000048845308,0.00007885607,0.00047456476,0.92086595,0.048875626,0.0006266677,0.007749967,0.00034247467],"about_ca_topic_score_codex":0.0000029554951,"about_ca_topic_score_gemma":0.000002258396,"teacher_disagreement_score":0.9130842,"about_ca_system_score_codex":0.000048170517,"about_ca_system_score_gemma":0.000020093337,"threshold_uncertainty_score":0.5325991},"labels":[],"label_agreement":null},{"id":"W4402722024","doi":"10.1145/3675094.3678456","title":"Summary of SHL Challenge 2024: Motion Sensor-based Locomotion and Transportation Mode Recognition in Missing Data Scenarios","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Horizon 2020 Framework Programme","keywords":"Mode (computer interface); Motion (physics); Computer science; Motion sensors; Artificial intelligence; Computer vision; Human–computer interaction","score_opus":0.03089887097417312,"score_gpt":0.24803962861506232,"score_spread":0.2171407576408892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402722024","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":[0.7802777,0.0014579233,0.21403153,0.0011816362,0.00020625662,0.00030770252,0.00014158954,0.00086035166,0.0015352971],"genre_scores_gemma":[0.9968282,0.00030863768,0.0022801615,0.00000893857,0.00001480304,0.00000502588,0.00051603484,0.000017039816,0.00002118711],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993883,0.000017304714,0.0002295649,0.00019856237,0.000059770915,0.00010649942],"domain_scores_gemma":[0.9997439,0.000036218215,0.000015036517,0.0001727513,0.000012471726,0.000019660907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017036428,0.00009249837,0.000115173985,0.00018646737,0.00001959785,0.000009354989,0.000060395763,0.00016145063,0.000043997006],"category_scores_gemma":[0.0000058836063,0.00009835449,0.000016228903,0.00015220766,0.00003078404,0.0002774125,0.0000057887714,0.00018300118,0.0000056185204],"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":[0.000039755527,0.000111114554,0.0016700425,0.0015960782,0.00005258165,0.000045207053,0.0006828832,0.048829626,0.014038954,0.00071690464,0.0002932696,0.93192357],"study_design_scores_gemma":[0.00023364711,0.000022726284,0.0038242573,0.00025612375,0.000022383128,0.0000020020173,0.00010302888,0.989739,0.0041212495,0.0014095603,0.00014951495,0.000116502095],"about_ca_topic_score_codex":0.000074925156,"about_ca_topic_score_gemma":0.0003355543,"teacher_disagreement_score":0.9409094,"about_ca_system_score_codex":0.00003608156,"about_ca_system_score_gemma":0.000014350363,"threshold_uncertainty_score":0.40107796},"labels":[],"label_agreement":null},{"id":"W4402727019","doi":"10.1109/cvpr52733.2024.01376","title":"Producing and Leveraging Online Map Uncertainty in Trajectory Prediction","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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 Toronto","funders":"","keywords":"Trajectory; Computer science; Data mining; Artificial intelligence; Physics","score_opus":0.009128275291501979,"score_gpt":0.2064045974311459,"score_spread":0.1972763221396439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402727019","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":[0.9922105,0.001795615,0.0028636986,0.00035002906,0.00027577032,0.00008529751,0.0000063175385,0.0016764433,0.0007363043],"genre_scores_gemma":[0.9991339,0.00007763766,0.00051176467,0.000016999418,0.000049294576,0.000006064725,0.0000075391285,0.000012574636,0.00018425907],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958754,0.000005707573,0.000111631976,0.00014664982,0.000030098854,0.00011839094],"domain_scores_gemma":[0.99988097,0.000019435394,0.0000026591856,0.00007869704,0.0000032820906,0.000014927192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011067435,0.000067882625,0.00006928161,0.00011175993,0.000019344345,0.0000103612365,0.000031377618,0.00007242042,0.000027872928],"category_scores_gemma":[0.0000043745854,0.00006388615,0.000011030632,0.00010648316,0.000020669082,0.00008750612,0.000012826466,0.00023085147,0.0000073844376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001878071,0.00007468159,0.026749548,0.0012395027,0.00019732295,0.00014522574,0.010651601,0.52029455,0.013924572,0.0034025493,0.0030019262,0.42029974],"study_design_scores_gemma":[0.00011930146,0.0000143576,0.06485323,0.00009291763,0.000006633946,0.000016601149,0.00022497821,0.9306374,0.0007419479,0.0006612066,0.0025207256,0.00011066505],"about_ca_topic_score_codex":0.00004228062,"about_ca_topic_score_gemma":0.00016879766,"teacher_disagreement_score":0.42018908,"about_ca_system_score_codex":0.00006796673,"about_ca_system_score_gemma":0.000010915691,"threshold_uncertainty_score":0.26052016},"labels":[],"label_agreement":null},{"id":"W4402727728","doi":"10.1109/cvpr52733.2024.01410","title":"Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Backdoor; Trajectory; Adversarial system; Computer science; Computer security; Artificial intelligence; Machine learning; Physics","score_opus":0.01649745150454939,"score_gpt":0.24518688104410846,"score_spread":0.22868942953955906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402727728","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":[0.92871094,0.0025828788,0.019006796,0.0008088523,0.0048566577,0.00053894846,0.00037943965,0.009715748,0.033399716],"genre_scores_gemma":[0.9983551,0.00003785937,0.0004588432,0.00003900917,0.00012797616,0.0000100498155,0.00020760018,0.00004022786,0.0007233374],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894387,0.000020638545,0.00026606864,0.00037646818,0.000100291465,0.0002926594],"domain_scores_gemma":[0.9993903,0.00013133165,0.0000118851995,0.0004199104,0.000005538269,0.00004100561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021563993,0.00017757156,0.00016337328,0.00016796768,0.00005228322,0.000040326107,0.00033151582,0.0002618949,0.00024876365],"category_scores_gemma":[0.00004054625,0.00017449073,0.00003138119,0.00022583053,0.000040719373,0.00033541955,0.00007938157,0.00066729664,0.00021409417],"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":[0.00013904407,0.0002759841,0.008939027,0.0007691839,0.00077818264,0.0005377285,0.0019230021,0.25282225,0.026088038,0.019163592,0.45729706,0.23126692],"study_design_scores_gemma":[0.0002873886,0.00004352613,0.003770566,0.00011561998,0.000020046093,0.000013643898,0.00006576595,0.9650227,0.0007537961,0.00006701321,0.029606765,0.00023321329],"about_ca_topic_score_codex":0.000031429536,"about_ca_topic_score_gemma":0.00012132977,"teacher_disagreement_score":0.7122004,"about_ca_system_score_codex":0.0002584207,"about_ca_system_score_gemma":0.00003556974,"threshold_uncertainty_score":0.71155256},"labels":[],"label_agreement":null},{"id":"W4402754005","doi":"10.1109/cvpr52733.2024.01409","title":"CaDeT: A Causal Disentanglement Approach for Robust Trajectory Prediction in Autonomous Driving","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Cadet; Trajectory; Computer science; Artificial intelligence; Control theory (sociology)","score_opus":0.012456704977745488,"score_gpt":0.21218413105779674,"score_spread":0.19972742608005126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402754005","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":[0.12465035,0.00084607536,0.8559527,0.00015747626,0.0005880934,0.0007427482,0.000045730983,0.00391501,0.013101815],"genre_scores_gemma":[0.9897949,0.000018446273,0.009161091,0.0000116807705,0.00008654615,0.000255282,0.000040333525,0.000038422488,0.0005933105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992147,0.000007859703,0.00022833585,0.00022440417,0.000057742545,0.0002669587],"domain_scores_gemma":[0.9997931,0.000033878037,0.000007161225,0.0001262223,0.0000052365226,0.00003438883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015829095,0.00013225258,0.00013052276,0.00016814878,0.00004078909,0.000020813182,0.00008839129,0.00016066426,0.00006603795],"category_scores_gemma":[0.0000042640395,0.0001294713,0.00005491462,0.0001593442,0.000026817115,0.000112599744,0.000018948598,0.0002297377,0.000011735872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021007312,0.00017647057,0.012122348,0.0007635516,0.0002718944,0.000041333085,0.0017086451,0.8683614,0.004159824,0.020831326,0.0077200974,0.08382211],"study_design_scores_gemma":[0.00021181379,0.000033418222,0.0045485473,0.000023014552,0.000017416656,0.000009714982,0.00019089293,0.9907423,0.0009655108,0.00013936096,0.0029755386,0.00014246364],"about_ca_topic_score_codex":0.000012298671,"about_ca_topic_score_gemma":0.00008473968,"teacher_disagreement_score":0.86514455,"about_ca_system_score_codex":0.0002941857,"about_ca_system_score_gemma":0.000030223488,"threshold_uncertainty_score":0.52796865},"labels":[],"label_agreement":null},{"id":"W4402927795","doi":"10.1016/j.tre.2024.103780","title":"Safety aware neural network for connected and automated vehicle operations","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Center for Medicare and Medicaid Innovation; Division of Civil, Mechanical and Manufacturing Innovation; National Science Foundation","keywords":"Artificial neural network; Computer science; Vehicle safety; Transport engineering; Engineering; Automotive engineering; Artificial intelligence","score_opus":0.05183170274479722,"score_gpt":0.343027503165378,"score_spread":0.2911958004205808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402927795","genre_codex":"review","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":[0.17722835,0.4397256,0.3405308,0.011548945,0.0012854332,0.009954495,0.007991823,0.011254312,0.00048020866],"genre_scores_gemma":[0.9122119,0.08395778,0.0010654322,0.0001276956,0.00005638833,0.00032841627,0.0021549321,0.000040680192,0.000056784924],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985192,0.000050986273,0.0005663524,0.00032296818,0.00017969318,0.00036083814],"domain_scores_gemma":[0.99922574,0.00030070555,0.000018781748,0.00014418358,0.00019248365,0.00011809209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060876267,0.0001842272,0.00030816294,0.00009257751,0.0003166362,0.00005504581,0.00008149589,0.00015049712,0.00007134882],"category_scores_gemma":[0.000024871968,0.00017514941,0.00006080359,0.00048324745,0.00020017072,0.000153156,0.0000012747726,0.00035012898,0.000007245064],"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":[0.00014862795,0.0001095592,0.0054137004,0.036730055,0.0006507329,0.00021137808,0.0011790388,0.34026772,0.00037743428,0.50098217,0.027818829,0.08611076],"study_design_scores_gemma":[0.0006163481,0.00015131477,0.056810357,0.0018525318,0.0002602079,0.0000035151982,0.000066973334,0.8348777,0.000045649766,0.0017428317,0.10316816,0.0004043628],"about_ca_topic_score_codex":0.000038151575,"about_ca_topic_score_gemma":0.0011796125,"teacher_disagreement_score":0.73498356,"about_ca_system_score_codex":0.000021965863,"about_ca_system_score_gemma":0.000052885982,"threshold_uncertainty_score":0.7142385},"labels":[],"label_agreement":null},{"id":"W4402948736","doi":"10.1061/jtepbs.teeng-8332","title":"Stopping Sight Distance Requirements for Basic Turbo Roundabouts","year":2024,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","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":true,"ca_institutions":"University of Toronto; WSP (Canada); Thornhill Medical (Canada)","funders":"","keywords":"Sight; Computer science; Turbo; Engineering; Automotive engineering; Physics","score_opus":0.013086521050069749,"score_gpt":0.22293457170909015,"score_spread":0.2098480506590204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402948736","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":[0.13027593,0.010041186,0.8530801,0.000089164685,0.005503979,0.00027722592,0.000069499314,0.00059827685,0.00006464384],"genre_scores_gemma":[0.99860156,0.000106165055,0.000765626,0.0000035711525,0.0003215175,0.000029457702,0.00001713129,0.000047888298,0.00010709064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988455,0.0000059883228,0.00066473964,0.0001085614,0.00016584604,0.00020932891],"domain_scores_gemma":[0.99962395,0.00006656263,0.0000774996,0.00010112624,0.00006880019,0.000062084604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003174075,0.00015698039,0.0002717316,0.00018933302,0.00003824506,0.000053218708,0.00012664999,0.00011457565,0.0000059372737],"category_scores_gemma":[0.0000088688785,0.00015002502,0.00013855059,0.00018883726,0.000011666226,0.00034161922,8.929034e-7,0.00023026745,0.0000041177336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019509005,0.000013846297,0.00030716055,0.0016880083,0.00034398222,0.000070047536,0.0007314147,0.98235977,0.005731827,0.0061317594,0.0009653912,0.0016373156],"study_design_scores_gemma":[0.00070060283,0.00012379629,0.0021950088,0.0019299522,0.00017745055,0.000070995215,0.00013582493,0.5475848,0.0034337915,0.0000921118,0.4431186,0.0004370726],"about_ca_topic_score_codex":0.0000010815917,"about_ca_topic_score_gemma":0.0000032041098,"teacher_disagreement_score":0.8683256,"about_ca_system_score_codex":0.00013428771,"about_ca_system_score_gemma":0.00003172683,"threshold_uncertainty_score":0.6117843},"labels":[],"label_agreement":null},{"id":"W4402968356","doi":"10.1109/ap-s/inc-usnc-ursi52054.2024.10686886","title":"Driver Distraction Detection using mm- Wave FMCW Radar","year":2024,"lang":"en","type":"article","venue":"","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":"University of Waterloo","funders":"","keywords":"Distraction; Computer science; Radar; Continuous-wave radar; Radar engineering details; Radar detection; Radar imaging; Telecommunications; Psychology","score_opus":0.011539590373119503,"score_gpt":0.21047778135929565,"score_spread":0.19893819098617616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402968356","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":[0.70979095,0.00019163822,0.2804855,0.000049478775,0.00070884486,0.000057553185,0.0000020350424,0.0025992477,0.0061147315],"genre_scores_gemma":[0.99824435,0.000025861924,0.0013511686,0.0000071290233,0.00006823677,0.0000023378739,0.000002101544,0.000018247947,0.0002805723],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963,0.0000045543384,0.000095084986,0.00010739581,0.00004281397,0.00012016681],"domain_scores_gemma":[0.99986327,0.000014588554,0.0000051003553,0.00009246168,0.000005753481,0.00001880048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000048695892,0.00007629313,0.000059659196,0.000069133646,0.000055188786,0.000019411606,0.000028442837,0.00013423209,0.00015725248],"category_scores_gemma":[0.0000026018538,0.00007330707,0.0000360719,0.00012348205,0.000021821475,0.00016651714,0.000009689528,0.00020964557,0.00012204153],"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":[0.000009069939,0.000017300576,0.00012980273,0.00012710073,0.00015176291,0.0000939612,0.00019852133,0.010054276,0.5228166,0.007191206,0.0003426526,0.45886773],"study_design_scores_gemma":[0.00006060251,0.000013401084,0.0019705847,0.000016083757,0.000024783472,0.0000978123,0.00005434077,0.8308969,0.1520257,0.0011098769,0.013575002,0.00015488156],"about_ca_topic_score_codex":0.000018963116,"about_ca_topic_score_gemma":0.000026225442,"teacher_disagreement_score":0.8208426,"about_ca_system_score_codex":0.00012484245,"about_ca_system_score_gemma":0.0000068488453,"threshold_uncertainty_score":0.29893756},"labels":[],"label_agreement":null},{"id":"W4403021814","doi":"10.1109/tase.2024.3466272","title":"Fast and Accurate Multi-Agent Trajectory Prediction for Crowded Unknown Scenes","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Autonomous Vehicle Technology and Safety","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 Victoria","funders":"National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Artificial intelligence; Computer vision; Physics","score_opus":0.015576962242451144,"score_gpt":0.23940511889125216,"score_spread":0.223828156648801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403021814","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":[0.127363,0.00029115114,0.8696284,0.000058983824,0.0007220763,0.00023220154,0.000027990127,0.0016568812,0.000019313637],"genre_scores_gemma":[0.99650604,0.00019523592,0.0031034115,0.000011115312,0.00001785295,0.00010237332,0.000001205661,0.000018276405,0.00004450939],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930584,0.0000034942852,0.00016733776,0.00021528557,0.00011331029,0.0001947398],"domain_scores_gemma":[0.9997404,0.000057055335,0.000009043152,0.000094606636,0.000035676578,0.00006318697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027401443,0.00012539858,0.000106025036,0.00036544338,0.00021067323,0.00010453216,0.00006943327,0.00008466797,0.0000037337088],"category_scores_gemma":[0.000009523184,0.0001228577,0.000027538126,0.0003928206,0.00009567255,0.0005227829,0.0000010441216,0.00014442146,0.0000048854085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004054036,0.000016748156,0.0000035470903,0.0011435092,0.000037892038,0.0000015393587,0.0007553766,0.8240486,0.1050718,0.00039518782,0.000019487816,0.06850226],"study_design_scores_gemma":[0.0001447448,0.000028287373,0.00086228416,0.00016493067,0.000021271342,0.000013865786,0.000046245692,0.9595898,0.03875234,0.000009221904,0.00025045194,0.000116568335],"about_ca_topic_score_codex":0.0000010604389,"about_ca_topic_score_gemma":0.0000029630698,"teacher_disagreement_score":0.869143,"about_ca_system_score_codex":0.00009138284,"about_ca_system_score_gemma":0.000030481462,"threshold_uncertainty_score":0.50099915},"labels":[],"label_agreement":null},{"id":"W4403332258","doi":"10.1109/icfec61590.2024.00012","title":"A Real-World Testbed for V2X in Autonomous Vehicles: From Simulation to Actual Road Testing","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Ontario Tech University","funders":"","keywords":"Testbed; Computer science; Simulation; Real-time computing; Computer network","score_opus":0.024884279803328867,"score_gpt":0.27587007772943767,"score_spread":0.2509857979261088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403332258","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":[0.9532037,0.000090817906,0.03595345,0.00031410754,0.00023185486,0.0005051273,0.00003514855,0.0039102267,0.0057555824],"genre_scores_gemma":[0.9836433,0.0000021829178,0.015613269,0.00006448018,0.00010007687,0.00008696153,0.000019159052,0.0000495107,0.000421075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990468,0.000006805284,0.0003131209,0.00028370222,0.0000609342,0.0002886382],"domain_scores_gemma":[0.99903476,0.0006785264,0.000012536184,0.00019716471,0.000021819415,0.00005518837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015884123,0.00016481496,0.00019268853,0.00028516707,0.00004490851,0.000040780957,0.00013758619,0.00015352233,0.000042641524],"category_scores_gemma":[0.00012359153,0.00016759141,0.00004224254,0.0005543697,0.00001759461,0.00014040209,0.00003689839,0.00021689078,0.00012811176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020598169,0.00002313459,0.0028861274,0.00005470752,0.000044041506,0.00002047916,0.00068486744,0.6839446,0.010841731,0.0023579476,0.00035054435,0.29877126],"study_design_scores_gemma":[0.00020103589,0.000041054172,0.022564322,0.000069554444,0.000011718672,6.563034e-7,0.000059700335,0.9693634,0.0029395602,0.0022380755,0.0022990135,0.00021191729],"about_ca_topic_score_codex":0.00052542693,"about_ca_topic_score_gemma":0.0009957355,"teacher_disagreement_score":0.29855934,"about_ca_system_score_codex":0.00023504838,"about_ca_system_score_gemma":0.000045591318,"threshold_uncertainty_score":0.683418},"labels":[],"label_agreement":null},{"id":"W4403511942","doi":"10.1109/iv64223.2024.00032","title":"Visual Analytics of Motor Vehicle Accidents","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visual analytics; Computer science; Analytics; Visualization; Automotive engineering; Human–computer interaction; Data science; Artificial intelligence; Engineering","score_opus":0.0064920051131033335,"score_gpt":0.23516549016286095,"score_spread":0.22867348504975762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403511942","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":[0.9774164,0.0004988935,0.009826074,0.000076556724,0.00020840924,0.000051143,0.0000039614097,0.0012778637,0.010640724],"genre_scores_gemma":[0.99876857,0.000039810733,0.00030802778,0.000011509801,0.000020895159,0.0000021720007,0.0000013137244,0.0000138400055,0.0008338368],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996338,0.0000027570352,0.00013068163,0.00007290113,0.00005329759,0.00010658125],"domain_scores_gemma":[0.9998453,0.000023201896,0.0000047943945,0.00009734177,0.000008776012,0.000020566364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000051274365,0.000059704595,0.000087178174,0.0000819211,0.000011476534,0.0000074793634,0.000086814536,0.00008791818,0.00021228801],"category_scores_gemma":[0.00000501947,0.000054722394,0.000040552233,0.00015598667,0.000025168538,0.00006400133,0.000023717685,0.00010969049,0.00015790066],"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":[0.00004612392,0.00021949988,0.04231096,0.0011672928,0.00152373,0.00022929037,0.00087174214,0.023798402,0.34608653,0.13915785,0.017083526,0.42750505],"study_design_scores_gemma":[0.00010907721,0.000051314528,0.015460228,0.000026537926,0.000026887717,0.0000052591067,0.000052927797,0.8992226,0.079186834,0.001245104,0.0044712657,0.00014192887],"about_ca_topic_score_codex":0.000005556646,"about_ca_topic_score_gemma":0.0000051958477,"teacher_disagreement_score":0.8754242,"about_ca_system_score_codex":0.00002356302,"about_ca_system_score_gemma":0.000009246985,"threshold_uncertainty_score":0.23244049},"labels":[],"label_agreement":null},{"id":"W4403537148","doi":"10.2139/ssrn.4992077","title":"Machine Learning-Based Prediction of Mini Flash Crashes","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Flash (photography); Computer science; Machine learning; Artificial intelligence; Art","score_opus":0.005198393377737983,"score_gpt":0.19687171130617864,"score_spread":0.19167331792844067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403537148","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":[0.8415803,0.08423841,0.060883395,0.0012267198,0.002940512,0.0004903577,0.00016222283,0.003185009,0.0052930904],"genre_scores_gemma":[0.99189866,0.006751481,0.00013137412,0.000006565499,0.0002116295,0.000012588519,0.00004393521,0.000080208876,0.00086353574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978152,0.000053179476,0.0004996942,0.0002356871,0.0001927481,0.0012034778],"domain_scores_gemma":[0.9994695,0.00003869707,0.00014532548,0.0002466743,0.000052007083,0.000047793572],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00096023286,0.0002962884,0.00037967318,0.00032516528,0.000080839935,0.000024594874,0.00036592947,0.00061365793,0.00004575299],"category_scores_gemma":[0.000035564957,0.00029004933,0.00025100255,0.0001418643,0.000075077456,0.000032333053,0.00015258422,0.011323398,0.000053090334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009512957,0.00011092887,0.0061166743,0.0007963655,0.0024367932,0.000033347493,0.00026713195,0.86535597,0.0016810586,0.023244198,0.0005851616,0.099277236],"study_design_scores_gemma":[0.00072927884,0.00052208593,0.00097520184,0.0004460597,0.00040726372,0.00030410022,0.00015887505,0.60622793,0.0033516865,0.37868902,0.0076532788,0.0005351913],"about_ca_topic_score_codex":0.00001940437,"about_ca_topic_score_gemma":0.00021755593,"teacher_disagreement_score":0.35544482,"about_ca_system_score_codex":0.0009272356,"about_ca_system_score_gemma":0.0013457425,"threshold_uncertainty_score":0.9999552},"labels":[],"label_agreement":null},{"id":"W4403596208","doi":"10.1016/j.aej.2024.10.039","title":"Safedrive dreamer: Navigating safety–critical scenarios in autonomous driving with world models","year":2024,"lang":"en","type":"article","venue":"Alexandria Engineering Journal","topic":"Autonomous Vehicle Technology and Safety","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":"Hudbay Minerals (Canada); University of Toronto","funders":"","keywords":"Computer science; Environmental science; Transport engineering; Engineering","score_opus":0.005039370212651424,"score_gpt":0.2106552255415381,"score_spread":0.20561585532888668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403596208","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":[0.3996864,0.0058264495,0.58390105,0.0010919294,0.0019108175,0.00032021795,0.000010909961,0.0034621565,0.0037900445],"genre_scores_gemma":[0.9831854,0.00023742244,0.01603639,0.000021295764,0.00027657408,0.000018772567,0.000003999659,0.0001320206,0.00008811994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801075,0.000024388495,0.0006238589,0.0003191093,0.00023334955,0.0007885184],"domain_scores_gemma":[0.99917084,0.00031353315,0.00002938884,0.00024155663,0.00003839246,0.00020629917],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00042741376,0.0003880994,0.00041827952,0.00047372413,0.0001472108,0.00019333867,0.0002988544,0.00020640688,0.00010688444],"category_scores_gemma":[0.0000479102,0.00036684025,0.00011004773,0.00070803915,0.00008192824,0.00069645146,0.000056367433,0.0025590467,0.00003625741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013309621,0.000017205124,0.0006555239,0.00009516321,0.00010979282,0.00091407413,0.0006638567,0.9756889,0.0011837246,0.008381815,0.00009075663,0.012185899],"study_design_scores_gemma":[0.00042182798,0.000057435213,0.0009510073,0.0017956055,0.000041720257,0.0015468356,0.00008798728,0.9915474,0.00055814034,0.0005870118,0.0019032643,0.0005017882],"about_ca_topic_score_codex":0.0000074812638,"about_ca_topic_score_gemma":0.0000652784,"teacher_disagreement_score":0.583499,"about_ca_system_score_codex":0.0004649501,"about_ca_system_score_gemma":0.00009991234,"threshold_uncertainty_score":0.99987835},"labels":[],"label_agreement":null},{"id":"W4403675034","doi":"10.1109/biorob60516.2024.10719798","title":"Sequential Image Classification of Human-Robot Walking Environments Using Temporal Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Robot; Computer vision; Image (mathematics); Pattern recognition (psychology)","score_opus":0.025415521516097463,"score_gpt":0.2620034193539944,"score_spread":0.23658789783789694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403675034","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":[0.5819777,0.00031111605,0.4153739,0.000028535656,0.00027229925,0.000089718196,0.0000022819486,0.0004955547,0.001448877],"genre_scores_gemma":[0.9982089,0.000011124464,0.0015672429,0.000005740693,0.00006288054,0.0000035552507,0.000016672642,0.000027608927,0.0000962746],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936366,0.000014222361,0.00023726455,0.00014565788,0.000067390894,0.00017182392],"domain_scores_gemma":[0.9997793,0.000012698239,0.00002178965,0.0001608253,0.0000034377654,0.00002194343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000098951015,0.00010761296,0.00011673288,0.000077254146,0.00005651032,0.000018091556,0.00010290558,0.00014335556,0.00012637902],"category_scores_gemma":[0.0000014119837,0.00010811235,0.000053649783,0.00010313977,0.00008213394,0.00016567494,0.000036279576,0.00022194687,0.000011608596],"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":[0.000003454778,0.000017809267,0.0016545126,0.000064198095,0.000078364734,0.000020761256,0.000100801306,0.1405507,0.84092176,0.0032098193,0.00009452074,0.0132833],"study_design_scores_gemma":[0.000069217494,0.000012398567,0.0038664732,0.000016349912,0.000021416254,0.000007995149,0.00003236419,0.97872,0.016843004,0.00017912487,0.00012300549,0.00010866751],"about_ca_topic_score_codex":0.000015192903,"about_ca_topic_score_gemma":0.000005481948,"teacher_disagreement_score":0.8381693,"about_ca_system_score_codex":0.000077052224,"about_ca_system_score_gemma":0.000004855235,"threshold_uncertainty_score":0.44086936},"labels":[],"label_agreement":null},{"id":"W4403689389","doi":"10.1038/s41598-024-71187-8","title":"Federated learning system on autonomous vehicles for lane segmentation","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Science and Technology Development Fund","keywords":"Computer science; Segmentation; Artificial intelligence","score_opus":0.00924405427733324,"score_gpt":0.2259858777981672,"score_spread":0.21674182352083396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403689389","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":[0.9351761,0.0007834939,0.034170285,0.00018510637,0.013579057,0.00069231424,0.000007317608,0.007977042,0.007429307],"genre_scores_gemma":[0.9965435,0.0000021120545,0.0004329665,0.000005344462,0.000055415407,0.00006923228,0.00008668427,0.000030244442,0.0027745017],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989506,0.000014032406,0.0002950934,0.00038358185,0.0001285512,0.00022813179],"domain_scores_gemma":[0.99967,0.000045418536,0.00003815679,0.0001734815,0.000033869608,0.00003905527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065381615,0.000118277836,0.00012450234,0.00016965484,0.00034635002,0.00027134398,0.000048839775,0.00011200521,0.000016748818],"category_scores_gemma":[0.000021809023,0.00011142025,0.00005746582,0.00024828888,0.000053191397,0.00010599369,0.00001284757,0.00018003203,0.00009400095],"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":[0.000037851423,0.00007652935,0.00096605596,0.002268778,0.00037718134,0.0022555932,0.0016464957,0.19900125,0.3016602,0.013320224,0.060484655,0.41790518],"study_design_scores_gemma":[0.00015347218,0.00008881379,0.00020519704,0.0002541909,0.000040827836,0.00038886172,0.00047230977,0.68556935,0.16929223,0.001693637,0.14148211,0.00035898204],"about_ca_topic_score_codex":0.0000032954722,"about_ca_topic_score_gemma":0.0000048052057,"teacher_disagreement_score":0.48656812,"about_ca_system_score_codex":0.00018196435,"about_ca_system_score_gemma":0.000046635738,"threshold_uncertainty_score":0.4543586},"labels":[],"label_agreement":null},{"id":"W4403791025","doi":"10.55092/aias20240007","title":"Developing a Machine Intelligence Quotient (MIQ) for evaluating autonomous vehicle intelligence: A Conceptual Framework","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence and Autonomous Systems","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":"Zymeworks (Canada); Simon Fraser University","funders":"","keywords":"Quotient; Computer science; Artificial intelligence; Natural language processing; Mathematics; Combinatorics","score_opus":0.06750144049552453,"score_gpt":0.32210339883454187,"score_spread":0.25460195833901733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403791025","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":[0.05499013,0.014548818,0.92279613,0.0005146825,0.0029730522,0.0013910152,0.00009575134,0.0022987851,0.00039160368],"genre_scores_gemma":[0.9895063,0.00047829747,0.008682667,0.00010503518,0.00039230374,0.00050177926,0.000035295205,0.00013104962,0.0001672853],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959036,0.000100753605,0.0016134061,0.0010006496,0.00031638448,0.0010652199],"domain_scores_gemma":[0.9979359,0.000991862,0.00013908374,0.0005441918,0.00017254362,0.00021645438],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013328887,0.0006533968,0.00072999584,0.0003854127,0.00054740894,0.0004602717,0.00060638,0.000607469,0.000097053824],"category_scores_gemma":[0.00024390659,0.00064781785,0.00021401334,0.00075096806,0.0005431975,0.0003977138,0.00017057036,0.00087702647,0.00035112313],"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":[0.000025423275,0.000035543784,0.00003993827,0.0003857658,0.000146317,0.000026801887,0.004052339,0.0679979,0.0006962759,0.529202,0.000036796555,0.39735493],"study_design_scores_gemma":[0.000023278757,0.00030950457,0.000011288278,0.00062995625,0.0000726689,0.000079699734,0.005615976,0.87966347,0.031363323,0.07602038,0.005406878,0.00080355204],"about_ca_topic_score_codex":0.00018937724,"about_ca_topic_score_gemma":0.00007037293,"teacher_disagreement_score":0.93451613,"about_ca_system_score_codex":0.00050072663,"about_ca_system_score_gemma":0.00031024896,"threshold_uncertainty_score":0.9995973},"labels":[],"label_agreement":null},{"id":"W4403792789","doi":"10.1155/2024/7129562","title":"Vehicle Lane Change Multistep Trajectory Prediction Based on Data and CNN_BiLSTM Model","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Artificial intelligence","score_opus":0.02138539849660014,"score_gpt":0.24344984496736058,"score_spread":0.22206444647076043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403792789","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":[0.8878863,0.0019281855,0.10862467,0.00025590963,0.00048465305,0.00014902597,0.00027404653,0.00032124115,0.00007600337],"genre_scores_gemma":[0.9936576,0.00041344255,0.005713676,0.000031369316,0.00007630769,0.0000042877077,0.000077226454,0.000019667834,0.0000064214146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994307,0.0000065576264,0.0002485412,0.00011231615,0.000111500485,0.00009039077],"domain_scores_gemma":[0.9997348,0.00003381956,0.00003933388,0.0001325154,0.00002215594,0.000037378897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013148403,0.00008781372,0.00011675198,0.00012981262,0.0000283159,0.000008369053,0.00008912861,0.000086467706,0.000005066591],"category_scores_gemma":[0.0000045017923,0.0000814527,0.000027938086,0.000089189685,0.000021702706,0.00047298282,0.0000012102971,0.00026602423,0.0000013481881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009805943,0.00002986574,0.000715646,0.0001699572,0.000035897883,0.000055617526,0.0007097514,0.889055,0.008221284,0.00018209041,0.00006333738,0.1006635],"study_design_scores_gemma":[0.00048279116,0.00011605755,0.05061759,0.00013870565,0.00004579887,0.0000068564696,0.000054314263,0.94666433,0.00075266283,0.00021891244,0.0008252261,0.00007674953],"about_ca_topic_score_codex":9.5303955e-7,"about_ca_topic_score_gemma":0.000019459641,"teacher_disagreement_score":0.10577133,"about_ca_system_score_codex":0.00003725147,"about_ca_system_score_gemma":0.00001777698,"threshold_uncertainty_score":0.33215445},"labels":[],"label_agreement":null},{"id":"W4403821286","doi":"10.1155/2024/5793435","title":"Reconstruction of the Motion of Traffic Accident Vehicle in the Vehicle‐Mounted Video Based on Direct Linear Transform","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Vehicle accident; Computer science; Motion (physics); Traffic accident; Computer vision; Artificial intelligence; Engineering; Transport engineering; Medicine","score_opus":0.005011396918685546,"score_gpt":0.22131680382245156,"score_spread":0.216305406903766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403821286","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":[0.9961031,0.00026322153,0.0025511323,0.00041830333,0.0003619535,0.00017307069,0.000011228972,0.00004386636,0.0000741005],"genre_scores_gemma":[0.9994476,0.00013909105,0.000349232,0.000013570975,0.000026148376,0.000004661306,0.000003758064,0.000013581596,0.0000023538237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989846,0.000036155496,0.0005987961,0.000077881916,0.00020747955,0.00009511091],"domain_scores_gemma":[0.9995644,0.00011058964,0.00013733405,0.00011598411,0.000057991016,0.000013687693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034068516,0.00009722498,0.00018718406,0.00018003176,0.000028277196,0.00000410437,0.00014227399,0.00009881321,0.000008327073],"category_scores_gemma":[0.000013620309,0.00006435055,0.00015501084,0.00042519282,0.000051622985,0.00023546959,4.5055688e-7,0.00037950042,5.141141e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012825138,0.00004060762,0.0007052819,0.00009154462,0.000023899605,0.0000058273085,0.001072744,0.8904933,0.017237047,0.00008206471,0.0000023296864,0.090117104],"study_design_scores_gemma":[0.0017367214,0.00042652382,0.46971464,0.0010294572,0.00015125978,0.000023076112,0.0013689623,0.35461968,0.1699573,0.0006079225,0.00019183052,0.00017263001],"about_ca_topic_score_codex":0.0000033309846,"about_ca_topic_score_gemma":0.00009295646,"teacher_disagreement_score":0.5358736,"about_ca_system_score_codex":0.00006829391,"about_ca_system_score_gemma":0.00003582788,"threshold_uncertainty_score":0.26241392},"labels":[],"label_agreement":null},{"id":"W4403844452","doi":"10.1007/978-3-031-72646-0_19","title":"DySeT: A Dynamic Masked Self-distillation Approach for Robust Trajectory Prediction","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Trajectory; Distillation; Artificial intelligence; Chromatography","score_opus":0.008857785650474308,"score_gpt":0.19841353749304497,"score_spread":0.18955575184257065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403844452","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":[0.00017468737,0.00065625197,0.9924612,0.00004870811,0.001070934,0.00053976,0.00005105043,0.0015403582,0.0034570426],"genre_scores_gemma":[0.5967587,0.000065409076,0.40216038,0.000052147687,0.0003258259,0.00005431459,0.00009881014,0.00010134149,0.00038303903],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985448,0.0000041031467,0.00029341603,0.0006343751,0.00020019851,0.00032308503],"domain_scores_gemma":[0.99940693,0.00008875742,0.00004912008,0.00036773554,0.000043274864,0.0000441845],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027286838,0.00031722678,0.0002776106,0.00048575606,0.00011216529,0.000060047696,0.0004099951,0.00055864436,0.000006675765],"category_scores_gemma":[0.000009044019,0.00031357585,0.00008994603,0.0002136194,0.00022394213,0.00011250308,0.00008550682,0.0006715446,0.00001061931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004462434,0.000007125223,0.000010766582,0.00020183892,0.000028030578,0.000004288078,0.00024734312,0.8915201,0.00008046481,0.0014623469,0.000012900288,0.10642037],"study_design_scores_gemma":[0.00012716881,0.000056159013,0.00011255503,0.000091084876,0.000027832299,0.000025789648,2.3722461e-7,0.9800379,0.000090276604,0.018582497,0.0005719572,0.00027650894],"about_ca_topic_score_codex":0.0000011329657,"about_ca_topic_score_gemma":0.000016181128,"teacher_disagreement_score":0.596584,"about_ca_system_score_codex":0.0005147759,"about_ca_system_score_gemma":0.00008841761,"threshold_uncertainty_score":0.99993163},"labels":[],"label_agreement":null},{"id":"W4403919682","doi":"10.1109/sm63044.2024.10733404","title":"Smart Mobility: Leveraging Perception Sensors for Map-Based Navigation in Autonomous Vehicles","year":2024,"lang":"en","type":"article","venue":"","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":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Perception; Human–computer interaction; Intelligent sensor; Real-time computing; Computer vision; Artificial intelligence; Wireless sensor network; Computer network","score_opus":0.011777262612242554,"score_gpt":0.23230155032344277,"score_spread":0.22052428771120022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403919682","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":[0.9312183,0.00015017319,0.06444965,0.0007424636,0.00027782124,0.00033710152,0.000010079483,0.0022861005,0.00052825594],"genre_scores_gemma":[0.99603343,0.0000049814316,0.0035064656,0.000045073568,0.00003458666,0.00009669041,0.000044554512,0.000032547654,0.00020167348],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923575,0.00001428712,0.00023097201,0.00023148442,0.000056362347,0.00023115646],"domain_scores_gemma":[0.9996978,0.00009255518,0.000008459401,0.00015826417,0.00001677116,0.000026174319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027498748,0.00013196985,0.00013259861,0.0001634012,0.00004974552,0.000030515908,0.000074035706,0.00018188413,0.00006317162],"category_scores_gemma":[0.000008388056,0.00013679545,0.00006555372,0.00015999078,0.000038323637,0.00013575204,0.000010518843,0.00022199286,0.00008636137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006598361,0.00013743392,0.009892499,0.0020750798,0.00010853735,0.00006176664,0.0024036588,0.5524271,0.050130833,0.009874647,0.0018547119,0.37096775],"study_design_scores_gemma":[0.00021689557,0.000024639623,0.0073400903,0.000071044145,0.000009514505,0.0000039664596,0.00019323708,0.98033494,0.005187496,0.0019773678,0.004466822,0.00017399377],"about_ca_topic_score_codex":0.000032670217,"about_ca_topic_score_gemma":0.000028108121,"teacher_disagreement_score":0.42790785,"about_ca_system_score_codex":0.00027429883,"about_ca_system_score_gemma":0.00003193958,"threshold_uncertainty_score":0.55783564},"labels":[],"label_agreement":null},{"id":"W4403938286","doi":"10.1109/tvt.2024.3485511","title":"A Novel Motion Planning for Autonomous Vehicles Using Point Cloud Based Potential Field","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Motion planning; Cloud computing; Field (mathematics); Computer science; Motion (physics); Point (geometry); Potential field; Aerospace engineering; Engineering; Artificial intelligence; Physics; Mathematics; Robot; Geometry","score_opus":0.01302664332852313,"score_gpt":0.23844330098782318,"score_spread":0.22541665765930005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403938286","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":[0.16153066,0.0003838943,0.8312161,0.0010507453,0.0011897406,0.0003637189,0.000048075784,0.0041819275,0.000035118344],"genre_scores_gemma":[0.9837417,0.000012134889,0.015798073,0.000103677565,0.00007419553,0.00015056798,0.000006793784,0.000083530664,0.000029308409],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986462,0.000012725041,0.00034878615,0.00042585874,0.00010868334,0.00045777133],"domain_scores_gemma":[0.99940073,0.000094349256,0.000030241346,0.000383196,0.00004164727,0.00004982171],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016089724,0.00029202187,0.00028247465,0.0008336302,0.00026332785,0.000039827653,0.0002449993,0.0008946442,0.000033031523],"category_scores_gemma":[0.000007609343,0.0003188731,0.0002225236,0.00053259765,0.000114961076,0.00012993654,0.000002722847,0.0008319697,0.00002730493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037067086,0.000084439096,0.0000032256773,0.0001248737,0.00016984959,0.00007223279,0.000045843968,0.7796934,0.16336758,0.0011594993,0.00006929631,0.055172667],"study_design_scores_gemma":[0.0003981918,0.00014570833,0.0000045494703,0.000096573895,0.000095294185,0.00015406487,0.000052931093,0.70116204,0.2956464,0.00090932014,0.0010874507,0.00024747863],"about_ca_topic_score_codex":0.000010198422,"about_ca_topic_score_gemma":0.000005553471,"teacher_disagreement_score":0.8222111,"about_ca_system_score_codex":0.00021671025,"about_ca_system_score_gemma":0.000059547034,"threshold_uncertainty_score":0.9999263},"labels":[],"label_agreement":null},{"id":"W4404093281","doi":"10.1155/2024/9437594","title":"Classification and Prediction of Vehicle Lane‐Changing Crash Risk Levels Based on Video Trajectory Data","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Trajectory; Crash; Computer science; Transport engineering; Engineering","score_opus":0.019715327041264784,"score_gpt":0.24204482787160625,"score_spread":0.22232950083034148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404093281","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":[0.9167167,0.0005407019,0.081860475,0.00007308111,0.00029518214,0.000076122575,0.00025243097,0.00012942994,0.000055891836],"genre_scores_gemma":[0.99634075,0.0002699616,0.003258728,0.0000052113874,0.000049333714,0.0000017651097,0.000053380074,0.00001624446,0.0000046122063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9993041,0.00001566605,0.00035633545,0.00010929592,0.00012799341,0.00008661766],"domain_scores_gemma":[0.9995921,0.00008680671,0.00011016409,0.00014499907,0.00003863993,0.00002723834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029133228,0.00008051284,0.00013162455,0.00024934392,0.00003454868,0.0000067252527,0.00008947432,0.00008497056,0.000008379569],"category_scores_gemma":[0.00001602636,0.00007696097,0.000033634056,0.00018240663,0.000028055634,0.00039034657,0.000001241621,0.00026505394,8.691118e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014579357,0.000058301,0.013534712,0.00044077678,0.00013227265,0.00002777994,0.001659761,0.74187315,0.1015471,0.0008313879,0.000076378274,0.13967256],"study_design_scores_gemma":[0.0004832866,0.00014102632,0.6979684,0.00023214202,0.000098607634,0.000004427052,0.00028300338,0.29481694,0.0049701063,0.00041336758,0.0005149601,0.0000737093],"about_ca_topic_score_codex":9.2766936e-7,"about_ca_topic_score_gemma":0.000010027863,"teacher_disagreement_score":0.6844337,"about_ca_system_score_codex":0.00003859968,"about_ca_system_score_gemma":0.000027958758,"threshold_uncertainty_score":0.31383774},"labels":[],"label_agreement":null},{"id":"W4404109374","doi":"10.1017/s0263574724001942","title":"End-to-end deep learning-based framework for path planning and collision checking: bin-picking application – ERRATUM","year":2024,"lang":"en","type":"erratum","venue":"Robotica","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":"University of Victoria","funders":"","keywords":"Bin; Path (computing); Collision; End-to-end principle; Computer science; Motion planning; Artificial intelligence; Simulation; Algorithm; Robot; Computer network; Computer security","score_opus":0.010499492599846755,"score_gpt":0.2533371030018521,"score_spread":0.24283761040200533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404109374","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":[0.00016232417,0.0055573164,0.98183626,0.00076301594,0.0057807243,0.00084831414,0.000029891991,0.0020867046,0.0029354657],"genre_scores_gemma":[0.9067047,0.00034244327,0.07799743,0.0003309924,0.002061916,0.0009012242,0.0012287487,0.0006746132,0.009757927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803096,0.000029638688,0.00043387103,0.0007006207,0.0002207358,0.0005841901],"domain_scores_gemma":[0.9989078,0.00032161677,0.00010727793,0.00046131515,0.00006065544,0.00014133098],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.00033400927,0.00049831317,0.0005839369,0.00037252487,0.00030102785,0.00011832101,0.00032492544,0.001819729,0.00001727289],"category_scores_gemma":[0.00022138383,0.00053724006,0.00012910171,0.0004324933,0.000080892314,0.000057858597,0.00012075578,0.002885748,0.00006135392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004232636,0.00003570317,0.00022584572,0.0018164096,0.00023292268,0.000041245057,0.00068919914,0.87675524,0.00024198489,0.014548689,0.08762368,0.017746784],"study_design_scores_gemma":[0.00017138825,0.00013226135,0.00042678887,0.0012153208,0.00017045539,0.000008247769,0.000076393255,0.7032814,0.0001811412,0.004654421,0.28904888,0.0006332654],"about_ca_topic_score_codex":0.000004127101,"about_ca_topic_score_gemma":0.0000065750332,"teacher_disagreement_score":0.90654236,"about_ca_system_score_codex":0.00020971984,"about_ca_system_score_gemma":0.00010358922,"threshold_uncertainty_score":0.99970794},"labels":[],"label_agreement":null},{"id":"W4404124946","doi":"10.1007/978-3-031-73223-2_19","title":"DeTra: A Unified Model for Object Detection and Trajectory Forecasting","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Trajectory; Artificial intelligence; Object (grammar); Computer vision","score_opus":0.01965067332503872,"score_gpt":0.21273862180612285,"score_spread":0.19308794848108413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404124946","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":[0.0019242872,0.0008597946,0.99454755,0.00003118891,0.0005733176,0.00028462245,0.000008605255,0.00049398676,0.0012766588],"genre_scores_gemma":[0.94330555,0.000034015186,0.056248106,0.00004741902,0.00012698473,0.000015978032,0.0000015696706,0.000046364148,0.00017401809],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887145,0.0000021352128,0.00021426384,0.00049022224,0.00011724785,0.0003046935],"domain_scores_gemma":[0.99953645,0.00013576899,0.000032905795,0.00022030098,0.00003259571,0.000041966294],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026093578,0.00026335136,0.00024438277,0.000425852,0.00012307154,0.00005717021,0.0002562737,0.0004062578,0.0000015171545],"category_scores_gemma":[0.000020404064,0.00025883294,0.000058706864,0.0001437328,0.00026888095,0.000093751056,0.00009727758,0.00063470117,0.0000023422383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000407824,0.0000013110403,0.0000014004261,0.000110140034,0.000010638174,0.000007219064,0.00025659613,0.5024057,0.00053711154,0.0010813919,7.648838e-7,0.49558365],"study_design_scores_gemma":[0.00008773881,0.000046987396,0.000008220452,0.00012600241,0.000013993103,0.000040089355,1.6686572e-7,0.8967207,0.0019768483,0.10066149,0.000077125085,0.00024064856],"about_ca_topic_score_codex":0.000002648195,"about_ca_topic_score_gemma":0.00018970213,"teacher_disagreement_score":0.9413813,"about_ca_system_score_codex":0.00017673531,"about_ca_system_score_gemma":0.000070451744,"threshold_uncertainty_score":0.9999864},"labels":[],"label_agreement":null},{"id":"W4404187331","doi":"10.62154/ajastr.2024.017.010458","title":"A Comprehensive Systematic Scoping Review of Self-Driving Vehicle Models","year":2024,"lang":"en","type":"article","venue":"African Journal of Advances in Science and Technology Research","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":"Microsoft (Canada)","funders":"","keywords":"Self driving; Computer science; Engineering; Transport engineering","score_opus":0.02861764995136022,"score_gpt":0.3556956281048381,"score_spread":0.3270779781534779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404187331","genre_codex":"review","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":[0.19845508,0.797693,0.0010746247,0.00094250217,0.00012421307,0.00047316062,7.424653e-7,0.00017200237,0.0010646525],"genre_scores_gemma":[0.836456,0.16228908,0.0012212046,0.000005701475,0.000006306298,0.000013331956,2.033186e-8,0.0000066279,0.0000017376155],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99837613,0.00005537569,0.0005639619,0.00016619686,0.0004915737,0.0003467563],"domain_scores_gemma":[0.99870104,0.00030973245,0.000078731195,0.00020976609,0.0006520965,0.000048624785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031037827,0.000086251224,0.00043693554,0.0016611599,0.00009469249,0.0000194391,0.00061893713,0.00008229699,0.0000024177439],"category_scores_gemma":[0.00045178967,0.00006843607,0.00002443383,0.0045234333,0.0014020142,0.00077293586,0.00015222366,0.0007454805,0.0000017700265],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016160835,0.0001417689,0.002258765,0.7233158,0.0001642423,0.00078711926,0.0025379912,0.0043526604,0.039174046,0.10570029,0.00009308925,0.12145807],"study_design_scores_gemma":[0.00035613775,0.00066224317,0.00009069804,0.8259237,0.000041409,0.0010414848,0.0048123915,0.105001435,0.012572012,0.048884925,0.000289888,0.0003237211],"about_ca_topic_score_codex":4.0491105e-7,"about_ca_topic_score_gemma":0.000002415004,"teacher_disagreement_score":0.6380009,"about_ca_system_score_codex":0.00013185237,"about_ca_system_score_gemma":0.00034980426,"threshold_uncertainty_score":0.51657796},"labels":[],"label_agreement":null},{"id":"W4404469295","doi":"10.1109/tits.2024.3492794","title":"An Uncertainty-Aware, Dual-Tiered Decision-Making Method for Safe Autonomous Driving","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","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":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dual (grammatical number); Computer science; Risk analysis (engineering); Business","score_opus":0.01420780063916291,"score_gpt":0.29249535701999174,"score_spread":0.2782875563808288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404469295","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":[0.016014785,0.00042586905,0.9753229,0.0000367663,0.004090673,0.0009415756,0.00037038402,0.0026619362,0.0001351375],"genre_scores_gemma":[0.98647606,0.0001297378,0.012367683,0.000023406317,0.00010266343,0.0005649951,0.000055546938,0.00012621787,0.00015367144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977206,0.000058403046,0.00095473207,0.00056833157,0.00026151855,0.00043639986],"domain_scores_gemma":[0.99855876,0.0007152951,0.000059471087,0.0004321325,0.00010596045,0.00012835977],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052638346,0.0003885082,0.0004276249,0.0005641243,0.00028659485,0.00013270641,0.00023272316,0.00040401475,0.00013875312],"category_scores_gemma":[0.0000034440072,0.00040160827,0.00029538473,0.00049070833,0.000048603797,0.00035712475,2.1713316e-7,0.00047407873,0.00008902569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048885246,0.000053291005,0.0000071749223,0.0002293848,0.00020569022,0.000019781783,0.0010552926,0.81688434,0.00094247237,0.0017804921,0.000085843996,0.17868735],"study_design_scores_gemma":[0.00020845316,0.00017032256,0.00005549287,0.00055577146,0.00015696338,0.000027720202,0.0009148595,0.9778927,0.009747879,0.00064883806,0.0091475975,0.00047338795],"about_ca_topic_score_codex":0.000051332892,"about_ca_topic_score_gemma":0.0003124132,"teacher_disagreement_score":0.9704613,"about_ca_system_score_codex":0.00031491727,"about_ca_system_score_gemma":0.0000844997,"threshold_uncertainty_score":0.9998436},"labels":[],"label_agreement":null},{"id":"W4405093075","doi":"10.1016/j.neucom.2024.129105","title":"Predicting Pedestrian Crossing Intention in Autonomous Vehicles: A Review","year":2024,"lang":"en","type":"review","venue":"Neurocomputing","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pedestrian; Computer science; Pedestrian crossing; Artificial intelligence; Transport engineering; Engineering","score_opus":0.029518742778237586,"score_gpt":0.2989726353160346,"score_spread":0.269453892537797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405093075","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":[0.00014081177,0.99365,0.00021050028,0.00002868592,0.00065772654,0.00091585,0.000008063109,0.0026963279,0.0016920165],"genre_scores_gemma":[0.0010806549,0.99808455,0.00022235292,0.000049918795,0.00019843747,0.0000779884,0.00002665475,0.00018404842,0.00007540926],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971041,0.00012341682,0.0013899403,0.00064436847,0.00014090537,0.0005972406],"domain_scores_gemma":[0.99913263,0.00015862772,0.00020796609,0.0004127309,0.00001928207,0.00006873882],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00060804863,0.0006094197,0.0016430712,0.00043252835,0.0001634886,0.00015935668,0.00046686098,0.0005342268,0.000007875482],"category_scores_gemma":[0.000098062264,0.0005853428,0.00047083478,0.00087735255,0.000080266516,0.00008659916,0.00025192415,0.0023832757,0.00021157805],"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":[2.549928e-7,0.00000836876,0.000017174614,0.10125844,0.00003972528,0.00013316772,0.000036061218,0.00013709921,4.6303202e-7,0.00003493657,0.00018811066,0.8981462],"study_design_scores_gemma":[0.00008989082,0.000019118106,0.000013847987,0.19035253,0.0005158221,0.00031726182,0.000009380073,0.027500119,6.200742e-7,0.00003934257,0.78065825,0.00048381256],"about_ca_topic_score_codex":0.00001534116,"about_ca_topic_score_gemma":0.000011543476,"teacher_disagreement_score":0.8976624,"about_ca_system_score_codex":0.00035685799,"about_ca_system_score_gemma":0.00017506191,"threshold_uncertainty_score":0.9999183},"labels":[],"label_agreement":null},{"id":"W4405098751","doi":"10.22215/etd/2024-16224","title":"Methods for Classifying Driver Engagement in Autonomous Vehicles Using Physiological Sensors","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Transport Canada; AGE-WELL","keywords":"Computer science; Automotive engineering; Psychology; Aeronautics; Engineering","score_opus":0.05306230930377399,"score_gpt":0.3557558904638425,"score_spread":0.3026935811600685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405098751","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":[0.9672171,0.0015650985,0.021490738,0.00003612657,0.0018902572,0.00081188057,0.000013103884,0.0020186945,0.004957036],"genre_scores_gemma":[0.8226012,0.00018251776,0.17375375,0.000034090153,0.00018125532,0.00022013272,0.00024541162,0.00016010452,0.0026215427],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857074,0.00007955691,0.00045300773,0.00042929454,0.00006393375,0.00040346922],"domain_scores_gemma":[0.99948955,0.00018115187,0.000049617836,0.00021673366,0.000025551895,0.000037421196],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004656542,0.0003633024,0.000496667,0.0003164962,0.000092695394,0.00002865222,0.00019993236,0.000897463,0.000073795985],"category_scores_gemma":[0.000039566585,0.0003383868,0.00018262459,0.00020207345,0.000034392055,0.00006234148,0.000038040813,0.00096159824,0.0000309058],"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":[0.00012852697,0.00015261471,0.00012143553,0.0037522307,0.0009929624,0.00009119286,0.005941471,0.1788215,0.3688504,0.02333777,0.0020645373,0.41574535],"study_design_scores_gemma":[0.000328269,0.00005051078,0.0023618646,0.00021947244,0.000143447,0.000003926269,0.0017361076,0.94765484,0.030329827,0.0054356162,0.010995019,0.00074112136],"about_ca_topic_score_codex":0.000013978289,"about_ca_topic_score_gemma":0.00004424684,"teacher_disagreement_score":0.76883334,"about_ca_system_score_codex":0.00032181377,"about_ca_system_score_gemma":0.000056294222,"threshold_uncertainty_score":0.99990684},"labels":[],"label_agreement":null},{"id":"W4405305553","doi":"10.1109/tvt.2024.3515992","title":"MV-STGHAT: Multi-View Spatial-Temporal Graph Hybrid Attention Network for Decision-Making of Connected and Autonomous Vehicles","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Autonomous Vehicle Technology and Safety","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":"Dalhousie University","funders":"","keywords":"Computer science; Graph; Graph theory; Artificial intelligence; Theoretical computer science; Mathematics","score_opus":0.00817217478572224,"score_gpt":0.23362339397821308,"score_spread":0.22545121919249084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405305553","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":[0.30248618,0.0028840527,0.6913821,0.000220424,0.0005376105,0.0004572973,0.00007423788,0.0019506231,0.0000074685986],"genre_scores_gemma":[0.9810881,0.000427117,0.018134177,0.00002573739,0.000027287491,0.00019651078,0.0000095980795,0.0000761361,0.00001534282],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984261,0.000027349408,0.0005486967,0.00046173597,0.0001081354,0.0004279968],"domain_scores_gemma":[0.99917054,0.00024299904,0.000065201304,0.00040939433,0.00007138154,0.000040492916],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023967803,0.00031266664,0.00047290116,0.0006917868,0.00021962743,0.000025662626,0.00023133046,0.0005704834,0.0000218979],"category_scores_gemma":[0.000017758486,0.00032421955,0.00020960697,0.00066593493,0.0002941752,0.00011739397,0.0000056695167,0.00059664925,0.00001572703],"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":[0.00007417414,0.00013473838,0.00025344946,0.0004171872,0.00056155236,0.000060897124,0.000056837573,0.09435228,0.010076082,0.0020988658,0.00017197379,0.891742],"study_design_scores_gemma":[0.0011563406,0.00050252065,0.0007859739,0.0012897797,0.00037091077,0.00025053107,0.0000972166,0.9272585,0.04870186,0.014112014,0.0047089453,0.00076543074],"about_ca_topic_score_codex":0.000016073327,"about_ca_topic_score_gemma":0.00016253695,"teacher_disagreement_score":0.89097655,"about_ca_system_score_codex":0.0000805341,"about_ca_system_score_gemma":0.000038936167,"threshold_uncertainty_score":0.99992096},"labels":[],"label_agreement":null},{"id":"W4405485942","doi":"10.1139/cjce-2024-0137","title":"Transformer-Based Model for Predicting Trajectories in Autonomous Vehicle-Pedestrian Conflicts: A Proactive Approach to Road Safety","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pedestrian; Transport engineering; Transformer; Computer science; Engineering; Automotive engineering; Voltage; Electrical engineering","score_opus":0.012729246744490854,"score_gpt":0.20160995210613347,"score_spread":0.18888070536164261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405485942","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":[0.08071001,0.0016430744,0.9129835,0.0002854679,0.00060221524,0.0006739704,0.00019022502,0.00036830708,0.002543256],"genre_scores_gemma":[0.99667233,0.000011414703,0.0029897837,0.00002482037,0.00012119032,0.00006273828,0.000007951134,0.00008538441,0.000024400942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984835,0.000009545005,0.00059554324,0.0001989773,0.00011011756,0.0006023496],"domain_scores_gemma":[0.9992514,0.00008509857,0.00003322368,0.00013946767,0.00005966366,0.00043112002],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044673914,0.00026696303,0.0003939073,0.0009045711,0.0000733963,0.000064878135,0.00026744884,0.00024200302,0.000007053892],"category_scores_gemma":[0.0000874034,0.0002892291,0.00015206152,0.00048189363,0.000034987595,0.00028351333,0.000003116733,0.00067425735,0.0000012855088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002624445,0.000008408871,0.00014288304,0.00027208694,0.0001034657,0.000035760597,0.0029264665,0.9904191,0.0005244662,0.00076977466,0.000043727636,0.004727617],"study_design_scores_gemma":[0.0005726878,0.00008469841,0.0008133725,0.00025707512,0.000040414943,0.00005174077,0.00014287228,0.99334395,0.00078029005,0.000081697275,0.0035445753,0.000286621],"about_ca_topic_score_codex":0.00022633848,"about_ca_topic_score_gemma":0.027653847,"teacher_disagreement_score":0.9159623,"about_ca_system_score_codex":0.00082422077,"about_ca_system_score_gemma":0.00095453684,"threshold_uncertainty_score":0.999956},"labels":[],"label_agreement":null},{"id":"W4405571307","doi":"10.3390/wevj15120585","title":"Improving Reinforcement Learning with Expert Demonstrations and Vision Transformers for Autonomous Vehicle Control","year":2024,"lang":"en","type":"article","venue":"World Electric Vehicle Journal","topic":"Autonomous Vehicle Technology and Safety","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":"Université du Québec à Trois-Rivières","funders":"","keywords":"Reinforcement learning; Computer science; Intersection (aeronautics); Artificial intelligence; Key (lock); Machine learning; Generalization; Baseline (sea); Engineering; Transport engineering; Computer security; Mathematics","score_opus":0.00377667952180256,"score_gpt":0.21788177857791774,"score_spread":0.2141050990561152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405571307","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":[0.19845745,0.0073240153,0.7889168,0.0017515002,0.00022898498,0.00056908216,0.0000023879368,0.0011375009,0.0016122577],"genre_scores_gemma":[0.998071,0.00034714755,0.00086863776,0.00008039324,0.00013769555,0.00005139566,0.0000024733265,0.0000521188,0.00038914353],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987561,0.000023494267,0.00036405443,0.00020922354,0.00014337087,0.000503731],"domain_scores_gemma":[0.99956775,0.00013632874,0.000046702626,0.00007219998,0.00004239859,0.00013462649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036872484,0.0002113472,0.0002226327,0.00037890993,0.00050538,0.00017432494,0.00009704559,0.00011282354,0.000029255472],"category_scores_gemma":[0.000012118954,0.00018155805,0.00008387108,0.00045815008,0.000050324936,0.00039580252,0.0000070869282,0.000885306,0.000005846613],"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":[0.00015240624,0.000024276997,0.0004535506,0.00009754181,0.00028525898,0.00004850368,0.0004187782,0.06782792,0.10087972,0.0013625778,0.0005596964,0.8278898],"study_design_scores_gemma":[0.0011867331,0.0005390646,0.0004407459,0.00007264023,0.00007197288,0.0002516335,0.000075399,0.98232186,0.0060302564,0.00015834808,0.008591023,0.00026034814],"about_ca_topic_score_codex":0.000007898119,"about_ca_topic_score_gemma":0.000034394074,"teacher_disagreement_score":0.9144939,"about_ca_system_score_codex":0.00022978819,"about_ca_system_score_gemma":0.00011973085,"threshold_uncertainty_score":0.7403722},"labels":[],"label_agreement":null},{"id":"W4405618751","doi":"10.1007/978-3-031-77571-0_12","title":"Filtering Data from Motion Sensors with Rich Features for Monitoring Brushing Behaviors","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Motion (physics); Computer vision; Computer science; Motion sensors; Artificial intelligence","score_opus":0.019440451653381707,"score_gpt":0.23188096921955628,"score_spread":0.21244051756617457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405618751","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":[0.04900847,0.18429463,0.74266785,0.00013776863,0.009412121,0.002904005,0.0010940996,0.0028841388,0.0075968956],"genre_scores_gemma":[0.9953298,0.0007838039,0.00071768416,0.000004627783,0.001381573,0.00003900888,0.00037961343,0.00016773584,0.0011961394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867207,0.000009392686,0.00033853058,0.000554253,0.00011400789,0.00031174358],"domain_scores_gemma":[0.9990388,0.00025962194,0.000074564385,0.00056296744,0.000019554891,0.000044466],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017694937,0.0004238464,0.0005222106,0.00014131272,0.00009099596,0.00013072515,0.0002714265,0.0010445853,0.000004341222],"category_scores_gemma":[0.000009463257,0.00036479512,0.000042141815,0.00005186674,0.000043759483,0.000092333714,0.0001163318,0.0011295327,0.0000019763806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002834135,0.0000032330076,0.00084179675,0.0003973566,0.0002432948,0.000083492785,0.00022999561,0.9763871,0.00007233009,0.00072463346,0.00011708287,0.020871352],"study_design_scores_gemma":[0.00044331938,0.000077598226,0.00064674986,0.0048416927,0.0003889741,0.000102691134,0.000038433525,0.98294485,0.00013022998,0.0025148382,0.0067379316,0.0011327177],"about_ca_topic_score_codex":0.000101691716,"about_ca_topic_score_gemma":0.0002517301,"teacher_disagreement_score":0.94632137,"about_ca_system_score_codex":0.000098486096,"about_ca_system_score_gemma":0.000008252016,"threshold_uncertainty_score":0.9998804},"labels":[],"label_agreement":null},{"id":"W4406039185","doi":"10.14569/ijacsa.2024.0151288","title":"Multi-Label Decision-Making for Aerobics Platform Selection with Enhanced BERT-Residual Network","year":2024,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Selection (genetic algorithm); Residual; Artificial intelligence; Machine learning; Algorithm","score_opus":0.00814691319867866,"score_gpt":0.2787814304765091,"score_spread":0.27063451727783044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406039185","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":[0.06413481,0.00025882368,0.9348234,0.00014181946,0.00036984778,0.0001393069,0.0000038407957,0.000094739415,0.00003342497],"genre_scores_gemma":[0.5887549,0.00006605657,0.41083533,0.000042542928,0.00026699775,0.00002278829,7.292658e-7,0.000006543612,0.000004121898],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992738,0.0000015486338,0.00022252179,0.00014559779,0.00021434316,0.00014218621],"domain_scores_gemma":[0.9992984,0.00014467885,0.000059947783,0.000063374304,0.00039479343,0.00003878911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021051193,0.00008103398,0.00009071815,0.00016933237,0.00015001028,0.00010508488,0.00033139013,0.00003726003,0.00000153596],"category_scores_gemma":[0.000009949687,0.000066902154,0.000021241327,0.0003441437,0.000113911,0.000536181,0.00003849701,0.00015314043,0.0000016803053],"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":[0.000022326858,0.000016507007,0.00001616222,0.0000056805757,0.0000407472,0.0000018159682,0.00007029585,0.15683185,0.0042627356,0.011265998,0.00007138673,0.8273945],"study_design_scores_gemma":[0.0005495249,0.00015564487,0.00056739524,0.00024865192,0.000017391476,0.00024103494,0.000028692915,0.97178024,0.0038104716,0.012721771,0.009721243,0.00015793406],"about_ca_topic_score_codex":1.6957753e-7,"about_ca_topic_score_gemma":0.0000064090195,"teacher_disagreement_score":0.82723653,"about_ca_system_score_codex":0.00010961467,"about_ca_system_score_gemma":0.00009710502,"threshold_uncertainty_score":0.27281904},"labels":[],"label_agreement":null},{"id":"W4406047093","doi":"10.26599/jicv.2023.9210046","title":"Extracting Networkwide Road Segment Location, Direction, and Turning Movement Rules From Global Positioning System Vehicle Trajectory Data for Macrosimulation","year":2024,"lang":"en","type":"article","venue":"Journal of Intelligent and Connected Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Trajectory; Movement (music); Computer vision; Computer science; Geodesy; Artificial intelligence; Geography; Acoustics; Physics","score_opus":0.014884821038172449,"score_gpt":0.24797120027917818,"score_spread":0.23308637924100573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406047093","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":[0.8473039,0.020835692,0.1307401,0.00015870694,0.0004738586,0.0001514501,0.00006256958,0.00023491836,0.00003876094],"genre_scores_gemma":[0.996811,0.0005638338,0.0022159908,0.000029154211,0.00029638092,0.000005975261,0.000049284048,0.000020021354,0.000008370549],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893206,0.000035905625,0.0005363961,0.00020263475,0.00011995637,0.00017301964],"domain_scores_gemma":[0.9992584,0.00027851487,0.00012693962,0.00013743367,0.00013381668,0.00006489562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042643532,0.00015444764,0.00021589662,0.00008968875,0.00019931396,0.00014628602,0.00012978502,0.00012361146,0.000005899628],"category_scores_gemma":[0.000051027222,0.00014521554,0.000042801264,0.00013458951,0.000039238348,0.00036684074,0.000045026965,0.00022029561,0.000001665032],"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":[0.00024600985,0.00006433524,0.013595017,0.0006794188,0.0012383361,0.000045666864,0.0013671664,0.11378053,0.042321444,0.0034228198,0.000662719,0.8225765],"study_design_scores_gemma":[0.00032338843,0.00008660693,0.020980991,0.0008925978,0.00017435556,0.00006385423,0.0013880293,0.9650251,0.008701709,0.0011659524,0.00099409,0.00020327108],"about_ca_topic_score_codex":0.000032609514,"about_ca_topic_score_gemma":0.000029574401,"teacher_disagreement_score":0.8512446,"about_ca_system_score_codex":0.00018992335,"about_ca_system_score_gemma":0.000029926074,"threshold_uncertainty_score":0.5921718},"labels":[],"label_agreement":null},{"id":"W4406060583","doi":"10.36227/techrxiv.173602799.91045327/v1","title":"Accurate Vehicle Maneuver Analysis Using Smartphone GNSS Carrier-Phase Measurements","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; China Scholarship Council","keywords":"GNSS applications; Computer science; Heading (navigation); Acceleration; Displacement (psychology); Angular velocity; Reduction (mathematics); Global Positioning System; Simulation; Real-time computing; Engineering; Aerospace engineering; Mathematics; Telecommunications","score_opus":0.04772674717011116,"score_gpt":0.30315748778418816,"score_spread":0.255430740614077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406060583","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":[0.8581766,0.0007284151,0.12476713,0.000085872554,0.001056019,0.00046780758,0.00046252672,0.002443839,0.0118118245],"genre_scores_gemma":[0.9970197,0.000059588205,0.001833571,0.000072399926,0.00003946483,0.000035989324,0.00013878412,0.000026835787,0.00077363773],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981389,0.000052326737,0.0005610302,0.0005497312,0.00023924209,0.00045877942],"domain_scores_gemma":[0.99867785,0.000029739895,0.0000971291,0.0009973294,0.00009982562,0.00009813173],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030412286,0.00045348445,0.00074625947,0.00063498196,0.000136135,0.000051238763,0.00053928344,0.00083773426,0.00046172412],"category_scores_gemma":[0.000020277592,0.00048435506,0.0003637779,0.00079305423,0.00006721498,0.000082484185,0.0005106167,0.00095886906,0.00003855454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032666743,0.00009611855,0.0049243574,0.0002791253,0.0065488536,0.000037077447,0.00022944741,0.97737753,0.0030044338,0.00008759518,0.0008925117,0.006490307],"study_design_scores_gemma":[0.0009406462,0.000014861795,0.0018113692,0.00006505236,0.0025695502,0.0000020584719,0.000074153344,0.9705555,0.021916727,0.00034379878,0.0009082987,0.0007980122],"about_ca_topic_score_codex":0.00020308113,"about_ca_topic_score_gemma":0.0002233061,"teacher_disagreement_score":0.13884316,"about_ca_system_score_codex":0.00042589847,"about_ca_system_score_gemma":0.00013046035,"threshold_uncertainty_score":0.9997608},"labels":[],"label_agreement":null},{"id":"W4406062970","doi":"10.1016/j.robot.2024.104908","title":"Interaction-aware motion planning and control based on game theory with human-in-the-loop validation","year":2025,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Loop (graph theory); Human-in-the-loop; Motion (physics); Human–computer interaction; Control (management); Game theory; Motion planning; Simulation; Artificial intelligence; Robot; Mathematical economics","score_opus":0.008337066136703774,"score_gpt":0.22849717997518654,"score_spread":0.22016011383848277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406062970","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":[0.22423027,0.0007191805,0.7689346,0.00048192652,0.00036617817,0.00075641763,0.000009856083,0.00044778455,0.0040537952],"genre_scores_gemma":[0.99971807,0.0000050251365,0.00003829665,0.00006986243,0.000022173657,0.000035571207,0.00001054114,0.000013066435,0.00008738225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934673,0.00007113329,0.00020937918,0.00016308989,0.000060028673,0.00014961118],"domain_scores_gemma":[0.9995753,0.00017748034,0.00004565654,0.00016522828,0.000017834533,0.000018471066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032530146,0.00013819333,0.00018817495,0.00015548497,0.00012356647,0.0000691903,0.00007263022,0.00011706596,0.0000013199261],"category_scores_gemma":[0.000007289848,0.00010693806,0.000015912905,0.0001035723,0.00004486917,0.000065672306,0.000007525071,0.0002327926,0.0000017240968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022207418,0.000022953298,0.005224171,0.000113550646,0.000030962037,0.00000967654,0.00018766767,0.9742733,0.00011413431,0.018489175,0.000014318059,0.0014978762],"study_design_scores_gemma":[0.00091572135,0.00009621933,0.008245125,0.0003012802,0.000039919512,0.000016156358,0.0005747854,0.9888765,0.00019447715,0.00041197226,0.0001742692,0.00015358097],"about_ca_topic_score_codex":0.000013193857,"about_ca_topic_score_gemma":0.0000054214797,"teacher_disagreement_score":0.77548784,"about_ca_system_score_codex":0.000063770894,"about_ca_system_score_gemma":0.000013857776,"threshold_uncertainty_score":0.43608075},"labels":[],"label_agreement":null},{"id":"W4406267156","doi":"10.1109/vtc2024-fall63153.2024.10757767","title":"Hybrid Distance-Based Redundancy Mitigation Mechanisms for Collective Perception in Connected and Automated Driving","year":2024,"lang":"en","type":"article","venue":"","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":"University of Windsor","funders":"","keywords":"Redundancy (engineering); Computer science; Perception; Human–computer interaction; Psychology; Operating system; Neuroscience","score_opus":0.004557614425950742,"score_gpt":0.21163695880507086,"score_spread":0.20707934437912012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406267156","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":[0.48956668,0.00008186721,0.50596297,0.00019869134,0.00010934722,0.00029948787,0.000012127772,0.0033629034,0.00040592253],"genre_scores_gemma":[0.9925226,0.000009910848,0.007174814,0.000019282466,0.000009014718,0.000111929956,0.000034745713,0.000023294626,0.00009439332],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994775,0.000012050004,0.00014886916,0.00017368596,0.000037065663,0.00015084908],"domain_scores_gemma":[0.99979526,0.00009299791,0.000009091534,0.00006754413,0.000014189014,0.000020918807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000103299564,0.000098996854,0.00011294233,0.00014670094,0.00006112243,0.000027991202,0.00003498556,0.000094526426,0.000026021293],"category_scores_gemma":[0.000023400244,0.00010248184,0.000023361672,0.00018331924,0.000030201967,0.00010107808,0.000006203884,0.00010718393,0.0000049087134],"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":[0.00018527001,0.00013927836,0.002413253,0.001468179,0.00030516594,0.0000949855,0.0032391576,0.03521292,0.65523666,0.21448866,0.006799455,0.08041699],"study_design_scores_gemma":[0.00033301764,0.000041385592,0.006278619,0.000090236514,0.000009296023,0.0000043774567,0.00008795164,0.95740277,0.020141477,0.015355787,0.00012748536,0.00012761953],"about_ca_topic_score_codex":0.000004719024,"about_ca_topic_score_gemma":0.00007183098,"teacher_disagreement_score":0.92218983,"about_ca_system_score_codex":0.00023300132,"about_ca_system_score_gemma":0.00003663422,"threshold_uncertainty_score":0.41790882},"labels":[],"label_agreement":null},{"id":"W4406504078","doi":"10.1016/s0967-0653(97)85064-8","title":"10.1016/s0967-0653(97)85064-8","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Collision avoidance; Collision; Computer science; Collision avoidance system; Computer security","score_opus":0.004013825206568469,"score_gpt":0.1500800079313256,"score_spread":0.14606618272475713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406504078","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00047548013,0.00012986395,0.000014469218,0.0001586771,0.000002109955,0.000116334995,0.000009739016,0.0016444885,0.99744886],"genre_scores_gemma":[0.0010293279,9.079038e-7,0.00021221417,0.000018800962,0.000059983162,0.000019766218,0.000008339306,0.000036614103,0.9986141],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99929744,0.000010383714,0.00015962018,0.00015928355,0.000073568204,0.00029967658],"domain_scores_gemma":[0.9995613,0.000023732013,0.000008657001,0.00030946068,0.000011890504,0.00008493306],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00007929756,0.00014751044,0.00015987594,0.00006870778,0.00006321348,0.000011991431,0.00023204891,0.0001651006,0.99691904],"category_scores_gemma":[0.00000881644,0.00015714347,0.000047146907,0.00017546174,0.00003552737,0.00007259471,0.000025620917,0.00020793968,0.99959874],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015183082,0.000012663194,5.3105936e-8,0.0000054568745,0.000021726022,0.000007202537,0.000013730269,0.0029089593,0.000017711676,0.0000061626342,0.029353222,0.96763796],"study_design_scores_gemma":[0.00015203621,0.000044619,0.000008618235,0.0000075836742,0.0000096315935,0.000010236224,7.3278204e-7,0.0040540537,0.00053149334,0.000031108535,0.99495375,0.00019613617],"about_ca_topic_score_codex":0.0000042669235,"about_ca_topic_score_gemma":2.2984186e-7,"teacher_disagreement_score":0.9674418,"about_ca_system_score_codex":0.000045760582,"about_ca_system_score_gemma":0.000008992739,"threshold_uncertainty_score":0.64081246},"labels":[],"label_agreement":null},{"id":"W4406856912","doi":"10.1109/lra.2025.3534570","title":"Multiagent Trajectory Prediction With Difficulty-Guided Feature Enhancement Network","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Trajectory; Feature (linguistics); Computer science; Artificial intelligence; Machine learning; Physics","score_opus":0.005175245754432134,"score_gpt":0.19423553429482004,"score_spread":0.18906028854038792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406856912","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":[0.4251982,0.00017433401,0.56962717,0.003189727,0.00054452627,0.00023779132,0.0000040089453,0.00078364194,0.00024059515],"genre_scores_gemma":[0.99059325,0.00006835489,0.0085166795,0.00056905474,0.000065007756,0.000025640757,0.000018739558,0.000012575625,0.00013066852],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945074,0.000013059235,0.00014938877,0.00014040103,0.000072955394,0.00017346736],"domain_scores_gemma":[0.99978495,0.000019606592,0.00002954824,0.00012552323,0.00001498959,0.000025377562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006067249,0.00012831493,0.00012227445,0.00006207785,0.00011943996,0.000024154824,0.000054854434,0.000104520965,0.000004032835],"category_scores_gemma":[0.0000018891616,0.00011287646,0.00002199861,0.00014066939,0.000043284363,0.00007236391,0.000008106353,0.0001632798,0.0000037100397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043937634,0.000013257282,0.00071164704,0.000050889932,0.00008066227,0.0000029562868,0.00008980109,0.9640417,0.012995337,0.00034793015,0.018386362,0.0032750748],"study_design_scores_gemma":[0.0007342467,0.000035862162,0.050866343,0.000136003,0.00006962081,0.0000074084296,0.00002537675,0.9379144,0.007160713,0.00004261384,0.0027886038,0.00021884755],"about_ca_topic_score_codex":0.0000014332732,"about_ca_topic_score_gemma":0.000005071503,"teacher_disagreement_score":0.56539506,"about_ca_system_score_codex":0.00007169329,"about_ca_system_score_gemma":0.0000085434895,"threshold_uncertainty_score":0.46029687},"labels":[],"label_agreement":null},{"id":"W4406892863","doi":"10.1109/iscmi63661.2024.10851554","title":"Road Lane Tracking Using Constrained Scale Space Radon Transform","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Université de Sherbrooke","funders":"","keywords":"Scale (ratio); Tracking (education); Computer vision; Computer science; Artificial intelligence; Radon transform; Radon; Space (punctuation); Scale space; Geography; Cartography; Image processing; Physics; Image (mathematics)","score_opus":0.010025356540436877,"score_gpt":0.22341392922177628,"score_spread":0.2133885726813394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406892863","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":[0.6789585,0.0016321791,0.22402234,0.0010012966,0.00052037777,0.0002122772,0.0000142958515,0.0060544307,0.08758432],"genre_scores_gemma":[0.9959039,0.00003840397,0.003562668,0.00001879412,0.000048470905,0.0000027791732,0.000003609703,0.000029408639,0.0003919701],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941045,0.000005242636,0.00014833198,0.00014120014,0.00005827445,0.00023651372],"domain_scores_gemma":[0.99981594,0.000021162727,0.0000046912915,0.00011360119,0.0000068018517,0.000037822116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010495568,0.00012497808,0.00013610386,0.000091649024,0.00005219499,0.000030715015,0.00008071932,0.00016617327,0.00026661402],"category_scores_gemma":[0.0000021536891,0.00011654355,0.00005879644,0.00017182124,0.0000632406,0.00013762055,0.000007060828,0.00026091328,0.00007258772],"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":[0.000011087987,0.000024910769,0.0002465614,0.00033122092,0.00017688572,0.0001629879,0.001182955,0.012816297,0.0718228,0.023383122,0.0004332787,0.8894079],"study_design_scores_gemma":[0.00030376972,0.000028630237,0.00048483006,0.00011344356,0.00004834876,0.00021962632,0.00032723445,0.8885736,0.09736519,0.0017915876,0.010351951,0.00039177234],"about_ca_topic_score_codex":0.00002781671,"about_ca_topic_score_gemma":0.000045588513,"teacher_disagreement_score":0.8890161,"about_ca_system_score_codex":0.0000622667,"about_ca_system_score_gemma":0.000020870464,"threshold_uncertainty_score":0.4752508},"labels":[],"label_agreement":null},{"id":"W4406960887","doi":"10.1007/s10489-024-06213-3","title":"Applications of pre-trained CNN models and data fusion techniques in Unity3D for connected vehicles","year":2025,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Fusion; Artificial intelligence; Pattern recognition (psychology); Machine learning","score_opus":0.022334553801636382,"score_gpt":0.2874106393228356,"score_spread":0.2650760855211992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406960887","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":[0.034479372,0.00029878045,0.9609037,0.00007644968,0.000011324137,0.0008677409,0.00005226555,0.00039863467,0.002911726],"genre_scores_gemma":[0.98313195,0.00034589332,0.016061692,0.000028622422,0.000005304488,0.0003368348,0.000055708737,0.0000102282875,0.000023769373],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993358,0.0000057180805,0.00027123466,0.00021722539,0.00003600455,0.00013397462],"domain_scores_gemma":[0.99927527,0.0001773026,0.000030136775,0.0004719881,0.000029206387,0.00001607631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020633478,0.00009892372,0.00016518132,0.0001489466,0.000044225486,0.0000052968776,0.000401432,0.00015683148,0.000003474126],"category_scores_gemma":[0.000018806704,0.0001055148,0.00001095492,0.00033491474,0.000117214266,0.00006685638,0.00014769024,0.00014332218,8.42973e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048810103,0.00005357451,0.00011596134,0.00023379811,0.000027817816,1.8432881e-7,0.00023118877,0.0031999203,0.027143385,0.4253885,0.00013275191,0.5434241],"study_design_scores_gemma":[0.00012145457,0.00002200135,0.0003629936,0.00006117612,0.000019113568,6.930643e-7,0.00025830025,0.40308478,0.37000197,0.22310698,0.0027889637,0.00017154925],"about_ca_topic_score_codex":0.00001661589,"about_ca_topic_score_gemma":0.000051743962,"teacher_disagreement_score":0.94865257,"about_ca_system_score_codex":0.000021523841,"about_ca_system_score_gemma":0.000022822185,"threshold_uncertainty_score":0.43027687},"labels":[],"label_agreement":null},{"id":"W4407014017","doi":"10.52202/078367-0015","title":"Collision Avoidance Manoeuvre Automation with Deep Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Collision avoidance; Reinforcement learning; Computer science; Automation; Artificial intelligence; Collision; Aeronautics; Engineering; Computer security; Mechanical engineering","score_opus":0.003555138726422113,"score_gpt":0.18838500102421546,"score_spread":0.18482986229779336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407014017","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":[0.13175029,0.0004847185,0.80372375,0.00019293059,0.00014157883,0.00013997205,1.7817148e-7,0.005557747,0.058008835],"genre_scores_gemma":[0.9950734,0.00006603841,0.0018311917,0.000016748125,0.000018432085,0.000017717348,0.000006621198,0.000018883624,0.0029510153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956614,0.000005808125,0.00011016595,0.00010756708,0.00007485371,0.00013548527],"domain_scores_gemma":[0.9998428,0.000022351302,0.000007555894,0.00009772049,0.000009858536,0.000019692576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007944906,0.000085956744,0.000069693546,0.000067829424,0.00006824099,0.00002602133,0.000058158694,0.0000924755,0.0001528028],"category_scores_gemma":[0.0000036295608,0.00006843542,0.0000167069,0.00016723719,0.000019109984,0.00013798205,0.000013750946,0.0002383575,0.00025457525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043724312,0.0000021153726,0.00012204072,0.0000713826,0.000028954082,0.000015194717,0.00023521608,0.9578474,0.0005995907,0.017203515,0.0002891749,0.023581045],"study_design_scores_gemma":[0.00008301194,0.000049132952,0.00047265587,0.00005408858,0.000007374579,0.0000125862025,0.00008235925,0.97861236,0.005654548,0.00012554713,0.014739868,0.000106459134],"about_ca_topic_score_codex":0.000002791043,"about_ca_topic_score_gemma":0.000011587317,"teacher_disagreement_score":0.8633231,"about_ca_system_score_codex":0.000074160685,"about_ca_system_score_gemma":0.000009481447,"threshold_uncertainty_score":0.3272135},"labels":[],"label_agreement":null},{"id":"W4407025032","doi":"10.1109/lra.2025.3537860","title":"Adapting to Frequent Human Direction Changes in Autonomous Frontal Following Robots","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Autonomous Vehicle Technology and Safety","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":"Simon Fraser University","funders":"","keywords":"Robot; Artificial intelligence; Computer science; Psychology; Computer vision; Physical medicine and rehabilitation; Medicine","score_opus":0.007277385109792957,"score_gpt":0.22550985012409655,"score_spread":0.2182324650143036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407025032","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":[0.89142746,0.0000953032,0.10204047,0.004214185,0.0006992603,0.0002120317,0.0000014198156,0.0007763153,0.0005335759],"genre_scores_gemma":[0.9948109,0.0000074994696,0.0044980356,0.00055717316,0.000034060497,0.000026826478,0.000005029217,0.000015374417,0.000045142253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993097,0.00001763767,0.00021580119,0.00017641352,0.00006252081,0.00021791265],"domain_scores_gemma":[0.9997935,0.000024724017,0.000026153886,0.00011689871,0.000006631595,0.00003205633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001397588,0.00013478893,0.0001741905,0.00029757168,0.00012650833,0.000034366665,0.00007697569,0.000107305284,0.0000032695166],"category_scores_gemma":[0.000007768318,0.00015667826,0.000032718825,0.00020940712,0.000017141301,0.00009432428,0.000021098507,0.00016323336,0.000006218648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016372453,0.000013868195,0.003626818,0.000039655006,0.000042515327,0.000012657038,0.0003564994,0.87859535,0.09924531,0.0012436921,0.0004127088,0.016409302],"study_design_scores_gemma":[0.0009044697,0.000053130694,0.121801116,0.0003735721,0.000053205,0.0000070884744,0.0002000025,0.8536604,0.021192739,0.00045509412,0.0006864204,0.0006127489],"about_ca_topic_score_codex":0.00004064246,"about_ca_topic_score_gemma":0.00034201692,"teacher_disagreement_score":0.11817429,"about_ca_system_score_codex":0.00016297029,"about_ca_system_score_gemma":0.000007201912,"threshold_uncertainty_score":0.6389154},"labels":[],"label_agreement":null},{"id":"W4407378593","doi":"10.1049/pbtr028e_ch3","title":"Accident modelling: an overview","year":2024,"lang":"en","type":"book-chapter","venue":"","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":"Toronto Metropolitan University","funders":"","keywords":"Accident (philosophy); Computer science; Forensic engineering; Engineering; Philosophy; Epistemology","score_opus":0.05168022807535015,"score_gpt":0.24791592019828637,"score_spread":0.19623569212293623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407378593","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000020987958,0.019679917,0.0069076917,0.00006820649,0.00042700054,0.00011497133,0.000009327683,0.0032966814,0.9694752],"genre_scores_gemma":[0.011132503,0.009826128,0.0007931368,0.00007036076,0.00014093216,0.000008191461,0.00003707342,0.00016562741,0.97782606],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992689,0.0000010682601,0.00022886683,0.0002465299,0.00008742784,0.00016720395],"domain_scores_gemma":[0.99948883,0.0000072447206,0.000015354404,0.00042067203,0.000012639476,0.000055261626],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000055608267,0.00027635595,0.00026611378,0.00011568862,0.000024986526,0.000020288617,0.0002202759,0.0006585321,0.0016367623],"category_scores_gemma":[2.7877758e-7,0.00026374345,0.00011358437,0.000015144165,0.000028441871,0.000063359956,0.00006026626,0.0006216135,0.0024871354],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.5907343e-7,0.0000018547686,4.2650288e-7,0.000077638724,0.00008835191,0.00003438995,0.000019455609,0.018397031,0.0000017228432,0.9715052,0.0015215857,0.008351589],"study_design_scores_gemma":[0.000028147246,0.000015672744,6.8815393e-7,0.00008762343,0.0000534414,0.000013208526,0.0000022627328,0.16426691,0.000038464805,0.28595015,0.54924464,0.00029878697],"about_ca_topic_score_codex":0.000003969787,"about_ca_topic_score_gemma":0.000027297456,"teacher_disagreement_score":0.68555504,"about_ca_system_score_codex":0.00007943167,"about_ca_system_score_gemma":0.000014410075,"threshold_uncertainty_score":0.99998146},"labels":[],"label_agreement":null},{"id":"W4407566067","doi":"10.1007/s42154-024-00289-w","title":"Social Predictive Intelligent Driver Model for Autonomous Driving Simulation","year":2025,"lang":"en","type":"article","venue":"Automotive Innovation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science","score_opus":0.018701460303683543,"score_gpt":0.28510843299079486,"score_spread":0.26640697268711133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407566067","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":[0.18875992,0.000012667547,0.8075922,0.00029221928,0.00017826812,0.0004140366,0.00001347297,0.0010128603,0.0017243603],"genre_scores_gemma":[0.99710745,0.0000024694361,0.0020382148,0.00013907388,0.000051845916,0.00010892488,0.00006850643,0.000023345054,0.0004601966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991092,0.000011908599,0.00036902932,0.0002129858,0.000074758704,0.00022207989],"domain_scores_gemma":[0.99933827,0.00009487919,0.00007101542,0.0001211592,0.00036471136,0.000009957489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019918861,0.00015824159,0.00017012558,0.00038289477,0.00017890312,0.000018022753,0.00012125355,0.00025504988,0.000007478269],"category_scores_gemma":[0.000107441505,0.00018366441,0.000048575923,0.0007517466,0.000067173896,0.00021574492,0.000037953167,0.00021372916,0.000008323813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001572483,0.000028303713,0.00070736575,0.00002925856,0.00009571347,2.58287e-7,0.0010239078,0.866613,0.00092527515,0.09302477,0.00055816123,0.036978263],"study_design_scores_gemma":[0.0002699668,0.00002386156,0.010067003,0.000019045734,0.000024442515,1.6148508e-7,0.000059222995,0.9528192,0.0059645274,0.02968961,0.000920329,0.00014262927],"about_ca_topic_score_codex":0.0000012221975,"about_ca_topic_score_gemma":0.0000040863483,"teacher_disagreement_score":0.8083475,"about_ca_system_score_codex":0.00037194,"about_ca_system_score_gemma":0.00006507592,"threshold_uncertainty_score":0.7489617},"labels":[],"label_agreement":null},{"id":"W4407578834","doi":"10.3390/s25041175","title":"Weather-Adaptive Regenerative Braking Strategy Based on Driving Style Recognition for Intelligent Electric Vehicles","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","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":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Regenerative brake; Automotive engineering; Engineering; Engine braking; Computer science; Brake","score_opus":0.016694027544506117,"score_gpt":0.23644564331322526,"score_spread":0.21975161576871916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407578834","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":[0.9106123,0.00016906585,0.07676766,0.00018420332,0.00016519416,0.0005205821,0.000017624496,0.00082422193,0.010739167],"genre_scores_gemma":[0.9978356,0.00002620033,0.0016359854,0.00008300052,0.000040580213,0.000056527417,0.000014010795,0.00002802054,0.00028006936],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919033,0.00003991894,0.0002011316,0.00023055724,0.00006361198,0.00027444956],"domain_scores_gemma":[0.9994755,0.0002433249,0.00003663769,0.00016522096,0.000052719457,0.00002662435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014588598,0.00017243769,0.00017466865,0.00023907828,0.00016712451,0.00001825414,0.00009574879,0.00018558376,0.000017170203],"category_scores_gemma":[0.00005128229,0.00017895778,0.00007542556,0.0002980338,0.000033620887,0.000042253814,0.000009550902,0.0002569464,0.000023777326],"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":[0.0001667542,0.0000981172,0.0003298819,0.000070476744,0.00020478985,0.000010286211,0.00036791022,0.47246137,0.021162203,0.011007948,0.00054627465,0.493574],"study_design_scores_gemma":[0.00026644007,0.00015983418,0.0005336618,0.00008645327,0.00002472269,8.8776187e-7,0.00028963175,0.71890455,0.2757005,0.0034290706,0.00041601964,0.00018821732],"about_ca_topic_score_codex":0.000005476787,"about_ca_topic_score_gemma":0.000028829956,"teacher_disagreement_score":0.49338576,"about_ca_system_score_codex":0.00013756563,"about_ca_system_score_gemma":0.000035677538,"threshold_uncertainty_score":0.72976863},"labels":[],"label_agreement":null},{"id":"W4407831033","doi":"10.1007/s13177-025-00473-0","title":"Design and Analysis of Visual Vehicle Tracking System for Traffic Surveillance Using Deep Learning","year":2025,"lang":"en","type":"article","venue":"International Journal of Intelligent Transportation Systems Research","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Microsemi (Canada)","funders":"","keywords":"Tracking (education); Computer science; Eye tracking; Deep learning; Artificial intelligence; Traffic analysis; Real-time computing; Automotive engineering; Simulation; Engineering; Computer security; Psychology","score_opus":0.047096578126735435,"score_gpt":0.3595689081711769,"score_spread":0.31247233004444147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407831033","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":[0.503852,0.0010767989,0.49464154,0.000013433525,0.00023244548,0.00013993087,0.000006910507,0.00002559902,0.0000113438355],"genre_scores_gemma":[0.99872303,0.0002957069,0.00089419534,0.0000012092064,0.000034389792,0.000010097239,0.00001265924,0.000013203261,0.000015514759],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817926,0.00015965746,0.0009065909,0.00012527453,0.00045508135,0.00017416035],"domain_scores_gemma":[0.9977207,0.00055718294,0.00019856276,0.00006356865,0.0014192548,0.00004072366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002222343,0.000099414734,0.00036759072,0.0015245015,0.000078812045,0.000045859553,0.00025458672,0.00012243539,0.000004316484],"category_scores_gemma":[0.000068625726,0.00009781608,0.0001378368,0.0006992267,0.00007092964,0.00013517158,0.0000048850197,0.00031497097,4.1473183e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001492194,0.000029345496,0.008632922,0.00019378695,0.0018763072,0.000013180437,0.0006833795,0.9613301,0.009256205,0.0009347774,0.0000031284412,0.016897606],"study_design_scores_gemma":[0.00033001672,0.000083293184,0.010956084,0.00025934417,0.00012698257,0.000008668028,0.0021088263,0.9750978,0.010821859,0.000013935848,0.00011745054,0.00007578949],"about_ca_topic_score_codex":0.000016826925,"about_ca_topic_score_gemma":0.00002003565,"teacher_disagreement_score":0.49487102,"about_ca_system_score_codex":0.00023590727,"about_ca_system_score_gemma":0.000066080596,"threshold_uncertainty_score":0.39888242},"labels":[],"label_agreement":null},{"id":"W4408146128","doi":"10.1109/icmla61862.2024.00146","title":"Pedestrian Detection: An Explainable Approach","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Carleton University","funders":"","keywords":"Pedestrian; Computer science; Pedestrian detection; Computer vision; Artificial intelligence; Transport engineering; Engineering","score_opus":0.007842455387717918,"score_gpt":0.1972307937826798,"score_spread":0.18938833839496189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408146128","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":[0.17173201,0.0011379678,0.5135213,0.00010006574,0.000506699,0.00014547535,0.0000019755882,0.012086301,0.3007682],"genre_scores_gemma":[0.99685764,0.000022550874,0.0016362752,0.000010372123,0.000071673625,0.000021913469,0.000003015505,0.000019161775,0.001357387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961376,0.000005447992,0.000077100856,0.0001236944,0.000033867032,0.00014613639],"domain_scores_gemma":[0.99979997,0.000008355812,0.0000019541255,0.0001516872,0.000004330415,0.000033732016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007701272,0.000072859184,0.000061034876,0.00006867301,0.000054126835,0.000030613304,0.00007956947,0.00011629371,0.00013714876],"category_scores_gemma":[0.0000018606327,0.000066872184,0.000023526585,0.00016319944,0.000019755546,0.00017730785,0.00001058305,0.00017386589,0.00014603052],"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":[0.000013411186,0.00008569143,0.00018992387,0.00054606394,0.00018070804,0.00012426384,0.0010273645,0.030786775,0.010984142,0.12978436,0.0029699034,0.8233074],"study_design_scores_gemma":[0.000097259996,0.00005488527,0.00025046722,0.000005486819,0.000011844339,0.00009698656,0.00042660482,0.8907345,0.020671707,0.0027090453,0.084715314,0.0002258944],"about_ca_topic_score_codex":0.000009500589,"about_ca_topic_score_gemma":0.000014395829,"teacher_disagreement_score":0.85994774,"about_ca_system_score_codex":0.00003652019,"about_ca_system_score_gemma":0.000008361501,"threshold_uncertainty_score":0.27269685},"labels":[],"label_agreement":null},{"id":"W4408202005","doi":"10.3390/machines13030211","title":"A Novel Lateral Control System for Autonomous Vehicles: A Look-Down Strategy","year":2025,"lang":"en","type":"article","venue":"Machines","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Software deployment; Lane departure warning system; Computer vision; Controller (irrigation); Overhead (engineering); Artificial intelligence; Position (finance); Advanced driver assistance systems; Real-time computing; PID controller; Simulation; Control engineering; Engineering","score_opus":0.005966892029590408,"score_gpt":0.2163966494498516,"score_spread":0.21042975742026118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408202005","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":[0.5629201,0.0015490139,0.40953988,0.0008866793,0.0016654826,0.0013555251,0.00028084865,0.0058149993,0.015987469],"genre_scores_gemma":[0.9977914,0.0000032551393,0.0008277093,0.00009981019,0.00006159389,0.0001547447,0.00001204866,0.000027616963,0.0010218171],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991566,0.000010091783,0.0002795008,0.00020308766,0.000044869903,0.00030584206],"domain_scores_gemma":[0.99959904,0.000058985403,0.000029390581,0.00024620828,0.000031379437,0.00003502161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001322948,0.00020034715,0.0002977968,0.00012221935,0.00012205795,0.000032540098,0.00022115344,0.00020230877,0.0000091981965],"category_scores_gemma":[0.000011948979,0.00018445213,0.00009693018,0.00012415316,0.00004831835,0.00006426292,0.000024503002,0.00018315605,0.000022048669],"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":[0.0006262345,0.0003915676,0.025801595,0.00435555,0.0022477065,0.00007038333,0.00080885715,0.20233974,0.12554826,0.33666322,0.0072584236,0.29388845],"study_design_scores_gemma":[0.003012745,0.00007310219,0.009476304,0.00009397816,0.00009072827,0.00002767223,0.00008640348,0.97554827,0.0027788058,0.0014634721,0.006994691,0.00035384242],"about_ca_topic_score_codex":0.00004856502,"about_ca_topic_score_gemma":0.00003790652,"teacher_disagreement_score":0.7732085,"about_ca_system_score_codex":0.000098713615,"about_ca_system_score_gemma":0.000037530666,"threshold_uncertainty_score":0.75217396},"labels":[],"label_agreement":null},{"id":"W4408279735","doi":"10.1038/s41597-024-04193-0","title":"Harnessing Smartphone Sensors for Enhanced Road Safety: A Comprehensive Dataset and Review","year":2025,"lang":"en","type":"article","venue":"Scientific Data","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Data science","score_opus":0.027734023176698493,"score_gpt":0.29099685300039113,"score_spread":0.26326282982369265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408279735","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10737917,0.25490168,0.26133323,0.012443438,0.014670134,0.007423353,0.33017957,0.004485193,0.007184207],"genre_scores_gemma":[0.5608331,0.028048584,0.04340159,0.0038821169,0.00022292965,0.00029685925,0.35341343,0.00018210895,0.009719269],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988852,0.000020355184,0.0002595773,0.0005161467,0.00007452755,0.00024415719],"domain_scores_gemma":[0.99828213,0.00007395177,0.000034773726,0.0015291572,0.000041718027,0.000038255148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005097731,0.00012958694,0.00024805532,0.00009852823,0.00030600105,0.000073088035,0.0006017243,0.00007153917,0.000031504525],"category_scores_gemma":[0.00011029857,0.00012749006,0.000018544288,0.00037194838,0.00026878194,0.0002516494,0.00045091545,0.0001311782,0.000037840535],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017008884,0.000019106397,0.000010401725,0.0018360226,0.000081656115,0.0000033659833,0.00004169092,0.00009742556,0.0115938615,0.0004442999,0.663863,0.32199216],"study_design_scores_gemma":[0.00036123005,0.000005446994,0.0004766426,0.0007517498,0.000072961506,0.000004789233,0.000048474005,0.02094821,0.0044890507,0.00032071647,0.9723357,0.0001850418],"about_ca_topic_score_codex":0.0000044855256,"about_ca_topic_score_gemma":0.000022991055,"teacher_disagreement_score":0.45345393,"about_ca_system_score_codex":0.000026737713,"about_ca_system_score_gemma":0.000047647478,"threshold_uncertainty_score":0.51988935},"labels":[],"label_agreement":null},{"id":"W4408384608","doi":"10.1049/itr2.70013","title":"SECRM‐2D: RL‐Based Efficient and Comfortable Route‐Following Autonomous Driving With Analytic Safety Guarantees","year":2025,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Autonomous Vehicle Technology and Safety","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 Toronto","funders":"","keywords":"Computer science; Vehicle safety; Automotive engineering; Embedded system; Real-time computing; Transport engineering; Engineering","score_opus":0.005062171888390562,"score_gpt":0.19932787907137403,"score_spread":0.19426570718298347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408384608","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":[0.64540243,0.0020528992,0.3453049,0.00007300231,0.0006965182,0.0006914922,0.000032002143,0.001289101,0.0044576335],"genre_scores_gemma":[0.9988942,0.00006968666,0.00027567593,0.00003989763,0.00002473086,0.00006752239,0.00004817339,0.000056405363,0.0005236636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786705,0.00003065433,0.000794176,0.0004837874,0.00023564057,0.0005886651],"domain_scores_gemma":[0.999156,0.00010756265,0.00007406876,0.0005147957,0.00004101368,0.0001065791],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004650104,0.0004307627,0.0007113747,0.0003520423,0.00025920817,0.000047908114,0.00030287044,0.0002923416,0.000027471771],"category_scores_gemma":[0.000005082515,0.00038888672,0.00017448235,0.0005581043,0.0001161394,0.00009474796,0.00001601135,0.0004215482,0.0000134187485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006315483,0.00008257424,0.08920707,0.00058282516,0.00058933446,0.00013574192,0.00053407985,0.90322167,0.00041316677,0.003524592,0.000044309683,0.0016015122],"study_design_scores_gemma":[0.0011150773,0.0001291719,0.016907,0.0015766092,0.00046576047,0.000038986578,0.0012012961,0.9572892,0.0062317615,0.0000405424,0.014104643,0.00089991273],"about_ca_topic_score_codex":0.00018401936,"about_ca_topic_score_gemma":0.0003413866,"teacher_disagreement_score":0.3534918,"about_ca_system_score_codex":0.00026802512,"about_ca_system_score_gemma":0.00011499529,"threshold_uncertainty_score":0.9998563},"labels":[],"label_agreement":null},{"id":"W4408455414","doi":"10.1080/23249935.2025.2473628","title":"Modelling the impact of risky cut-in and cut-out manoeuvers on traffic platooning safety with predictability and explainability","year":2025,"lang":"en","type":"article","venue":"Transportmetrica A Transport Science","topic":"Autonomous Vehicle Technology and Safety","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":"Fundamental Research Funds for the Central Universities; Shanghai Rising-Star Program; National Natural Science Foundation of China","keywords":"Predictability; Cut-off; Engineering; Mathematics; Statistics","score_opus":0.008664939704928813,"score_gpt":0.22860991160144267,"score_spread":0.21994497189651385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408455414","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":[0.9726961,0.00028153652,0.025254294,0.000055433517,0.000036036214,0.000524159,0.0000476547,0.00020236801,0.0009023653],"genre_scores_gemma":[0.9992124,0.00032024685,0.0004139018,0.000006228169,0.0000027362112,0.000021000093,0.0000031142467,0.000013287221,0.0000070810156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99809945,0.000021173566,0.00052631967,0.0005673401,0.0002950566,0.000490658],"domain_scores_gemma":[0.9991079,0.00021774691,0.000057044268,0.00046289168,0.00005005798,0.000104349405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001163661,0.00028139248,0.0004398807,0.00061224296,0.00026142885,0.000011110708,0.00038125605,0.00014926604,0.0000068413287],"category_scores_gemma":[0.000015997522,0.00019427687,0.000085417676,0.0025356913,0.0016388671,0.0002824683,0.000008345925,0.0005265407,2.4351493e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002669837,0.00007456666,0.2524193,0.00010661447,0.00003075652,0.00001312129,0.0020176491,0.7416593,0.000064465414,0.0004689063,4.762148e-7,0.002877871],"study_design_scores_gemma":[0.0008351321,0.00020635496,0.68922555,0.000098622775,0.00004610134,0.0000053582353,0.00033362428,0.30790928,0.00086875533,0.00024076791,0.000013716639,0.00021674746],"about_ca_topic_score_codex":0.00022940732,"about_ca_topic_score_gemma":0.00023150611,"teacher_disagreement_score":0.43680623,"about_ca_system_score_codex":0.0001949378,"about_ca_system_score_gemma":0.00021041717,"threshold_uncertainty_score":0.79223806},"labels":[],"label_agreement":null},{"id":"W4408697113","doi":"10.1109/itsc58415.2024.10920151","title":"Safety Comparison of Vehicular Platoons Under Different Bidirectional Communication Topologies: A Signal Analysis Method","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Network topology; Computer science; SIGNAL (programming language); Electronic engineering; Computer network; Engineering","score_opus":0.0195661356899483,"score_gpt":0.3001646957113264,"score_spread":0.2805985600213781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408697113","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":[0.13953125,0.0026915292,0.8515468,0.0004464831,0.00007650056,0.00008427278,0.000019358875,0.0012362484,0.004367532],"genre_scores_gemma":[0.9907903,0.0001255489,0.008856681,0.0000150122805,0.000008282062,0.000012450348,0.0000430475,0.0000120365985,0.00013662723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992089,0.00006478817,0.0003360239,0.00014836926,0.000104044215,0.00013785696],"domain_scores_gemma":[0.9992641,0.00035922884,0.00002509554,0.00030251086,0.000022920398,0.000026096906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020107994,0.00012465914,0.00032422366,0.0002778174,0.00007537641,0.000012524533,0.0001830334,0.00018619526,0.0005843801],"category_scores_gemma":[0.000006280689,0.00010466925,0.00017617995,0.00057740835,0.00008483142,0.000056859393,0.000059413724,0.00030792673,0.000015149092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002027105,0.0001082778,0.009832018,0.0000789261,0.0042428104,0.0000018788907,0.0005208303,0.81607306,0.0046055815,0.14037041,0.0008157945,0.023330174],"study_design_scores_gemma":[0.00008871895,0.000022820626,0.017249702,0.0000137230845,0.00043987043,0.0000026453645,0.0004440999,0.95688987,0.01965461,0.0032778243,0.0017745373,0.00014160907],"about_ca_topic_score_codex":0.000042782543,"about_ca_topic_score_gemma":0.00015676397,"teacher_disagreement_score":0.85125905,"about_ca_system_score_codex":0.00010090526,"about_ca_system_score_gemma":0.000014806896,"threshold_uncertainty_score":0.6398552},"labels":[],"label_agreement":null},{"id":"W4408698283","doi":"10.1109/itsc58415.2024.10919748","title":"Knowledge Distillation Neural Network for Predicting Car-Following Behaviour of Human-Driven and Autonomous Vehicles","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial neural network; Distillation; Computer science; Artificial intelligence; Machine learning; Chemistry","score_opus":0.013425604880400005,"score_gpt":0.25487082100848946,"score_spread":0.24144521612808945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408698283","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":[0.98931354,0.0017940104,0.0054796296,0.000031821754,0.00036709337,0.00020782278,0.000013760825,0.0011966908,0.0015956073],"genre_scores_gemma":[0.9985074,0.000014924261,0.0011425364,0.0000023317975,0.00011719452,0.000024138335,0.00001604718,0.000032353484,0.0001430972],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993495,0.0000086284535,0.00023936649,0.00016313649,0.000036731184,0.00020267551],"domain_scores_gemma":[0.99973655,0.00009878971,0.000017489247,0.000103211765,0.000013487957,0.000030475092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000121533245,0.00011946579,0.00017377296,0.00006186928,0.000112605165,0.000020254545,0.00006842456,0.00014115845,0.0000061646847],"category_scores_gemma":[0.000007346524,0.00011784768,0.000077573306,0.000108749024,0.00003917276,0.00010314835,0.000036550748,0.00014182301,0.0000016895435],"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":[0.000021215998,0.000054324664,0.5873915,0.0013284577,0.0004785322,0.000029410432,0.0032997017,0.15204175,0.02065014,0.045037825,0.0013904555,0.1882767],"study_design_scores_gemma":[0.0002058193,0.00007323575,0.05473952,0.00008412335,0.000098100616,0.0000061891888,0.00009172115,0.94188887,0.0014578017,0.00078811526,0.00039255118,0.00017393404],"about_ca_topic_score_codex":0.00001281879,"about_ca_topic_score_gemma":0.0000458104,"teacher_disagreement_score":0.78984714,"about_ca_system_score_codex":0.00004062214,"about_ca_system_score_gemma":0.0000116721985,"threshold_uncertainty_score":0.4805689},"labels":[],"label_agreement":null},{"id":"W4408712481","doi":"10.1109/itsc58415.2024.10919741","title":"3D Object Detection with Track-Based Auto-Labelling Using Very Sparsely Labelled Data","year":2024,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Labelling; Object (grammar); Object detection; Artificial intelligence; Track (disk drive); Computer vision; Track-before-detect; Pattern recognition (psychology)","score_opus":0.04337745361477451,"score_gpt":0.23465276002080682,"score_spread":0.1912753064060323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408712481","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":[0.37878254,0.0010231093,0.61310375,0.00004172775,0.0003585617,0.00016859094,0.000018893095,0.004751105,0.0017516917],"genre_scores_gemma":[0.98391736,0.000026250746,0.015766256,0.000028713717,0.00005800026,0.0000058287746,0.000022006581,0.000058556037,0.00011702016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990643,0.00001703057,0.0001961642,0.00033562188,0.00010627617,0.00028056907],"domain_scores_gemma":[0.99932617,0.00007733538,0.000015074059,0.000527024,0.000015589878,0.00003878436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019362349,0.00018610862,0.00017146008,0.00016286022,0.00011794753,0.00005230261,0.00023850406,0.00019235784,0.00009464353],"category_scores_gemma":[0.000008838391,0.00016152466,0.00002625057,0.00040710147,0.000052413372,0.00028511,0.00004111893,0.00038882488,0.00008481213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011350418,0.00007917257,0.0005078746,0.0005734894,0.00055667036,0.0003128376,0.00023081257,0.65181345,0.18575667,0.00055506773,0.00029338058,0.1592071],"study_design_scores_gemma":[0.00023925642,0.00004038611,0.000093640156,0.000078741956,0.00006766164,0.0000297696,0.000021581987,0.9525408,0.043738987,0.000042978856,0.0028860325,0.00022017068],"about_ca_topic_score_codex":0.000042014097,"about_ca_topic_score_gemma":0.00014793998,"teacher_disagreement_score":0.60513484,"about_ca_system_score_codex":0.00010256217,"about_ca_system_score_gemma":0.000073576295,"threshold_uncertainty_score":0.6586785},"labels":[],"label_agreement":null},{"id":"W4408726780","doi":"10.1109/itsc58415.2024.10919577","title":"Trajectory Optimization for Autonomous Driving in Uncertain Environments Integrated with Risk-Based Corridors","year":2024,"lang":"en","type":"article","venue":"","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":"University of Victoria","funders":"","keywords":"Trajectory; Computer science; Risk analysis (engineering); Transport engineering; Engineering; Business","score_opus":0.0051653932613803825,"score_gpt":0.19079378582125558,"score_spread":0.1856283925598752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408726780","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":[0.15869051,0.00013177874,0.83911276,0.00004804253,0.00011122163,0.00030502904,0.000013641655,0.0010668077,0.00052021974],"genre_scores_gemma":[0.9682438,0.000024505069,0.03130474,0.000018655734,0.000010063625,0.00010402266,0.000045748675,0.00004409151,0.0002043851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993325,0.000015673004,0.00018044202,0.0002052106,0.000050763934,0.00021544861],"domain_scores_gemma":[0.99972737,0.00009028497,0.000016083308,0.00013278973,0.000004253588,0.000029217184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013729765,0.00015168835,0.00013613248,0.0001837482,0.000041703403,0.000018377666,0.000090864916,0.00014961477,0.000121329715],"category_scores_gemma":[0.000011640209,0.00012882041,0.000033038006,0.00021284251,0.000046244975,0.00009295223,0.000006441616,0.00023966959,0.0000119691895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013350401,0.000015615038,0.0025227913,0.000027417273,0.000027291173,0.000008685671,0.00007224406,0.9851469,0.00014351546,0.00011824534,0.00006722307,0.011836746],"study_design_scores_gemma":[0.0003701052,0.00006835903,0.0012391548,0.000051539082,0.000018099045,0.000001955215,0.00007464982,0.9940162,0.0023063086,0.000040663854,0.0016406868,0.00017228197],"about_ca_topic_score_codex":0.000032171753,"about_ca_topic_score_gemma":0.0001476791,"teacher_disagreement_score":0.80955327,"about_ca_system_score_codex":0.00027641555,"about_ca_system_score_gemma":0.000049975166,"threshold_uncertainty_score":0.52531433},"labels":[],"label_agreement":null},{"id":"W4409216736","doi":"10.3390/wevj16040215","title":"Integrating Vehicle-to-Infrastructure Communication for Safer Lane Changes in Smart Work Zones","year":2025,"lang":"en","type":"article","venue":"World Electric Vehicle Journal","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"SAFER; Work (physics); Automotive engineering; Computer science; Transport engineering; Environmental science; Engineering; Computer security; Mechanical engineering","score_opus":0.005083297677230267,"score_gpt":0.22424681469692367,"score_spread":0.21916351701969342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409216736","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":[0.92582417,0.0045966017,0.049874123,0.012362726,0.00066461554,0.0008081109,0.0000065483746,0.00072169024,0.005141443],"genre_scores_gemma":[0.9898914,0.0002586696,0.0084597515,0.0005828257,0.000093783965,0.00006259916,0.0000049151795,0.000033286826,0.00061275094],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99871886,0.000056337172,0.00042103228,0.00017773749,0.00010864831,0.00051738275],"domain_scores_gemma":[0.9992173,0.00026422695,0.00007175683,0.0002976681,0.00007201599,0.000077040655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005199692,0.00021536941,0.00031545135,0.00091084593,0.00028594738,0.00006221268,0.00046652328,0.00018382855,0.000044310113],"category_scores_gemma":[0.00009460193,0.00021034908,0.000069972964,0.0020676614,0.000027442382,0.000106109415,0.00005659748,0.0012009311,0.000011858089],"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":[0.00026177077,0.000063369494,0.06423594,0.00007259754,0.00015569429,0.000012366215,0.00051103457,0.013348025,0.008928213,0.008761061,0.018205956,0.885444],"study_design_scores_gemma":[0.0045643435,0.000350808,0.5663459,0.0013887219,0.00015224966,0.00007939273,0.000518936,0.24060927,0.016252039,0.06685493,0.10143131,0.0014521034],"about_ca_topic_score_codex":0.000012941737,"about_ca_topic_score_gemma":0.0014427953,"teacher_disagreement_score":0.88399184,"about_ca_system_score_codex":0.00044441194,"about_ca_system_score_gemma":0.00006151585,"threshold_uncertainty_score":0.85777867},"labels":[],"label_agreement":null},{"id":"W4409261949","doi":"10.1109/wacv61041.2025.00651","title":"CycleCrash: A Dataset of Bicycle Collision Videos for Collision Prediction and Analysis","year":2025,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Collision; Computer science; Artificial intelligence; Computer security","score_opus":0.004367781059473801,"score_gpt":0.23044931647680938,"score_spread":0.22608153541733558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409261949","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":[0.6283016,0.00018700202,0.36800683,0.00013017422,0.00006907578,0.00030950448,0.00198979,0.00029012893,0.0007158553],"genre_scores_gemma":[0.9945902,0.00010204352,0.004492345,0.000025643045,0.0000050853782,0.00002924548,0.00063675595,0.000006055868,0.00011262116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945015,0.000007922095,0.00024545478,0.0001447828,0.000046539633,0.00010516081],"domain_scores_gemma":[0.99961406,0.000094108094,0.000025900985,0.00021774329,0.000027117148,0.000021052498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001528412,0.00007852776,0.00019581095,0.00029742552,0.000064883956,0.000006847543,0.000081208644,0.00013771924,0.000013541791],"category_scores_gemma":[0.000026932268,0.00007289397,0.000044357363,0.00060122256,0.000041951786,0.000081984435,0.000039200037,0.000058609498,0.0000012503492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055857596,0.00038836666,0.20466694,0.001086553,0.0053577246,0.0000049651585,0.0003514074,0.48583326,0.0619834,0.030061897,0.11927766,0.09042925],"study_design_scores_gemma":[0.00058329134,0.000058280173,0.054799307,0.000020723883,0.00044375588,6.391588e-7,0.00006373067,0.8953405,0.038921412,0.0011362489,0.008532192,0.00009993348],"about_ca_topic_score_codex":0.000019105453,"about_ca_topic_score_gemma":0.0000491087,"teacher_disagreement_score":0.40950722,"about_ca_system_score_codex":0.00002674783,"about_ca_system_score_gemma":0.000010648629,"threshold_uncertainty_score":0.29725298},"labels":[],"label_agreement":null},{"id":"W4409263505","doi":"10.1109/wacv61041.2025.00121","title":"CTIP: Towards Accurate Tabular-to-Image Generation for Tire Footprint Generation","year":2025,"lang":"en","type":"article","venue":"","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Footprint; Computer science; Image (mathematics); Computer vision; Artificial intelligence; Environmental science; Geology","score_opus":0.019638635843685436,"score_gpt":0.2621273406155856,"score_spread":0.24248870477190018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409263505","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":[0.22608119,0.00007461595,0.76311636,0.0016240057,0.00050231017,0.0004603688,0.000008048,0.0008594543,0.00727362],"genre_scores_gemma":[0.975021,0.000022578555,0.022442171,0.00028742925,0.00015215168,0.00019696885,0.00004426075,0.000018513534,0.0018149108],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993404,0.000008740223,0.00020861643,0.00020057616,0.000047945497,0.00019370498],"domain_scores_gemma":[0.99964386,0.0000109414505,0.0000141879555,0.00023881004,0.000059949773,0.00003223864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001378999,0.00012693125,0.00012954841,0.00011192806,0.00012157621,0.000036653837,0.0001202164,0.00016754816,0.000041649975],"category_scores_gemma":[0.000034067907,0.00012721214,0.000047682337,0.00016369099,0.000015382457,0.00007613629,0.00003672994,0.00010126576,0.00003744877],"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":[0.000011407037,0.000024423589,0.00006730166,0.0000492793,0.00008045843,0.0000014898869,0.00010073107,0.026394151,0.47981766,0.045575697,0.015803263,0.43207416],"study_design_scores_gemma":[0.0002219312,0.00002621843,0.00046426748,0.000004159486,0.000013984146,6.441484e-7,0.000019782927,0.64040166,0.3366259,0.00029780742,0.021783201,0.00014041619],"about_ca_topic_score_codex":0.000010159292,"about_ca_topic_score_gemma":0.00013187154,"teacher_disagreement_score":0.7489398,"about_ca_system_score_codex":0.000099021316,"about_ca_system_score_gemma":0.000033944838,"threshold_uncertainty_score":0.51875603},"labels":[],"label_agreement":null},{"id":"W4409360608","doi":"10.1016/j.dib.2025.111552","title":"CMHT autonomous dataset: A multi-sensor dataset including radar and IR for autonomous driving","year":2025,"lang":"en","type":"article","venue":"Data in Brief","topic":"Autonomous Vehicle Technology and Safety","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":"McMaster University","funders":"","keywords":"Computer science; Radar; Real-time computing; Remote sensing; Environmental science; Geography; Telecommunications","score_opus":0.033813148556922315,"score_gpt":0.29703022061767964,"score_spread":0.26321707206075734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409360608","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16720633,0.0064243604,0.16860944,0.0042213406,0.001945074,0.0043844953,0.6422917,0.003845019,0.0010722124],"genre_scores_gemma":[0.6345899,0.0004726748,0.114099994,0.0012685667,0.00015547164,0.00024739397,0.24876831,0.0001591787,0.00023847152],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99818677,0.000031096628,0.0004979973,0.0006873905,0.0000768295,0.00051991467],"domain_scores_gemma":[0.99784994,0.0002803439,0.000059387326,0.0017273271,0.000012744739,0.000070240305],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00059356325,0.00029229053,0.0004068661,0.00026836307,0.00021537243,0.00006960501,0.0010379922,0.0002672211,0.000021097927],"category_scores_gemma":[0.00023687717,0.00033067216,0.000022529131,0.00026286585,0.00015446068,0.00054052117,0.0011782062,0.0004203986,0.00001668587],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019597428,0.0006580667,0.018541783,0.00179887,0.0009211745,0.00036449835,0.00078575173,0.004597275,0.01479523,0.011534544,0.594298,0.3515088],"study_design_scores_gemma":[0.0016361859,0.000023254133,0.01000238,0.00009402154,0.000058588794,0.000025783798,0.00009023813,0.24414095,0.00076811487,0.00018835865,0.7425263,0.0004458632],"about_ca_topic_score_codex":0.0002649267,"about_ca_topic_score_gemma":0.0008794149,"teacher_disagreement_score":0.4673836,"about_ca_system_score_codex":0.00013973193,"about_ca_system_score_gemma":0.00008363902,"threshold_uncertainty_score":0.9999145},"labels":[],"label_agreement":null},{"id":"W4409787593","doi":"10.61091/jcmcc127a-362","title":"Semantic segmentation model for lane lines based on multi-scale attention mechanism","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Computer science; Mechanism (biology); Scale (ratio); Artificial intelligence; Natural language processing; Cartography; Geography; Physics","score_opus":0.012258104106110182,"score_gpt":0.25025620753928285,"score_spread":0.23799810343317268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409787593","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":[0.28401008,0.00005221375,0.70995504,0.00010754345,0.005363953,0.00028719055,0.0000030726228,0.00010513896,0.00011578868],"genre_scores_gemma":[0.9604172,0.000013860294,0.039295167,0.000025529394,0.00020127172,0.0000057277057,0.0000031378313,0.000025243546,0.000012842273],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987677,0.000025351892,0.0006812935,0.000128642,0.00019276133,0.00020427903],"domain_scores_gemma":[0.9990167,0.00029711734,0.00027636866,0.00014447173,0.00021238367,0.000052969746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007093383,0.00020109086,0.00044665323,0.00023273929,0.00018863568,0.000057538102,0.00018637413,0.00020702733,9.3944385e-7],"category_scores_gemma":[0.00009343103,0.00019148183,0.00013260031,0.00015695713,0.00003093089,0.00008258356,0.00003872397,0.00030021486,7.5863704e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018607534,0.00082551414,0.00017818659,0.0010571232,0.00020674737,0.0000057727757,0.0004897034,0.057942256,0.011659014,0.9227306,0.00023349913,0.0044855084],"study_design_scores_gemma":[0.003236294,0.0001848351,0.00003121352,0.00023637802,0.00007665116,0.000003466856,0.00006458142,0.7290277,0.0029619334,0.26403987,0.000015762947,0.00012134082],"about_ca_topic_score_codex":7.5234647e-7,"about_ca_topic_score_gemma":4.479802e-7,"teacher_disagreement_score":0.67640716,"about_ca_system_score_codex":0.00008949555,"about_ca_system_score_gemma":0.00005544085,"threshold_uncertainty_score":0.7808402},"labels":[],"label_agreement":null},{"id":"W4409793700","doi":"10.61091/jcmcc127a-165","title":"Path Optimization and Decision Making Mechanism for Driverless Vehicles in Intelligent Transportation Systems under Vehicle-Circuit Collaboration","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mechanism (biology); Path (computing); Intelligent transportation system; Computer science; Operations research; Transport engineering; Engineering; Computer network","score_opus":0.00930020857722024,"score_gpt":0.23905831020845775,"score_spread":0.2297581016312375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409793700","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":[0.53985304,0.00031424675,0.45582658,0.00002472369,0.0036343313,0.0002785693,0.0000023798789,0.00003942468,0.000026729234],"genre_scores_gemma":[0.9918454,0.00014233022,0.007850563,0.000008958419,0.00012184834,0.000006425233,0.000002168107,0.000021589827,6.886788e-7],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985555,0.000030320156,0.0008975873,0.0001431208,0.00019097776,0.00018248416],"domain_scores_gemma":[0.9987004,0.0005145691,0.0003138712,0.00009967126,0.00032935198,0.00004212994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007255514,0.00018162615,0.0004625552,0.00028426433,0.00015212748,0.00012109418,0.00013821285,0.00024984518,5.044282e-7],"category_scores_gemma":[0.00009016327,0.00018532599,0.000051405445,0.00031167036,0.00003503434,0.00017932139,0.000022743543,0.00026058924,1.0941319e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005941207,0.0000901669,0.00018838365,0.000370884,0.000055528075,0.0000034839038,0.0007039143,0.078286536,0.00032675496,0.91665673,0.000013685899,0.0032445134],"study_design_scores_gemma":[0.0017577542,0.000117983385,0.00016194087,0.0007158661,0.0000499052,0.000004723533,0.00084372406,0.48190385,0.00037570117,0.5139346,0.000010467319,0.00012350049],"about_ca_topic_score_codex":0.0000023708449,"about_ca_topic_score_gemma":0.0000018556626,"teacher_disagreement_score":0.45199242,"about_ca_system_score_codex":0.00015486666,"about_ca_system_score_gemma":0.00006544158,"threshold_uncertainty_score":0.7557374},"labels":[],"label_agreement":null},{"id":"W4410095430","doi":"10.47392/irjaeh.2025.0225","title":"DUI (Driver Under Influence) Detection Using Open CV","year":2025,"lang":"en","type":"article","venue":"International Research Journal on Advanced Engineering Hub (IRJAEH)","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science","score_opus":0.028271329878116955,"score_gpt":0.3651872239836145,"score_spread":0.3369158941054975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410095430","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":[0.82693267,0.0002550555,0.16457051,0.0005902054,0.0015873077,0.00026052783,0.000009194143,0.00034847626,0.005446041],"genre_scores_gemma":[0.9946377,0.0002835485,0.004195105,0.00007640339,0.0001259849,0.000019330639,0.0000025982106,0.000043870066,0.0006154037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983264,0.00004059128,0.0003825548,0.00024131255,0.00050773204,0.00050139095],"domain_scores_gemma":[0.99902797,0.00020450844,0.000046248566,0.0002536604,0.00033930832,0.00012832109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070978975,0.00020213827,0.000204909,0.0009858827,0.00028458764,0.00022969127,0.0010833635,0.0001737786,0.00011364854],"category_scores_gemma":[0.00037523784,0.0002113165,0.000077541736,0.0005646412,0.000064217464,0.00067476794,0.00026436767,0.001712837,0.000059707356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006456937,0.00002488602,0.00007445863,0.000013938368,0.0001416747,0.00003940616,0.000023261335,0.9099128,0.057851158,0.007979066,0.000065627086,0.023809155],"study_design_scores_gemma":[0.0022489089,0.00019411749,0.018523935,0.00089825224,0.000019513982,0.00028413656,0.00019789391,0.821258,0.05582195,0.013288512,0.086623624,0.0006411373],"about_ca_topic_score_codex":0.00001746876,"about_ca_topic_score_gemma":0.000010689352,"teacher_disagreement_score":0.16770507,"about_ca_system_score_codex":0.0013797,"about_ca_system_score_gemma":0.00010082337,"threshold_uncertainty_score":0.86172366},"labels":[],"label_agreement":null},{"id":"W4410428188","doi":"10.1109/lra.2025.3570948","title":"Sensor Query Schedule and Sensor Noise Covariances for Accuracy-Constrained Trajectory Estimation","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trajectory; Noise (video); Schedule; Computer science; Estimation; Algorithm; Real-time computing; Artificial intelligence; Engineering; Physics","score_opus":0.008049005393917142,"score_gpt":0.22863694309732335,"score_spread":0.2205879377034062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410428188","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":[0.39262345,0.000099295896,0.6024949,0.003757275,0.0002571305,0.00024005114,0.000013630073,0.00044965753,0.00006461964],"genre_scores_gemma":[0.92118907,0.000038158305,0.07816989,0.0004945068,0.000030457342,0.000025377645,0.00001279675,0.000012808202,0.00002690811],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994075,0.000014772352,0.00021730996,0.00015654985,0.000045294444,0.0001585787],"domain_scores_gemma":[0.9996046,0.00019216361,0.00004521183,0.00010770189,0.00002177301,0.000028542221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011922092,0.00013158191,0.00016270307,0.00012298876,0.00013702642,0.00004391801,0.000047064048,0.00012390323,0.0000022745614],"category_scores_gemma":[0.000046633748,0.00013898252,0.000028946428,0.00009489386,0.00010068579,0.00015473616,0.000007700391,0.00010889834,0.0000023818538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014405123,0.00001738253,0.0004026825,0.0003894001,0.00010918988,0.0000033279632,0.0001818467,0.8738177,0.08590794,0.0071772565,0.00078209187,0.031196821],"study_design_scores_gemma":[0.00053457625,0.0000125796505,0.00441069,0.000055165026,0.000041581785,0.000008040164,0.000053381336,0.9866631,0.0074406997,0.0004036804,0.00021703223,0.00015949496],"about_ca_topic_score_codex":0.000002095732,"about_ca_topic_score_gemma":0.000002361083,"teacher_disagreement_score":0.52856565,"about_ca_system_score_codex":0.0000316802,"about_ca_system_score_gemma":0.000018013005,"threshold_uncertainty_score":0.5667543},"labels":[],"label_agreement":null},{"id":"W4410439504","doi":"10.22214/ijraset.2025.70867","title":"Lane Detection for Autonomous Vehicles","year":2025,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Aeronautics; Artificial intelligence; Computer vision; Engineering","score_opus":0.025850639630541957,"score_gpt":0.3471422856310883,"score_spread":0.3212916460005463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410439504","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":[0.60099494,0.0006947882,0.3850822,0.006784895,0.002286789,0.0010064687,0.000014502194,0.0009787775,0.0021566448],"genre_scores_gemma":[0.99371564,0.000115732386,0.0058534695,0.00001659662,0.000050132832,0.00020843494,7.662632e-7,0.000010120141,0.000029102383],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989844,0.00000261816,0.00020322361,0.00018411072,0.00020348646,0.0004221559],"domain_scores_gemma":[0.9994618,0.0001387097,0.000015417767,0.00010609684,0.00023859704,0.000039365244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016610038,0.000083433464,0.000113061054,0.0023776654,0.00021118374,0.00007587466,0.00062498654,0.0001641899,8.980447e-7],"category_scores_gemma":[0.00025641476,0.000085541506,0.000019818246,0.0008401306,0.00029745998,0.00009416439,0.00010376027,0.0005403459,0.0000015472397],"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":[0.000056568788,0.000022570584,0.00013773971,0.00004736179,0.00004300984,0.0000072036914,0.000043069816,0.009470855,0.2586158,0.23595557,0.00019782317,0.49540243],"study_design_scores_gemma":[0.001452513,0.00012563205,0.0009795678,0.00009791706,0.0000053294416,0.00011976037,0.00027945673,0.57359934,0.16151175,0.17771776,0.08386654,0.00024441298],"about_ca_topic_score_codex":0.0000019171025,"about_ca_topic_score_gemma":0.0000067572596,"teacher_disagreement_score":0.5641285,"about_ca_system_score_codex":0.0004237888,"about_ca_system_score_gemma":0.00010134969,"threshold_uncertainty_score":0.34882814},"labels":[],"label_agreement":null},{"id":"W4410571804","doi":"10.1007/s42154-025-00390-8","title":"Preface for Feature Topic on Testing of Highly Automated Vehicles","year":2025,"lang":"en","type":"article","venue":"Automotive Innovation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"","keywords":"Feature (linguistics); Computer science; Information retrieval; Data science; Linguistics","score_opus":0.013876444010435165,"score_gpt":0.25591050406954696,"score_spread":0.2420340600591118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410571804","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":[0.9803703,0.000041938107,0.0105829695,0.0007334385,0.00020977265,0.00034980554,0.00002712755,0.0024419036,0.0052427566],"genre_scores_gemma":[0.99447733,0.0000011973638,0.0050134007,0.00012048708,0.000016861059,0.000031980293,0.000034333356,0.000012330047,0.00029205973],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99943084,0.000009653678,0.00023706343,0.00013222812,0.00005276615,0.00013748076],"domain_scores_gemma":[0.9993518,0.00015544605,0.00006621643,0.00015032185,0.00027053902,0.0000056992476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013898642,0.00010736878,0.00015184577,0.00026927897,0.000062462146,0.0000062020913,0.00010385005,0.00018715613,0.0000016976342],"category_scores_gemma":[0.00027116566,0.00010866232,0.000020631836,0.0011324334,0.000038557406,0.000068393645,0.000017018405,0.00016025816,0.0000033173646],"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":[0.000071526425,0.00016683126,0.019903231,0.00064705714,0.00035796576,0.0000028641894,0.00039798187,0.03762682,0.27492052,0.3887961,0.015563188,0.2615459],"study_design_scores_gemma":[0.00058961904,0.00014038834,0.25797117,0.0001557274,0.000019461064,6.8461725e-7,0.00003259128,0.1869365,0.54498667,0.006515452,0.0024918297,0.00015992226],"about_ca_topic_score_codex":0.0000037659618,"about_ca_topic_score_gemma":0.000001446962,"teacher_disagreement_score":0.38228065,"about_ca_system_score_codex":0.00007661702,"about_ca_system_score_gemma":0.00003155088,"threshold_uncertainty_score":0.4431121},"labels":[],"label_agreement":null},{"id":"W4410637391","doi":"10.1038/s41598-025-03287-y","title":"Agile DQN: adaptive deep recurrent attention reinforcement learning for autonomous UAV obstacle avoidance","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Obstacle avoidance; Computer science; Agile software development; Reinforcement; Artificial intelligence; Avoidance learning; Machine learning; Psychology; Neuroscience; Robot; Mobile robot; Software engineering; Social psychology","score_opus":0.009011987508973217,"score_gpt":0.22649718546038775,"score_spread":0.21748519795141452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410637391","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":[0.31499428,0.0020869633,0.6345934,0.00033837778,0.020773577,0.0018554716,0.0000034963894,0.0026443822,0.022710005],"genre_scores_gemma":[0.9859892,0.000012884677,0.0013522557,0.000014613297,0.000029844334,0.00017894732,0.000067340894,0.00001965657,0.012335235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982652,0.000020260988,0.00056052627,0.00055656827,0.00017310331,0.00042434223],"domain_scores_gemma":[0.9990639,0.000045334626,0.0001541918,0.00055126625,0.00012913933,0.00005613154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008582703,0.00018270018,0.00021962666,0.00021825511,0.0005568576,0.00008974635,0.00015669645,0.00015067674,0.00005889723],"category_scores_gemma":[0.00011148065,0.00019673625,0.00012895516,0.00041551015,0.00015637984,0.00017986473,0.000075539334,0.00028459416,0.00003886389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056290923,0.00009989299,0.0019371754,0.0002796871,0.00021812179,0.00010274436,0.0007947263,0.75802904,0.018957056,0.008496124,0.022767583,0.18826154],"study_design_scores_gemma":[0.00039754095,0.000095420866,0.001657084,0.00016344979,0.00006418672,0.00004419007,0.00034605263,0.69737697,0.025296051,0.010914033,0.26318267,0.00046233114],"about_ca_topic_score_codex":0.0000053576255,"about_ca_topic_score_gemma":0.000026382864,"teacher_disagreement_score":0.67099494,"about_ca_system_score_codex":0.0002877999,"about_ca_system_score_gemma":0.00008590291,"threshold_uncertainty_score":0.80226713},"labels":[],"label_agreement":null},{"id":"W4410770572","doi":"10.1109/mvt.2025.3552887","title":"Autonomous Vehicles: The Critical Role of Vehicle Sensors [Automotive Electronics]","year":2025,"lang":"en","type":"article","venue":"IEEE Vehicular Technology Magazine","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Automotive industry; Automotive electronics; Electronics; Vehicle safety; Automotive engineering; Engineering; Computer science; Electrical engineering; Aerospace engineering","score_opus":0.0032103080584546546,"score_gpt":0.21479696231496193,"score_spread":0.21158665425650727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410770572","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":[0.9721318,0.00799352,0.003348068,0.0065198736,0.00036931515,0.00046721476,0.000022629962,0.0032940502,0.005853522],"genre_scores_gemma":[0.99851614,0.00021968408,0.0006083271,0.00017041738,0.000032032716,0.00009258395,0.000004413293,0.000053084306,0.0003033398],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99791473,0.0000678063,0.00061127247,0.00044608515,0.00017573156,0.00078434637],"domain_scores_gemma":[0.99837637,0.00023138696,0.00007557855,0.0010455463,0.00022026156,0.00005084259],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034123045,0.00038201315,0.00055731996,0.00060678856,0.00023106286,0.000016705255,0.0009026949,0.00092742563,0.000033146935],"category_scores_gemma":[0.00022070858,0.0003286323,0.00017646811,0.0015152602,0.0012838767,0.000104518396,0.00014727586,0.001325891,0.00017935186],"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":[0.0000838071,0.00041037696,0.0044040484,0.00025343522,0.0010499964,0.00012541813,0.0003228683,0.03357433,0.60170484,0.2807022,0.0038636585,0.07350504],"study_design_scores_gemma":[0.0009050689,0.00023112407,0.003279921,0.00010856503,0.00022215597,0.000093939176,0.00043048832,0.1352673,0.75430894,0.056585673,0.047993887,0.00057291955],"about_ca_topic_score_codex":0.0000047995654,"about_ca_topic_score_gemma":0.000015513237,"teacher_disagreement_score":0.22411652,"about_ca_system_score_codex":0.00018712271,"about_ca_system_score_gemma":0.00011802958,"threshold_uncertainty_score":0.99991655},"labels":[],"label_agreement":null},{"id":"W4410887310","doi":"10.1109/syscon64521.2025.11014781","title":"System Engineering-Based Threat Modeling for Heavy-Duty Vehicles","year":2025,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Heavy duty; Computer science; Environmental science; Automotive engineering; Engineering","score_opus":0.007072082990910211,"score_gpt":0.20050946708701242,"score_spread":0.1934373840961022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410887310","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":[0.074450664,0.0004151117,0.9174915,0.00018306603,0.0002578525,0.0002712018,0.000009363059,0.0037104946,0.0032107406],"genre_scores_gemma":[0.9912,0.0000046950195,0.008425733,0.000054716616,0.00002423969,0.00010563847,0.000008381716,0.000026440648,0.00015018013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930584,0.0000036170863,0.00021621132,0.00016368848,0.00004441305,0.00026624912],"domain_scores_gemma":[0.99961436,0.000065247106,0.000008489266,0.00025052708,0.00003041653,0.000030934203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011931211,0.0001544455,0.00020004592,0.00013357073,0.000077925084,0.000013677559,0.00015939282,0.00020125843,0.0000043545956],"category_scores_gemma":[0.000009472525,0.0001515225,0.000081688,0.00015114083,0.000013610172,0.000048725324,0.000018333023,0.00013780559,0.000012991929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013886354,0.000007893906,0.00010525043,0.00039855434,0.00004375582,0.0000011164976,0.000008063567,0.9430784,0.00068396365,0.053159222,0.00020258938,0.002297291],"study_design_scores_gemma":[0.0003614895,0.000012820965,0.00003350753,0.00008520584,0.000020200365,0.0000010453174,0.000039032715,0.9824557,0.015607333,0.0002558876,0.0009821854,0.00014563398],"about_ca_topic_score_codex":0.000009370438,"about_ca_topic_score_gemma":0.000007442656,"teacher_disagreement_score":0.9167493,"about_ca_system_score_codex":0.00012962287,"about_ca_system_score_gemma":0.000027191176,"threshold_uncertainty_score":0.61789083},"labels":[],"label_agreement":null},{"id":"W4411154120","doi":"10.3390/vehicles7020057","title":"Navigating Uncertainty: Advanced Techniques in Pedestrian Intention Prediction for Autonomous Vehicles—A Comprehensive Review","year":2025,"lang":"en","type":"article","venue":"Vehicles","topic":"Autonomous Vehicle Technology and Safety","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":"University of Windsor","funders":"","keywords":"Pedestrian; Computer science; Data science; Artificial intelligence; Human–computer interaction; Transport engineering; Engineering","score_opus":0.011890064908191071,"score_gpt":0.27879391451536395,"score_spread":0.2669038496071729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411154120","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":[0.9318576,0.03729251,0.018110305,0.0029776562,0.00055313174,0.0031040062,0.000097277305,0.004556903,0.0014506434],"genre_scores_gemma":[0.9876217,0.007118412,0.0040789917,0.00041012838,0.0000320827,0.0005648308,0.00008139409,0.000034628487,0.00005783413],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987128,0.000040278996,0.0005491733,0.00029592562,0.0000773228,0.00032453978],"domain_scores_gemma":[0.9993839,0.00013833727,0.000075449294,0.00027371917,0.00009507596,0.000033497567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026231233,0.0002176548,0.00037329574,0.00011178975,0.00012011184,0.000016558739,0.0002079212,0.0002410274,0.0000054545585],"category_scores_gemma":[0.00007800432,0.00023525218,0.00010499833,0.00043851652,0.000079399666,0.00017053804,0.000050802402,0.0004955983,0.000006247893],"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":[0.00004199804,0.00004560089,0.002470062,0.0023007207,0.00005407141,0.000004680589,0.00008966333,0.0027944797,0.013202975,0.0013769466,0.0008675848,0.9767512],"study_design_scores_gemma":[0.005883168,0.00075209345,0.049182605,0.0385563,0.00039026572,0.00004880959,0.001717905,0.37753868,0.1365775,0.030978186,0.35621342,0.0021610828],"about_ca_topic_score_codex":0.000038513175,"about_ca_topic_score_gemma":0.00004451858,"teacher_disagreement_score":0.9745901,"about_ca_system_score_codex":0.00026380343,"about_ca_system_score_gemma":0.000042605523,"threshold_uncertainty_score":0.9593306},"labels":[],"label_agreement":null},{"id":"W4411183293","doi":"10.1016/j.commtr.2025.100183","title":"A multiagent social interaction model for autonomous vehicle testing","year":2025,"lang":"en","type":"article","venue":"Communications in Transportation Research","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":"Ministry of Education and Child Care","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science","score_opus":0.1874162502315943,"score_gpt":0.428341716699128,"score_spread":0.2409254664675337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411183293","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":[0.73239094,0.0007575561,0.25025845,0.0044401214,0.00012112677,0.0015825336,0.00008369234,0.0010092645,0.009356343],"genre_scores_gemma":[0.9842345,0.000105764455,0.014681381,0.000022013646,0.0000059221293,0.00069797126,0.00007641821,0.0000142942035,0.00016169672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918866,0.00005003415,0.0003215712,0.00013385586,0.00008005432,0.00022584466],"domain_scores_gemma":[0.9989826,0.00036065347,0.000020371435,0.0004450018,0.00017415013,0.000017190981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054129295,0.00007325395,0.00010593526,0.00037103496,0.0003824047,0.000018062226,0.0004914949,0.00013567648,0.0000044949347],"category_scores_gemma":[0.000071714734,0.00009127823,0.000035937814,0.00067995675,0.00014275266,0.00011399846,0.00002091006,0.00055708067,0.000006304009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072128016,0.00046927118,0.013235092,0.0003588035,0.00009654272,0.0000019844604,0.009057756,0.4631918,0.013115188,0.16131577,0.0013986013,0.33768708],"study_design_scores_gemma":[0.00036366066,0.000008406059,0.036482893,0.000031691543,0.000005659593,6.346463e-8,0.0004204858,0.9556161,0.00055542774,0.0048676087,0.0015771858,0.000070801514],"about_ca_topic_score_codex":0.000050366223,"about_ca_topic_score_gemma":0.0014102734,"teacher_disagreement_score":0.4924243,"about_ca_system_score_codex":0.00022529165,"about_ca_system_score_gemma":0.000087902656,"threshold_uncertainty_score":0.3722218},"labels":[],"label_agreement":null},{"id":"W4411232762","doi":"10.1109/plans61210.2025.11028310","title":"Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS","year":2025,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":true,"ca_institutions":"Queen's University","funders":"","keywords":"Automotive industry; Automotive engineering; Computer science; Automotive electronics; Engineering; Aerospace engineering","score_opus":0.006336324889067265,"score_gpt":0.22384735378088563,"score_spread":0.21751102889181836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411232762","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":[0.95274824,0.000048030422,0.031262737,0.0019379911,0.00023766891,0.00014813794,0.000038581842,0.0010827581,0.012495862],"genre_scores_gemma":[0.98489696,0.000008090224,0.012811364,0.00026632572,0.000021937241,0.0000036857991,0.000013929822,0.0000097496195,0.0019679521],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956304,0.0000059778445,0.00013832354,0.00013424318,0.00003474126,0.00012365288],"domain_scores_gemma":[0.9997295,0.00004575234,0.000011346254,0.00015093874,0.000026901855,0.00003553442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003634291,0.00010249975,0.00014893024,0.00006857693,0.00007671505,0.0000118375665,0.00007117767,0.000119750555,0.0002046195],"category_scores_gemma":[0.000048628655,0.00009953492,0.000014878948,0.00011696936,0.000030425132,0.000044383905,0.00006286835,0.00011638154,0.000099580815],"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":[0.00032380974,0.0003781617,0.044580728,0.00042946852,0.0021273105,0.00020388336,0.01011567,0.050241917,0.035621,0.10384362,0.03522906,0.71690536],"study_design_scores_gemma":[0.00049307477,0.000075854514,0.24560367,0.00006621251,0.00007913962,0.000005470479,0.00023807173,0.7171949,0.01801519,0.0032909682,0.014511264,0.00042617662],"about_ca_topic_score_codex":0.000019869416,"about_ca_topic_score_gemma":0.00001647324,"teacher_disagreement_score":0.7164792,"about_ca_system_score_codex":0.000033905384,"about_ca_system_score_gemma":0.000013014658,"threshold_uncertainty_score":0.40589163},"labels":[],"label_agreement":null},{"id":"W4411239062","doi":"10.1155/atr/9995539","title":"Comparison of Different Controller Architectures for Autonomous Driving and Recommendations for Robust and Safe Implementations","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Implementation; Computer science; Controller (irrigation); Control engineering; Engineering; Software engineering; Biology","score_opus":0.013077183346603216,"score_gpt":0.301375729578555,"score_spread":0.2882985462319518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411239062","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":[0.6108117,0.0002733028,0.38798037,0.0005441464,0.00008312643,0.00024424208,0.00003920804,0.000016474263,0.000007421613],"genre_scores_gemma":[0.9530844,0.00007347742,0.04675444,0.00001293862,0.000010449961,0.00002886929,0.000020792422,0.0000069653943,0.000007649223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9993616,0.000005780291,0.00046151876,0.00005927841,0.000030550887,0.00008130463],"domain_scores_gemma":[0.99950445,0.00021895647,0.00015332714,0.000033374334,0.00006941486,0.000020499458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000075503114,0.00007388803,0.00023843652,0.00013544296,0.000076282144,0.000004658541,0.000035655128,0.00004376599,0.0000023393316],"category_scores_gemma":[0.000015409933,0.000068509275,0.000050459053,0.000044901226,0.000027569971,0.000048384256,0.0000011163326,0.00008698417,8.4849505e-9],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033363106,0.00009969608,0.029672086,0.00038110133,0.0003880251,2.6854528e-7,0.0030302536,0.647459,0.020198354,0.010223505,0.00016536868,0.2880487],"study_design_scores_gemma":[0.009876851,0.000732569,0.8405069,0.00030401174,0.00058006647,0.000004375166,0.0035615363,0.08428722,0.028971665,0.027771717,0.0031129715,0.00029007648],"about_ca_topic_score_codex":6.331693e-7,"about_ca_topic_score_gemma":0.00010241211,"teacher_disagreement_score":0.8108348,"about_ca_system_score_codex":0.000021887738,"about_ca_system_score_gemma":0.000012534303,"threshold_uncertainty_score":0.27937272},"labels":[],"label_agreement":null},{"id":"W4411476086","doi":"10.1007/s10489-025-06665-1","title":"MSTD: A multimodal vehicle trajectory prediction method based on multi-level spatiotemporal decoupling","year":2025,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Decoupling (probability); Trajectory; Artificial intelligence; Real-time computing; Computer vision; Control engineering","score_opus":0.02364105327654182,"score_gpt":0.27335396285540514,"score_spread":0.24971290957886333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411476086","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":[0.040010937,0.00006427081,0.95318717,0.00005974311,0.0002688935,0.0003468723,0.000022873815,0.0011866067,0.0048526605],"genre_scores_gemma":[0.9092704,0.000016334938,0.09027926,0.00017289116,0.000027194736,0.00011273997,0.000015584328,0.00002900974,0.00007660307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881166,0.00001835401,0.00036748528,0.00036397317,0.00012356078,0.0003149421],"domain_scores_gemma":[0.9992842,0.00019982073,0.00004078837,0.00039271187,0.000031156844,0.000051307554],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032304172,0.00023388922,0.00022336321,0.00023388448,0.00014333685,0.00001656262,0.00028099262,0.00031238727,0.000058575675],"category_scores_gemma":[0.000033245004,0.00025344917,0.00006940107,0.00036467053,0.00007469508,0.000057435635,0.00003243657,0.0005116535,0.00009972871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005699282,0.00009426527,0.000957455,0.00005248153,0.000042645748,0.000002896681,0.00012135448,0.6875792,0.0077147535,0.0042864084,0.000092157854,0.2989994],"study_design_scores_gemma":[0.00021878762,0.000029336145,0.007038689,0.000033535816,0.00001495685,5.0161555e-7,0.00006842201,0.8215608,0.17009483,0.0004886736,0.0002822364,0.00016922079],"about_ca_topic_score_codex":0.000024255465,"about_ca_topic_score_gemma":0.000026188867,"teacher_disagreement_score":0.8692595,"about_ca_system_score_codex":0.0001675093,"about_ca_system_score_gemma":0.00005424513,"threshold_uncertainty_score":0.9999918},"labels":[],"label_agreement":null},{"id":"W4411516718","doi":"10.1155/atr/1188373","title":"Vehicle Collision Warning Based on Combination of the YOLO Algorithm and the Kalman Filter in the Driving Assistance System","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Collision; Warning system; Computer science; Process (computing); Kalman filter; Set (abstract data type); Filter (signal processing); Lane departure warning system; Monocular vision; Interval (graph theory); Focus (optics); Algorithm; Simulation; Real-time computing; Artificial intelligence; Computer vision; Computer security; Telecommunications","score_opus":0.002691690850678071,"score_gpt":0.19717707467649162,"score_spread":0.19448538382581354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411516718","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":[0.9558517,0.00021741152,0.042473752,0.00090227916,0.00019837798,0.00018907481,0.0000026419461,0.000020126516,0.00014467048],"genre_scores_gemma":[0.9989602,0.00003772449,0.00093003357,0.000045736793,0.000007193549,0.0000066966404,0.0000014212615,0.000005083487,0.0000058938194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99930465,0.00007180992,0.00035420962,0.000054820044,0.00014366455,0.00007086043],"domain_scores_gemma":[0.9993649,0.0002877114,0.00017635926,0.000111467125,0.000052901352,0.000006659501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052870606,0.000068007525,0.00014379119,0.00007760765,0.000091543494,0.000006808819,0.00014570208,0.00005658737,5.4406155e-7],"category_scores_gemma":[0.000026099575,0.00003697071,0.0000525385,0.00025645975,0.000061032686,0.00010796942,0.0000015213121,0.0002948732,8.149592e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016526767,0.000041240743,0.009491262,0.00011513067,0.000027314662,0.0000063555904,0.0019798118,0.9526978,0.0014982177,0.00773941,0.000013024662,0.026225172],"study_design_scores_gemma":[0.0024197502,0.000055140605,0.7861643,0.00060615694,0.000042386848,0.0000023462549,0.0015133485,0.20427312,0.003991395,0.00073530315,0.00014290372,0.000053888136],"about_ca_topic_score_codex":0.0000019912056,"about_ca_topic_score_gemma":0.000025179357,"teacher_disagreement_score":0.776673,"about_ca_system_score_codex":0.00005614733,"about_ca_system_score_gemma":0.000016736538,"threshold_uncertainty_score":0.15076217},"labels":[],"label_agreement":null},{"id":"W4411580108","doi":"10.1016/j.engappai.2025.111483","title":"Enhancing Autonomous Vehicle control in complex scenarios with Deep Transformer Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Innovation and Economic Development Trois Rivières","funders":"","keywords":"Computer science; Reinforcement learning; Transformer; Artificial intelligence; Control (management); Electrical engineering; Voltage","score_opus":0.007204830982810559,"score_gpt":0.223383485819841,"score_spread":0.21617865483703044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411580108","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":[0.030532844,0.0001647064,0.9675821,0.000100838006,0.000027049346,0.00044397544,0.000001420355,0.00042635828,0.000720693],"genre_scores_gemma":[0.9948934,0.000024894267,0.004736492,0.000016694188,0.00001265758,0.0002636228,0.0000066609914,0.000019894074,0.000025685715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894875,0.000007334036,0.00050226203,0.00018490116,0.00008281512,0.00027393372],"domain_scores_gemma":[0.9995425,0.00010107198,0.00003772181,0.00023927628,0.00004564848,0.00003379968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016888438,0.0001607397,0.00023316444,0.00029539294,0.00007259488,0.000012291692,0.0002258119,0.00010888739,0.00003149115],"category_scores_gemma":[0.000016884002,0.0001755204,0.00004073416,0.0006364621,0.00006929628,0.00007553888,0.0000114552595,0.00034977702,0.000019290397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013373352,0.00002425938,0.00014592853,0.000069963535,0.000031204094,7.215321e-7,0.00021468857,0.89269614,0.03577622,0.018970205,0.0000011975151,0.0520561],"study_design_scores_gemma":[0.00006868989,0.000034951616,0.00039321574,0.000051286344,0.0000143543,0.0000011409735,0.00021210109,0.89525163,0.10244213,0.00033874804,0.0010364424,0.00015533392],"about_ca_topic_score_codex":0.00003456379,"about_ca_topic_score_gemma":0.00010644618,"teacher_disagreement_score":0.96436054,"about_ca_system_score_codex":0.00011408213,"about_ca_system_score_gemma":0.000031423093,"threshold_uncertainty_score":0.7157514},"labels":[],"label_agreement":null},{"id":"W4411799824","doi":"10.1109/se4ads66461.2025.00015","title":"Towards a Traffic Scenario Catalog for Collaborative Testing of Autonomous Vehicles","year":2025,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Transport engineering; World Wide Web; Engineering","score_opus":0.013692927660483436,"score_gpt":0.23330729240809223,"score_spread":0.2196143647476088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411799824","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":[0.91270477,0.0009314222,0.03852356,0.00057472964,0.00028063834,0.0008035169,0.00009557273,0.0021840474,0.04390176],"genre_scores_gemma":[0.978848,0.000008005722,0.020616882,0.00003807116,0.000011085369,0.000062034174,0.000009325221,0.000014441258,0.000392172],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993246,0.000007842836,0.00026770303,0.00014873796,0.000040237614,0.00021086763],"domain_scores_gemma":[0.99950635,0.00015256656,0.000031550044,0.00017873706,0.00010795281,0.000022840539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013112013,0.00012854466,0.0002393747,0.00012825531,0.00007463894,0.000008142206,0.00017043784,0.00016854699,0.000008450504],"category_scores_gemma":[0.00009509717,0.00012538717,0.000044544973,0.0004862631,0.00009703296,0.000057897334,0.000031236686,0.00012232152,0.0000045883],"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":[0.00008041075,0.00016601577,0.0025176315,0.0006772527,0.00047269216,0.0000073416218,0.0014255976,0.11730069,0.016851136,0.08582235,0.0055096126,0.7691693],"study_design_scores_gemma":[0.0021999048,0.00033471768,0.01026948,0.00017325913,0.00013257242,0.000008173698,0.0013271038,0.7343477,0.22443072,0.0059978245,0.02010288,0.0006756727],"about_ca_topic_score_codex":0.00001768574,"about_ca_topic_score_gemma":0.00006432596,"teacher_disagreement_score":0.7684936,"about_ca_system_score_codex":0.000078909776,"about_ca_system_score_gemma":0.00018589865,"threshold_uncertainty_score":0.5113141},"labels":[],"label_agreement":null},{"id":"W4411980080","doi":"10.1016/j.aei.2025.103591","title":"Contexts Matter: Robot-Aware 3D human motion prediction for Agentic AI-empowered Human-Robot collaboration","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto; University of Texas at San Antonio; National Science Foundation","keywords":"Robot; Human–robot interaction; Motion (physics); Human motion; Artificial intelligence; Computer science; Human–computer interaction","score_opus":0.003909205649999582,"score_gpt":0.23819303324240723,"score_spread":0.23428382759240765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411980080","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07244857,0.00011501177,0.92389935,0.0000599389,0.00068849436,0.00065847934,0.00005511549,0.0016082744,0.00046677777],"genre_scores_gemma":[0.9860293,0.000017550752,0.013089247,0.000100462144,0.000035102454,0.00020039684,0.0003037547,0.000041936262,0.00018219971],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876684,0.0000051657544,0.000673784,0.000120718054,0.00009958955,0.00033389425],"domain_scores_gemma":[0.9993968,0.000033241435,0.000082541046,0.0003303129,0.0001118091,0.0000453083],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011656039,0.0002589778,0.0002757996,0.00027988473,0.00021751388,0.000050223498,0.00017658704,0.00026145965,0.000024793218],"category_scores_gemma":[0.00002007146,0.00031125054,0.000064345,0.00033265183,0.000030235527,0.00068783306,0.00003363347,0.00027896801,0.000025284702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006189016,0.000015519434,0.00026611527,0.00061354734,0.00007778602,5.7274275e-7,0.0003341469,0.98340523,0.00674056,0.0031276816,0.00055346556,0.0048591797],"study_design_scores_gemma":[0.0012947991,0.00007311721,0.0064518945,0.00021212484,0.00006291392,0.000004333115,0.00013877227,0.9752537,0.010300856,0.00042904195,0.005415893,0.0003625562],"about_ca_topic_score_codex":9.3173304e-7,"about_ca_topic_score_gemma":0.0000043250056,"teacher_disagreement_score":0.9135808,"about_ca_system_score_codex":0.00025632567,"about_ca_system_score_gemma":0.000016978649,"threshold_uncertainty_score":0.99993396},"labels":[],"label_agreement":null},{"id":"W4412089031","doi":"10.1080/23249935.2025.2528055","title":"A strategic framework for road safety analysis in the connected and autonomous vehicle era","year":2025,"lang":"en","type":"article","venue":"Transportmetrica A Transport Science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Business; Transport engineering; Process management; Computer science; Engineering","score_opus":0.011736947922434304,"score_gpt":0.24796015844837027,"score_spread":0.23622321052593598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412089031","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":[0.8388479,0.00071443076,0.15668428,0.00057791703,0.00008935906,0.0006251235,0.00008809363,0.00040076234,0.0019721233],"genre_scores_gemma":[0.9950123,0.0001742701,0.004494991,0.00015900753,0.00000795187,0.000096034535,0.000025470075,0.000012582975,0.000017402086],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99801683,0.000018366642,0.0005637216,0.00054526277,0.00025348482,0.00060231454],"domain_scores_gemma":[0.9990321,0.00028722215,0.000044530087,0.0004959855,0.000059738108,0.00008044884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010937752,0.00025102706,0.00046620326,0.0015163192,0.00034155208,0.000034688128,0.00077285396,0.00023408247,0.000027338561],"category_scores_gemma":[0.00002886987,0.00020653136,0.00016276102,0.013536611,0.00064779876,0.0002054779,0.000005766929,0.00052510767,0.0000014318222],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033875683,0.00044263626,0.30513155,0.00034300954,0.00077864964,0.00012854872,0.004968086,0.06379762,0.0018111266,0.56998265,0.000008650016,0.05226871],"study_design_scores_gemma":[0.00062626065,0.000058612448,0.9336208,0.000025093452,0.00038261796,0.000002466778,0.00034682095,0.05328743,0.00045348058,0.010552276,0.00036378202,0.0002803617],"about_ca_topic_score_codex":0.000084841864,"about_ca_topic_score_gemma":0.00039947973,"teacher_disagreement_score":0.62848926,"about_ca_system_score_codex":0.00009659533,"about_ca_system_score_gemma":0.00019483661,"threshold_uncertainty_score":0.8422105},"labels":[],"label_agreement":null},{"id":"W4412091584","doi":"10.1155/atr/9991333","title":"Customized Generative Adversarial Imitation Learning for Driving Behavior Modeling in Traffic Simulation","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Science and Technology Commission of Shanghai Municipality; Shanghai Municipal Education Commission; National Natural Science Foundation of China","keywords":"Generative grammar; Imitation; Adversarial system; Computer science; Artificial intelligence; Generative adversarial network; Human–computer interaction; Simulation; Machine learning; Deep learning; Psychology; Neuroscience","score_opus":0.010446688422698643,"score_gpt":0.2598678735036579,"score_spread":0.24942118508095928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412091584","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":[0.57674044,0.00006935057,0.42276102,0.000021852444,0.00020578131,0.00015133299,8.6162424e-7,0.000042710897,0.0000066524804],"genre_scores_gemma":[0.98381245,0.00005159807,0.016028581,0.0000054169673,0.000035675224,0.000023016126,0.00002293622,0.000013073446,0.000007228034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917513,0.000018212018,0.0005346273,0.00008318372,0.000074260024,0.00011455594],"domain_scores_gemma":[0.99958843,0.00011043161,0.00012408498,0.00003789701,0.00012246387,0.000016702896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020047785,0.00009372471,0.0002035052,0.00028534184,0.000089442896,0.000005555137,0.000052320112,0.00011552755,0.000002017487],"category_scores_gemma":[0.000047101457,0.00010271799,0.00008295722,0.00018063466,0.000010284834,0.000391331,8.531958e-7,0.00026621114,1.7960791e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021633403,0.00002033471,0.0004940937,0.000021288663,0.000021759271,0.0000028480188,0.0014877308,0.95410186,0.007167919,0.00013296519,5.27473e-7,0.036332358],"study_design_scores_gemma":[0.00295186,0.000053969223,0.008798928,0.00007560545,0.00007174898,4.7426633e-7,0.00065073185,0.98564744,0.0011908871,0.00042159637,0.00004777493,0.00008899642],"about_ca_topic_score_codex":6.0379085e-7,"about_ca_topic_score_gemma":0.000054687785,"teacher_disagreement_score":0.40707204,"about_ca_system_score_codex":0.00012721666,"about_ca_system_score_gemma":0.000037128222,"threshold_uncertainty_score":0.4188718},"labels":[],"label_agreement":null},{"id":"W4412346484","doi":"10.1109/dsn64029.2025.00057","title":"RAVAGE: Robotic Autonomous Vehicles’ Attack Generation Engine","year":2025,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Automotive engineering; Aeronautics; Engineering","score_opus":0.012336688957739674,"score_gpt":0.22955708869321836,"score_spread":0.2172203997354787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412346484","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":[0.4044428,0.001219276,0.50075823,0.0017904378,0.0011860916,0.00035813564,0.0000030515964,0.005433051,0.08480893],"genre_scores_gemma":[0.98984164,0.000049395,0.0034201276,0.0002142836,0.000059028338,0.000020159336,0.000013607316,0.000018307228,0.006363432],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993159,0.000011468973,0.00021800965,0.00016968975,0.000049341616,0.00023556834],"domain_scores_gemma":[0.9996259,0.000023607636,0.000011955329,0.00028523436,0.000021526002,0.000031759057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000098211814,0.0001433743,0.0001641839,0.00013347549,0.00008515904,0.000019388312,0.00015552374,0.00020344401,0.0001468705],"category_scores_gemma":[0.000011938409,0.00014595415,0.000043397846,0.00023701065,0.00003738382,0.00009034182,0.000035566383,0.000228456,0.00022121717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051577235,0.000049221286,0.0008523573,0.00006963888,0.00015689424,0.0000155977,0.00008845176,0.7552212,0.027231855,0.038335267,0.035953015,0.14202133],"study_design_scores_gemma":[0.00032279876,0.000016885064,0.0031905773,0.000008997447,0.000024156701,0.000004963942,0.000017081278,0.9357671,0.04197089,0.00035315347,0.018115409,0.00020796305],"about_ca_topic_score_codex":0.000005228558,"about_ca_topic_score_gemma":0.000020689753,"teacher_disagreement_score":0.5853989,"about_ca_system_score_codex":0.00010397665,"about_ca_system_score_gemma":0.000029227584,"threshold_uncertainty_score":0.5951838},"labels":[],"label_agreement":null},{"id":"W4412536387","doi":"10.1109/tits.2025.3589203","title":"Scene-Centric Vehicle Trajectory Prediction at Cooperative Intersection Using Decision-Aware Attention Graph Transformer","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","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":"McMaster University","funders":"","keywords":"Computer science; Intersection (aeronautics); Artificial intelligence; Trajectory; Transformer; Graph; Machine learning; Engineering; Transport engineering; Theoretical computer science; Electrical engineering; Voltage","score_opus":0.013310671904111416,"score_gpt":0.23582003609618218,"score_spread":0.22250936419207076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412536387","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":[0.39204141,0.00018585363,0.603489,0.000010963581,0.0026937064,0.0006295846,0.00017016676,0.00064648554,0.00013281644],"genre_scores_gemma":[0.99873966,0.00040050366,0.00012867067,0.000024108209,0.000034569177,0.00021384476,0.00006628453,0.00005170102,0.00034064965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785703,0.000071774644,0.0009514821,0.00046140657,0.00029775826,0.00036055018],"domain_scores_gemma":[0.99926317,0.00009958504,0.00008073357,0.00028676275,0.00017500095,0.000094771836],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002070053,0.00036905034,0.00036716033,0.00085524685,0.0005041703,0.000042161388,0.00016039934,0.00043300164,0.00013250444],"category_scores_gemma":[0.0000015574275,0.00039427544,0.0002779666,0.0010708458,0.00009747742,0.000351746,2.7520323e-7,0.00048144205,0.000063783235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027372173,0.00017492786,0.0009576231,0.00019924984,0.0003444816,0.0000062342756,0.0006467904,0.96668506,0.016806947,0.00017301844,0.00010114263,0.013630821],"study_design_scores_gemma":[0.002174032,0.00031542947,0.008417919,0.0011306736,0.00056660315,0.000029219287,0.0027506424,0.7514683,0.23074631,0.00008793569,0.0014662377,0.00084667566],"about_ca_topic_score_codex":0.0000973767,"about_ca_topic_score_gemma":0.00059338025,"teacher_disagreement_score":0.6066983,"about_ca_system_score_codex":0.00088263757,"about_ca_system_score_gemma":0.00006045268,"threshold_uncertainty_score":0.9998509},"labels":[],"label_agreement":null},{"id":"W4412567958","doi":"10.1109/wocc63563.2025.11082250","title":"Robust Vehicle Trajectory Prediction via Counterfactual Intervention for Autonomous Driving","year":2025,"lang":"en","type":"article","venue":"","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":"Artificial Intelligence in Medicine (Canada)","funders":"Chongqing Municipal Education Commission; Natural Science Foundation of Chongqing; Chongqing University","keywords":"Counterfactual thinking; Trajectory; Computer science; Intervention (counseling); Artificial intelligence; Psychology","score_opus":0.009139109420375647,"score_gpt":0.21062613805306593,"score_spread":0.20148702863269027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412567958","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":[0.29135475,0.0001239175,0.7019464,0.00007815542,0.00055744365,0.0002382198,0.000009554955,0.0016650113,0.0040265755],"genre_scores_gemma":[0.9971896,0.00001002294,0.0014307224,0.00003532344,0.000036745536,0.00008033646,0.000017648954,0.000018698158,0.0011809499],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930483,0.000009636334,0.00026143852,0.00017055009,0.000042650732,0.00021091188],"domain_scores_gemma":[0.99970937,0.000052252846,0.000021084708,0.00016655456,0.000027628277,0.000023116834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015724522,0.00012314449,0.00013801677,0.00012920333,0.00010221522,0.00001661616,0.00012531603,0.00019414692,0.0000910493],"category_scores_gemma":[0.000013775682,0.00013206595,0.00008987439,0.00010690314,0.000038399183,0.00014710776,0.000024395888,0.00016783529,0.000018984569],"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":[0.00013511688,0.00036499213,0.0155863175,0.0007024883,0.00081227085,0.000004765073,0.00065017643,0.29771063,0.048554886,0.015910314,0.017174782,0.60239327],"study_design_scores_gemma":[0.0008643107,0.00009033167,0.017828418,0.000053236974,0.00004203333,0.0000032162247,0.00013217186,0.9475442,0.025157934,0.00090395653,0.0072104717,0.00016972642],"about_ca_topic_score_codex":0.0000095344085,"about_ca_topic_score_gemma":0.000071467264,"teacher_disagreement_score":0.7058348,"about_ca_system_score_codex":0.00018458077,"about_ca_system_score_gemma":0.000015420237,"threshold_uncertainty_score":0.5385493},"labels":[],"label_agreement":null},{"id":"W4412617404","doi":"10.1155/atr/3150069","title":"Key Perception Technologies for Intelligent Docking in Autonomous Modular Buses","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Modular design; Key (lock); Docking (animal); Computer science; Perception; Human–computer interaction; Transport engineering; Systems engineering; Engineering; Computer security; Operating system; Psychology; Medicine; Neuroscience","score_opus":0.006612282115388023,"score_gpt":0.24072416019887566,"score_spread":0.23411187808348763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412617404","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":[0.7694604,0.0006227618,0.229047,0.00022663754,0.00024919366,0.00014916064,0.000003334264,0.00019999595,0.000041517073],"genre_scores_gemma":[0.9824508,0.000665253,0.016819451,0.000012002287,0.000011239916,0.000016651164,0.000006262689,0.000010663526,0.0000076790975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9992495,0.0000046043165,0.000471349,0.000083454586,0.00005742318,0.00013364347],"domain_scores_gemma":[0.99970365,0.0000380453,0.0000937368,0.000082213104,0.000072061885,0.000010268641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013011663,0.000097317774,0.00019502819,0.00036497298,0.000037384838,0.000005825153,0.00011903812,0.00014160138,0.000002762345],"category_scores_gemma":[0.000024409605,0.00009750719,0.000075472904,0.0002204476,0.00003302449,0.00024396744,0.0000014963475,0.00023483865,6.137066e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064050364,0.000026333211,0.0023263944,0.00008059794,0.000030007059,0.0000071361505,0.000759359,0.8187806,0.01580347,0.0020330865,0.000007709776,0.16008128],"study_design_scores_gemma":[0.0047155325,0.00062315253,0.6594145,0.0012549453,0.00022659458,0.000025668498,0.019469189,0.05515177,0.15515581,0.08724925,0.015896408,0.0008171776],"about_ca_topic_score_codex":0.0000012481432,"about_ca_topic_score_gemma":0.000049209553,"teacher_disagreement_score":0.7636288,"about_ca_system_score_codex":0.00015013842,"about_ca_system_score_gemma":0.000026054186,"threshold_uncertainty_score":0.39762276},"labels":[],"label_agreement":null},{"id":"W4412830300","doi":"10.1109/icaci65340.2025.11096173","title":"LANet: A Lane Boundaries-Aware Approach for Robust Trajectory Prediction","year":2025,"lang":"en","type":"article","venue":"","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Trajectory; Computer science; Artificial intelligence; Computer vision","score_opus":0.008941503523857171,"score_gpt":0.19413638724891916,"score_spread":0.185194883725062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412830300","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":[0.0057716,0.00021204345,0.94184536,0.00009418542,0.00024927457,0.00028450476,0.000051082254,0.0020732388,0.049418703],"genre_scores_gemma":[0.9803833,0.000014382824,0.016010843,0.000071895476,0.000048122598,0.00011298575,0.00013743424,0.000017944067,0.0032031299],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995454,0.0000053076183,0.00012387072,0.00012731289,0.000032806685,0.0001652988],"domain_scores_gemma":[0.9997811,0.000026306667,0.000007977533,0.00015358461,0.000013877322,0.00001718366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008060123,0.00009292835,0.00011712028,0.00007771311,0.00013513867,0.000020047657,0.000094813004,0.00018509827,0.000040073675],"category_scores_gemma":[0.00000807114,0.000088877925,0.00003679566,0.00010662083,0.00006125167,0.000045210658,0.000011973489,0.00014360105,0.0000059962563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021914764,0.00029803414,0.014684746,0.0016612647,0.0010354994,0.0000073768965,0.00086467265,0.4420468,0.001578919,0.1360233,0.23830537,0.16327485],"study_design_scores_gemma":[0.0006734652,0.000046588346,0.005039203,0.000011009031,0.000037736805,0.000005531225,0.0002165434,0.9101875,0.0010433191,0.0007223007,0.08184525,0.0001715665],"about_ca_topic_score_codex":0.000013441688,"about_ca_topic_score_gemma":0.000027684227,"teacher_disagreement_score":0.97461164,"about_ca_system_score_codex":0.00005541441,"about_ca_system_score_gemma":0.000035693756,"threshold_uncertainty_score":0.36243364},"labels":[],"label_agreement":null},{"id":"W4413089326","doi":"10.1016/j.geits.2025.100340","title":"Automating the Estimation of Turning Movement Rates at Multilane Roundabouts Using Unmanned Aerial Vehicles and Deep Learning","year":2025,"lang":"en","type":"article","venue":"Green Energy and Intelligent Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"University of Sharjah; Carleton University","keywords":"Movement (music); Artificial intelligence; Computer science; Aeronautics; Simulation; Engineering; Physics","score_opus":0.006926921183074839,"score_gpt":0.21974667089895264,"score_spread":0.2128197497158778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413089326","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":[0.88137186,0.0010939002,0.117194265,0.00004246577,0.00006336269,0.000065389475,0.000002148316,0.0001220342,0.000044590448],"genre_scores_gemma":[0.998479,0.0004245347,0.00095073355,0.000027913107,0.000011125248,0.0000080396685,0.000044825993,0.000009676991,0.000044143304],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993845,0.00002442298,0.00029064337,0.00012010935,0.00006102163,0.00011926882],"domain_scores_gemma":[0.99976516,0.000065723485,0.000066232766,0.00006308368,0.000023205303,0.000016626678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012698045,0.000112175134,0.0001342662,0.0000765685,0.00021446358,0.000012082922,0.000047634192,0.00009978857,0.000008279453],"category_scores_gemma":[0.000007759702,0.000097074895,0.000024211751,0.00011004728,0.00007552095,0.00009705462,0.0000075037433,0.00009285169,2.2154103e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043282682,0.000009928409,0.008508407,0.00012823212,0.00009168328,0.000001573976,0.0019065497,0.65661687,0.014010119,0.0075077848,9.52075e-7,0.31117463],"study_design_scores_gemma":[0.00018178142,0.000024903753,0.046178576,0.00007965424,0.000048471564,0.0000010755266,0.0002524021,0.87357396,0.078725256,0.00071623345,0.0001265875,0.00009107266],"about_ca_topic_score_codex":0.0003350536,"about_ca_topic_score_gemma":0.0006350057,"teacher_disagreement_score":0.31108356,"about_ca_system_score_codex":0.000034235876,"about_ca_system_score_gemma":0.000006900824,"threshold_uncertainty_score":0.39585996},"labels":[],"label_agreement":null},{"id":"W4413120857","doi":"10.1109/access.2025.3596942","title":"Vision-Based Fall Risk Assessment Through Attention Augmented Neural Encoding and Data Augmentation","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Computer science; Encoding (memory); Artificial neural network; Artificial intelligence; Risk assessment; Machine learning; Computer security","score_opus":0.026860064746507907,"score_gpt":0.35822526789095943,"score_spread":0.3313652031444515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413120857","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":[0.63994455,0.00012402747,0.35712895,0.00044080624,0.00061358226,0.0001982551,0.000035264555,0.0004557839,0.001058764],"genre_scores_gemma":[0.9980478,0.00012267883,0.0014785323,0.0001355054,0.000023306156,0.000021327469,0.0001263354,0.000012244484,0.000032239837],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992036,0.000035488734,0.00022195268,0.00027980522,0.00009259872,0.00016653263],"domain_scores_gemma":[0.9994043,0.00006733039,0.0000532711,0.00043203018,0.000022765342,0.00002030528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018561642,0.00012719442,0.00013204955,0.00009318284,0.00016929513,0.00009636557,0.0004247666,0.000104953404,0.000013712541],"category_scores_gemma":[0.000013396239,0.00012946689,0.000020922853,0.00020940333,0.00005548573,0.0009973441,0.00013078586,0.00022658476,0.0000038693674],"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":[0.00009218348,0.00026641105,0.5274418,0.000546227,0.0005096554,0.00003213464,0.00016445246,0.17842126,0.031596765,0.001998198,0.010526952,0.24840395],"study_design_scores_gemma":[0.0008454371,0.000023985627,0.15219653,0.000060207232,0.0000759098,9.774303e-7,0.00004046702,0.84067446,0.004093319,0.0011470178,0.0006753476,0.00016635186],"about_ca_topic_score_codex":0.00012344067,"about_ca_topic_score_gemma":0.00012141595,"teacher_disagreement_score":0.6622532,"about_ca_system_score_codex":0.00008290416,"about_ca_system_score_gemma":0.000022127357,"threshold_uncertainty_score":0.52795064},"labels":[],"label_agreement":null},{"id":"W4413254929","doi":"10.1155/atr/6812281","title":"Trajectory Planning for Autonomous Vehicles at Intersections Based on the Spatial Structure","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Intersection (aeronautics); Computer science; Mathematical optimization; Obstacle; Smoothness; Quadratic programming; Path (computing); Flexibility (engineering); Trajectory; Spline (mechanical); Any-angle path planning; Algorithm; Mathematics; Artificial intelligence; Engineering; Transport engineering","score_opus":0.006101254621034232,"score_gpt":0.2260101926954826,"score_spread":0.21990893807444836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413254929","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":[0.84788686,0.00009029898,0.15076323,0.00035882855,0.0005615634,0.00014302603,0.000029098068,0.000079277524,0.0000878009],"genre_scores_gemma":[0.9979445,0.0000062943973,0.0018519169,0.00010141547,0.000042387943,0.000008961436,0.000012267524,0.000011680302,0.000020593547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99947155,0.000009443272,0.00028401444,0.000066978435,0.000065339045,0.00010270387],"domain_scores_gemma":[0.99960625,0.00015013368,0.00009395356,0.000082051025,0.0000501834,0.000017414926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007731521,0.000093141825,0.00013018437,0.00013327105,0.00012228517,0.0000051422135,0.000096455005,0.00008899359,0.00001567528],"category_scores_gemma":[0.000014640542,0.000072075134,0.0000953003,0.000097276825,0.000030485871,0.00007790184,7.3218945e-7,0.00025677477,3.3141743e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021936295,0.000011603887,0.0012002643,0.00003029504,0.000047579644,0.0000036985737,0.00043944802,0.97493136,0.0144086685,0.0004924277,0.000115936455,0.008099355],"study_design_scores_gemma":[0.0044186222,0.00075520715,0.648541,0.00054207945,0.00029724895,0.000013198302,0.0017433907,0.15575445,0.16994427,0.005707222,0.01183175,0.00045156866],"about_ca_topic_score_codex":0.00000142339,"about_ca_topic_score_gemma":0.00009192329,"teacher_disagreement_score":0.8191769,"about_ca_system_score_codex":0.000120428886,"about_ca_system_score_gemma":0.000034696084,"threshold_uncertainty_score":0.29391387},"labels":[],"label_agreement":null},{"id":"W4413394129","doi":"10.23919/acc63710.2025.11107955","title":"Optimism Induction Attack on Deep Reinforcement Learning with Control Barrier Function Safety Filter for Autonomous Driving","year":2025,"lang":"en","type":"article","venue":"","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":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Optimism; Function (biology); Control (management); Artificial intelligence; Psychology; Psychotherapist; Cell biology","score_opus":0.007634622487419396,"score_gpt":0.21306681077900883,"score_spread":0.20543218829158943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413394129","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":[0.024905348,0.000031190197,0.9467216,0.00033389757,0.000325568,0.0005275766,9.026501e-7,0.0011926121,0.025961274],"genre_scores_gemma":[0.99266964,0.000011245071,0.0016637259,0.0002657975,0.00005035672,0.00014282364,0.000019771567,0.00002915708,0.0051474525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990823,0.000016636053,0.0002737919,0.00024136524,0.00007808166,0.00030777033],"domain_scores_gemma":[0.9995574,0.000092369955,0.000042285094,0.00021965268,0.00004679728,0.000041501466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001846612,0.00020193485,0.0002107461,0.00016124255,0.00027014528,0.00002321384,0.00009397346,0.00023431715,0.00019939651],"category_scores_gemma":[0.000026509158,0.00017721248,0.000063286745,0.0001361608,0.000040406012,0.00014560406,0.000017946793,0.00038159525,0.000027712313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002067387,0.000009651625,0.00047257158,0.0000267116,0.00017026965,7.494456e-7,0.00005184632,0.96639276,0.0005609485,0.006091129,0.00034171835,0.02567494],"study_design_scores_gemma":[0.001955375,0.00041030213,0.003214389,0.00005289624,0.00008268562,0.0000034238803,0.0001467526,0.9555929,0.0033017353,0.00014601125,0.03481245,0.00028111215],"about_ca_topic_score_codex":0.0000035766336,"about_ca_topic_score_gemma":0.000018819299,"teacher_disagreement_score":0.9677643,"about_ca_system_score_codex":0.00023222799,"about_ca_system_score_gemma":0.000029736164,"threshold_uncertainty_score":0.72265154},"labels":[],"label_agreement":null},{"id":"W4413588611","doi":"10.64628/aam.udeju5qpt","title":"Drivers of self-driving cars can rely too much on autopilot, and that’s a recipe for disaster","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Recipe; Autopilot; Self driving; Aeronautics; Business; Computer science; Transport engineering; Engineering; Aerospace engineering; Chemistry","score_opus":0.01623879652618159,"score_gpt":0.2194488927965642,"score_spread":0.2032100962703826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413588611","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":[0.9757072,0.00011465148,0.0031866615,0.0005957982,0.00082370755,0.0008263994,0.00014250891,0.0016130178,0.01699008],"genre_scores_gemma":[0.9927257,0.00015237724,0.005636447,0.00006140884,0.000024602728,0.00014813489,0.00003314857,0.000054902077,0.0011633252],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989574,0.00002194593,0.00026367145,0.0003728627,0.0001181288,0.00026600808],"domain_scores_gemma":[0.9992555,0.00011746315,0.00007955023,0.0004792179,0.000019214493,0.000049068083],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016506056,0.00026297604,0.0004009705,0.00015887246,0.00008879778,0.000012404576,0.0002883771,0.0003661256,0.00009548923],"category_scores_gemma":[0.000016227896,0.0002726144,0.00011309172,0.00005955134,0.000058344827,0.00003025188,0.0004328868,0.00068178633,0.0000013109593],"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":[0.00040569104,0.0008224527,0.14978118,0.008269499,0.0067797233,0.00016276348,0.05631864,0.28965434,0.0030846163,0.29491028,0.03799488,0.1518159],"study_design_scores_gemma":[0.007148901,0.0024956523,0.14797373,0.0012401108,0.0014120069,0.00008941531,0.012132012,0.5437808,0.030736929,0.040543884,0.2056286,0.0068179644],"about_ca_topic_score_codex":0.000071616225,"about_ca_topic_score_gemma":0.00013371161,"teacher_disagreement_score":0.2543664,"about_ca_system_score_codex":0.0002237008,"about_ca_system_score_gemma":0.000053448064,"threshold_uncertainty_score":0.9999726},"labels":[],"label_agreement":null},{"id":"W4413722078","doi":"10.3390/machines13090760","title":"Motion Planning for Autonomous Driving in Unsignalized Intersections Using Combined Multi-Modal GNN Predictor and MPC Planner","year":2025,"lang":"en","type":"article","venue":"Machines","topic":"Autonomous Vehicle Technology and Safety","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":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ontario Institute of Technology","keywords":"Modal; Planner; Computer science; Motion (physics); Artificial intelligence; Transport engineering; Engineering; Chemistry","score_opus":0.013033549053073822,"score_gpt":0.2645727706090127,"score_spread":0.2515392215559389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413722078","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":[0.7118527,0.00019111841,0.28677267,0.000075167896,0.00030678895,0.00022342891,0.00001162021,0.00044578043,0.00012070494],"genre_scores_gemma":[0.9948402,0.0000040879154,0.0049266405,0.000023860215,0.000024814133,0.000044867837,0.000017068838,0.000019881656,0.00009856897],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934644,0.000018609337,0.0002316043,0.00017662442,0.000029223218,0.00019750775],"domain_scores_gemma":[0.99974704,0.00008273514,0.000026244896,0.000105673964,0.000014183762,0.00002410204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012084934,0.0001449943,0.00019968279,0.00027631008,0.00012771031,0.000020511403,0.00008016544,0.00015416753,0.0000053514436],"category_scores_gemma":[0.000041726136,0.00014971063,0.00003600775,0.00015159196,0.00004461951,0.00009992496,0.000043299493,0.00020328232,6.4200987e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021662441,0.00017968842,0.5343696,0.00045387694,0.00032618135,0.000022447957,0.003346047,0.4119142,0.02145138,0.006015488,0.00025253813,0.02145196],"study_design_scores_gemma":[0.0012231467,0.000022905451,0.05995613,0.000097434364,0.0000226877,0.000005612517,0.0000875178,0.936373,0.0005254579,0.0014289403,0.00012837634,0.0001288084],"about_ca_topic_score_codex":0.00007017516,"about_ca_topic_score_gemma":0.00020134417,"teacher_disagreement_score":0.52445877,"about_ca_system_score_codex":0.00008996429,"about_ca_system_score_gemma":0.00001497364,"threshold_uncertainty_score":0.61050224},"labels":[],"label_agreement":null},{"id":"W4413991507","doi":"10.3390/math13172856","title":"A Quantum-Inspired Hybrid Artificial Neural Network for Identifying the Dynamic Parameters of Mobile Car-Like Robots","year":2025,"lang":"en","type":"article","venue":"Mathematics","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Mobile robot; Robot; Computer science; Quantum; Artificial intelligence; Control engineering; Engineering; Physics","score_opus":0.019890797152547445,"score_gpt":0.25953018972066605,"score_spread":0.23963939256811861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413991507","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":[0.75267786,0.0003954207,0.24537385,0.00007133975,0.0004890409,0.0005468083,0.000015561096,0.0003277368,0.000102375],"genre_scores_gemma":[0.9869949,0.000016042137,0.012705316,0.00003055405,0.000014296612,0.00014848568,0.000006938317,0.000024432311,0.000059010574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911964,0.000013599375,0.00041757335,0.00011698341,0.00007153733,0.0002606884],"domain_scores_gemma":[0.99924815,0.00027351928,0.00007806094,0.000358509,0.000025758689,0.000016009228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024816088,0.00013762533,0.00027679125,0.000054542084,0.00012500583,0.000018686322,0.00026777745,0.00008642057,0.000003984225],"category_scores_gemma":[0.000035663845,0.00011335894,0.00011837825,0.00018356985,0.00009214507,0.000037860285,0.000049056387,0.0001590904,0.000005084409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021028784,0.000086658314,0.00008807359,0.0009824226,0.00028004547,0.000004666675,0.0010639526,0.9460939,0.0037014966,0.024996225,0.0014303816,0.021251105],"study_design_scores_gemma":[0.00011932179,0.000024689516,0.00008207992,0.00006873537,0.00007219574,0.000004269092,0.00029965656,0.95325637,0.002864986,0.04298359,0.0001186559,0.00010542618],"about_ca_topic_score_codex":0.000003681031,"about_ca_topic_score_gemma":0.000021082631,"teacher_disagreement_score":0.23431705,"about_ca_system_score_codex":0.000040791387,"about_ca_system_score_gemma":0.000015854417,"threshold_uncertainty_score":0.46226433},"labels":[],"label_agreement":null},{"id":"W4414015808","doi":"10.11159/mvml25.107","title":"Nighttime Detection of Illegal Crossing by Pedestrians and Pedestrian Lane Obstruction by Vehicles through Effective Deep Learning Model","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pedestrian; Pedestrian detection; Computer science; Pedestrian crossing; Artificial intelligence; Deep learning; Transport engineering; Computer vision; Remote sensing; Geography; Engineering","score_opus":0.002660159813385014,"score_gpt":0.18473179629272762,"score_spread":0.1820716364793426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414015808","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":[0.9711494,0.0018131625,0.026244905,0.000035064302,0.00027263604,0.0001841025,0.0000016735048,0.0001406997,0.000158354],"genre_scores_gemma":[0.99951464,0.00006776558,0.00026547056,0.000004110559,0.000018537412,0.0000104825685,1.2699063e-7,0.000007576263,0.000111286594],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992523,0.0000052317014,0.0001990579,0.00022503591,0.000121007455,0.00019734376],"domain_scores_gemma":[0.9997148,0.000073554,0.00006746453,0.000051579143,0.000056388944,0.000036241665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001953118,0.00013637819,0.00022749756,0.00014339053,0.00026934617,0.00011040742,0.00014226833,0.00007609253,5.4248506e-8],"category_scores_gemma":[0.00002455451,0.00011008232,0.000021306547,0.00051992555,0.00024269655,0.00017260558,0.000057148456,0.0002864712,3.132463e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010284728,0.000063840234,0.006637862,0.0012527991,0.00015921035,7.494172e-7,0.00071665127,0.20949262,0.5803965,0.026540054,0.00021966199,0.17441721],"study_design_scores_gemma":[0.00023079313,0.00007493513,0.0012020364,0.0001457115,0.000013598965,0.000010747107,0.000013911104,0.9607746,0.037136093,0.00008388504,0.00021028273,0.000103410035],"about_ca_topic_score_codex":0.000024349318,"about_ca_topic_score_gemma":0.0000011102459,"teacher_disagreement_score":0.751282,"about_ca_system_score_codex":0.00004285954,"about_ca_system_score_gemma":0.000010747407,"threshold_uncertainty_score":0.4489027},"labels":[],"label_agreement":null},{"id":"W4414170517","doi":"10.1109/atigb66719.2025.11142082","title":"Urban Intersection Collision Warning System Utilizing Traffic Surveillance Cameras","year":2025,"lang":"en","type":"article","venue":"","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":"Natural Resources Canada","funders":"","keywords":"Intersection (aeronautics); Collision; Collision avoidance; Warning system; Variety (cybernetics); Object (grammar); Active safety","score_opus":0.003774910752146329,"score_gpt":0.19318658311699322,"score_spread":0.18941167236484688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414170517","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":[0.9180543,0.00038003072,0.034633975,0.00006538248,0.00077983766,0.00013180783,0.0000015143224,0.0036713076,0.042281885],"genre_scores_gemma":[0.9990969,0.000017003675,0.00021264098,0.00001699758,0.000021897751,0.000012120796,0.0000035444423,0.000013253254,0.0006056847],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993869,0.000022904229,0.00020230898,0.00015530639,0.000047007223,0.00018555258],"domain_scores_gemma":[0.99971926,0.00004912969,0.00001883512,0.0001715304,0.000020582072,0.000020653486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016487308,0.000112984046,0.00016071033,0.0001422435,0.00011623369,0.000014996747,0.00010793004,0.00015507294,0.000012509549],"category_scores_gemma":[0.00001336707,0.00011244111,0.000042111373,0.00029885603,0.000026330745,0.00006455481,0.000026118909,0.00022339747,0.000036885172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012915213,0.00008881762,0.05246248,0.0014881066,0.00057266274,0.00006089679,0.0022831324,0.5839575,0.01473446,0.09506728,0.038078003,0.2110775],"study_design_scores_gemma":[0.00037539264,0.000027320239,0.008952287,0.00017014744,0.000011509903,0.00001452772,0.0023014396,0.9699628,0.00682068,0.000012990619,0.0111088585,0.00024207275],"about_ca_topic_score_codex":0.000015841111,"about_ca_topic_score_gemma":0.00006334559,"teacher_disagreement_score":0.38600525,"about_ca_system_score_codex":0.00023391546,"about_ca_system_score_gemma":0.000014284669,"threshold_uncertainty_score":0.45852154},"labels":[],"label_agreement":null},{"id":"W4414197173","doi":"10.1109/cvprw67362.2025.00242","title":"Data Scaling Laws for End-to-End Autonomous Driving","year":2025,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Simple (philosophy); Ranging; Software; Motion (physics); Training (meteorology); Architecture; Training set; Data-driven","score_opus":0.017480634141401035,"score_gpt":0.26339986299111673,"score_spread":0.2459192288497157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414197173","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":[0.051099308,0.00022431697,0.89818114,0.0016365442,0.0005774127,0.0004504998,0.000097000695,0.0028992617,0.044834513],"genre_scores_gemma":[0.96781415,0.000011426959,0.0298038,0.00024414517,0.000046950634,0.00004015258,0.000052932683,0.000024474793,0.0019619558],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910456,0.0000061348137,0.0002345497,0.00028959117,0.000045919474,0.00031925875],"domain_scores_gemma":[0.99904305,0.00013226169,0.000012788338,0.0007475989,0.000017532324,0.000046782672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025848256,0.00013626838,0.0001850908,0.00013851914,0.00012545676,0.000026140311,0.00061279704,0.0001583181,0.00011270262],"category_scores_gemma":[0.00006453813,0.00014054183,0.00003644222,0.00019820406,0.000032176442,0.00012949995,0.00027662798,0.00016704788,0.00006186954],"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":[0.00002000633,0.0000632763,0.002932052,0.00021181305,0.0003664883,0.000009813303,0.00018371776,0.023614401,0.01071928,0.1708843,0.0373875,0.75360733],"study_design_scores_gemma":[0.00038036308,0.000019063398,0.0028227794,0.000047635676,0.000043813932,0.0000036993522,0.00010374817,0.5817324,0.012662062,0.0028010865,0.3990571,0.00032623525],"about_ca_topic_score_codex":0.000013178217,"about_ca_topic_score_gemma":0.00017110487,"teacher_disagreement_score":0.91671485,"about_ca_system_score_codex":0.00006940762,"about_ca_system_score_gemma":0.00003917183,"threshold_uncertainty_score":0.5731129},"labels":[],"label_agreement":null},{"id":"W4414304430","doi":"10.1016/j.asoc.2025.113924","title":"Secure and efficient vehicle control of autonomous vehicles using federated deep reinforcement learning","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Reinforcement learning; Deep learning; Action (physics); State (computer science); Field (mathematics); Control (management)","score_opus":0.004252467708513247,"score_gpt":0.1995272891228834,"score_spread":0.19527482141437014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414304430","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":[0.5637107,0.00034157446,0.43392396,0.00001909346,0.000053621312,0.00016713196,3.143357e-7,0.00044433682,0.0013393054],"genre_scores_gemma":[0.9983163,0.000005313259,0.00155591,0.000064206615,0.000015821946,0.000004664257,0.0000031137602,0.000022896833,0.000011795174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989161,0.000020247542,0.00041265532,0.00022724835,0.0000902618,0.00033348912],"domain_scores_gemma":[0.99953425,0.00016013689,0.00009068105,0.00013570812,0.000039481733,0.000039760816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002581237,0.00019333864,0.00032848865,0.00013196937,0.00033127284,0.000027553218,0.00011772122,0.0001825866,0.0000048079896],"category_scores_gemma":[0.000018951368,0.00021520868,0.000039761962,0.00025176175,0.00010092198,0.000022297412,0.00010382189,0.00038547607,0.0000031391276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017417353,0.00001262983,0.0011486459,0.00009205754,0.00009029837,0.0000020212299,0.00037588703,0.93940824,0.027109375,0.005166799,0.0000034050104,0.026573248],"study_design_scores_gemma":[0.00089301215,0.000018074386,0.001245249,0.000055761346,0.00003669198,0.000002967979,0.00029676355,0.98601526,0.011004983,0.00015670764,0.00009941017,0.00017509515],"about_ca_topic_score_codex":0.0000114553295,"about_ca_topic_score_gemma":0.0000016298475,"teacher_disagreement_score":0.4346056,"about_ca_system_score_codex":0.00009343198,"about_ca_system_score_gemma":0.000034227196,"threshold_uncertainty_score":0.87759554},"labels":[],"label_agreement":null},{"id":"W4414348348","doi":"10.1109/tits.2025.3607860","title":"A Proactive Risk Prediction Framework for Cut-In Maneuvers Incorporating Inherent Driving Styles","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Transportation of Ontario","funders":"National Key Research and Development Program of China","keywords":"Intelligent transportation system; Risk management; Vehicle dynamics; Risk assessment; Field (mathematics)","score_opus":0.012795534962783104,"score_gpt":0.24271212219060395,"score_spread":0.22991658722782085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414348348","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":[0.13762431,0.00008206598,0.858659,0.00002740477,0.0012190004,0.0013069761,0.00020422286,0.0006658894,0.000211089],"genre_scores_gemma":[0.99686617,0.0001644356,0.001567524,0.000009512277,0.000026828377,0.0012057978,0.000028932584,0.00003732363,0.00009348308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984502,0.000051824903,0.0007552741,0.0003332838,0.0001478482,0.00026157784],"domain_scores_gemma":[0.9992804,0.00026520493,0.000112628404,0.00022565444,0.00006936039,0.000046736066],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002663919,0.00024967146,0.00029605816,0.0004856983,0.0002038731,0.000030024736,0.00013535246,0.00036667904,0.000011641306],"category_scores_gemma":[0.0000070706415,0.00027525157,0.00013651469,0.00050169864,0.00005198174,0.00017970886,2.0301356e-7,0.0006023162,0.000010029949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093154136,0.00012610544,0.003731535,0.00025369032,0.00026074774,0.0000027368908,0.0013971087,0.9691823,0.00028243315,0.008157243,0.000019601934,0.016493311],"study_design_scores_gemma":[0.0017431778,0.0004054877,0.017266473,0.0021740447,0.00043317914,0.0000048085135,0.016551588,0.8280278,0.12457699,0.006548899,0.0012678743,0.0009996825],"about_ca_topic_score_codex":0.00010703555,"about_ca_topic_score_gemma":0.0008568621,"teacher_disagreement_score":0.85924184,"about_ca_system_score_codex":0.00033289802,"about_ca_system_score_gemma":0.000045237073,"threshold_uncertainty_score":0.99996996},"labels":[],"label_agreement":null},{"id":"W4414646699","doi":"10.1007/978-3-031-95111-4_18","title":"Leveraging Lidar-Based Simulations to Quantify the Complexity of the Surrounding Environment for Autonomous Vehicles","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Identification (biology); Viewpoints; Scalability; Function (biology); Replicate; Virtual machine; Government (linguistics)","score_opus":0.025336625512591505,"score_gpt":0.224678708058786,"score_spread":0.1993420825461945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414646699","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":[0.004242055,0.0010751893,0.9888613,0.0014091736,0.0006669383,0.0012833123,0.0002198738,0.00046017914,0.0017819717],"genre_scores_gemma":[0.9957349,0.00001257643,0.0036951257,0.000095239004,0.00006849353,0.00004789681,0.000021014188,0.000085906286,0.00023886708],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870837,0.000011644247,0.00047105522,0.0003018286,0.00015793991,0.00034914576],"domain_scores_gemma":[0.9982828,0.00093934656,0.00008084293,0.000642219,0.000022145654,0.000032655546],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021186813,0.00040555792,0.00045193805,0.0002565205,0.00014719045,0.000020824647,0.000514984,0.00043638583,0.000034641016],"category_scores_gemma":[0.00013526167,0.00032832864,0.00020773463,0.00012663293,0.00013158108,0.000028228844,0.00012226633,0.00085237506,0.0000024286217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005885877,0.0000061784885,0.00016286375,0.00022087108,0.000070350565,9.665833e-7,0.00016944458,0.98599255,0.00077364984,0.009210954,0.000012448151,0.003373813],"study_design_scores_gemma":[0.000335321,0.000027316262,0.0016578195,0.0007265936,0.000093143404,0.0000021189371,0.000004014934,0.95876783,0.006763828,0.007815068,0.023267915,0.00053902],"about_ca_topic_score_codex":0.000013207203,"about_ca_topic_score_gemma":0.0002390132,"teacher_disagreement_score":0.9914928,"about_ca_system_score_codex":0.00039539568,"about_ca_system_score_gemma":0.000058968435,"threshold_uncertainty_score":0.99991685},"labels":[],"label_agreement":null},{"id":"W4414653818","doi":"10.2139/ssrn.5546074","title":"Motion Sickness Prediction in Intelligent Electric Vehicles Using Collaborative Subjective-Objective Data Fusion","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Motion (physics); Artificial neural network; Sensor fusion; Key (lock); Feature (linguistics); Convolutional neural network; Support vector machine; Motion sickness","score_opus":0.014072371592693558,"score_gpt":0.25511315927989536,"score_spread":0.2410407876872018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414653818","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":[0.7151657,0.013523984,0.26760715,0.000105358224,0.0011340883,0.0009202037,0.00022375173,0.00054317096,0.0007766138],"genre_scores_gemma":[0.98444813,0.014698933,0.000244864,0.00000946397,0.00023862362,0.000028944985,0.00016907419,0.000051981824,0.00010995988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961583,0.00027320845,0.00075428776,0.000680748,0.00028242182,0.0018510097],"domain_scores_gemma":[0.99864155,0.00009392304,0.0002729301,0.000704768,0.0002290218,0.000057826135],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0021108072,0.00049115415,0.0005862038,0.0010311835,0.0002975286,0.00006538588,0.0009693348,0.0009117635,0.000010894446],"category_scores_gemma":[0.00011537614,0.00053387525,0.00010290432,0.0011645629,0.000075563126,0.00036878162,0.0006162822,0.008725932,0.000008119232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056315004,0.00045892954,0.011925516,0.00033308007,0.0021303596,0.00004427035,0.0022647192,0.6221485,0.0072854757,0.018688843,0.00011595851,0.33404118],"study_design_scores_gemma":[0.00093137095,0.00021177508,0.005324215,0.000505454,0.00027075614,0.00020978536,0.0028653415,0.7962047,0.0056616715,0.18690118,0.00015511239,0.0007586716],"about_ca_topic_score_codex":0.00015564784,"about_ca_topic_score_gemma":0.0016593942,"teacher_disagreement_score":0.33328253,"about_ca_system_score_codex":0.007878075,"about_ca_system_score_gemma":0.0040260986,"threshold_uncertainty_score":0.9997113},"labels":[],"label_agreement":null},{"id":"W4414807106","doi":"10.1016/b978-0-443-22392-1.00002-x","title":"Language for automated driving","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Neurotrauma Foundation","funders":"","keywords":"Terminology; Automation; Software deployment; Confusion; Taxonomy (biology); Liability","score_opus":0.005438265603353601,"score_gpt":0.21916022217414435,"score_spread":0.21372195657079074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414807106","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000047479163,0.0012251263,0.00023802757,0.000033847973,0.0003807486,0.00050734024,0.00006205049,0.0052454155,0.99226],"genre_scores_gemma":[0.0046083205,0.000042105028,0.0014346527,0.000076670105,0.00010566164,0.00007786194,0.00004830484,0.00010103193,0.9935054],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991051,0.000003765576,0.00030041536,0.00024718302,0.00007056614,0.00027298645],"domain_scores_gemma":[0.9993796,0.00007286148,0.00005291829,0.00042530487,0.000028056942,0.000041259675],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009951694,0.00032720537,0.0004165897,0.00017282461,0.00008099622,0.000014462152,0.000261839,0.0006854141,0.00007977574],"category_scores_gemma":[0.000012239071,0.00035186918,0.00018111618,0.000010083468,0.000061640305,0.000018539999,0.0000671991,0.00043215003,0.00007937456],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023128111,0.0000012903572,0.0000011879462,0.00019602763,0.00016405921,0.000013882563,0.00009747915,0.000037986432,0.00008716574,0.014264534,0.0017230203,0.9834111],"study_design_scores_gemma":[0.00020216912,0.000016907697,0.000010044416,0.00034368542,0.00009985765,0.0000057129273,0.0000062534127,0.008879523,0.00035310342,0.0038722418,0.98581713,0.0003933933],"about_ca_topic_score_codex":7.844276e-8,"about_ca_topic_score_gemma":0.000013924311,"teacher_disagreement_score":0.9840941,"about_ca_system_score_codex":0.000105317755,"about_ca_system_score_gemma":0.000046135996,"threshold_uncertainty_score":0.9998933},"labels":[],"label_agreement":null},{"id":"W4415135180","doi":"10.1080/15472450.2025.2526401","title":"Unifying expert knowledge and field data toward an enhanced scenario description for CAV certification: a comprehensive scenario-based approach","year":2025,"lang":"en","type":"article","venue":"Journal of Intelligent Transportation Systems","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":"Polytechnique Montréal","funders":"","keywords":"Field (mathematics); Expert system; Key (lock); Knowledge-based systems","score_opus":0.09015233390361652,"score_gpt":0.31115895885209666,"score_spread":0.22100662494848014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415135180","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":[0.089902595,0.004047917,0.90465426,0.0001445837,0.0006323562,0.0003956063,0.000021862848,0.00009390672,0.000106923166],"genre_scores_gemma":[0.99451405,0.00037918604,0.0047801603,0.000045115466,0.0000711075,0.000029488501,0.00013549859,0.000015929787,0.000029492572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875534,0.000039160976,0.0007630769,0.0001947048,0.00010302475,0.00014467792],"domain_scores_gemma":[0.99905443,0.000109525485,0.00017121356,0.00027822453,0.00032504898,0.00006156018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003080866,0.00015123254,0.0003107083,0.0002600037,0.00009411766,0.000046412795,0.0002872161,0.00018990632,0.0000035611772],"category_scores_gemma":[0.000017649836,0.00014287182,0.0000602466,0.00019079173,0.000036994003,0.00032481508,0.000004116654,0.000223829,8.7815437e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019879846,0.0011730666,0.006554839,0.007988457,0.0016827955,0.000026537315,0.03799793,0.57892036,0.13873377,0.012341934,0.0055171326,0.20707521],"study_design_scores_gemma":[0.0015861061,0.00038120127,0.0031567402,0.00099996,0.00021906613,0.000023993427,0.011830196,0.8654508,0.08379395,0.00014534677,0.031927705,0.00048495247],"about_ca_topic_score_codex":0.00003442503,"about_ca_topic_score_gemma":0.000051927225,"teacher_disagreement_score":0.9046114,"about_ca_system_score_codex":0.00009219304,"about_ca_system_score_gemma":0.000094323594,"threshold_uncertainty_score":0.5826144},"labels":[],"label_agreement":null},{"id":"W4415147754","doi":"10.1145/3748777.3748800","title":"ChargeNav: End-to-End Fleet Optimization and Energy Aware Navigation for Electric Trucks","year":2025,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Truck; Energy (signal processing); Electric energy; Energy consumption; Global Positioning System","score_opus":0.003952047787170688,"score_gpt":0.20523450853202965,"score_spread":0.20128246074485898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415147754","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":[0.023713058,0.00023341435,0.97066426,0.0004040782,0.00012009729,0.00013278531,0.000008605149,0.0006682299,0.004055459],"genre_scores_gemma":[0.9936491,0.00008746991,0.0047855866,0.00015909347,0.000021144424,0.000051399944,0.00005850133,0.00001436539,0.0011733128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951977,0.000005729081,0.00013680337,0.00014318078,0.00003251667,0.00016199281],"domain_scores_gemma":[0.99978983,0.000035083616,0.0000119251945,0.00010799601,0.000027348415,0.000027824019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000060738686,0.00009400032,0.000107412714,0.00014799472,0.00008212667,0.00001148631,0.00006567142,0.00014872353,0.000046433623],"category_scores_gemma":[0.000007735927,0.00009706416,0.00002149513,0.0002797504,0.0000103375705,0.00006544572,0.000015660693,0.000063711566,0.000002446829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004204118,0.000031924064,0.0006786844,0.00014730605,0.0001546331,0.0000018248543,0.0001433772,0.42627472,0.012001523,0.13554627,0.0039000134,0.42107767],"study_design_scores_gemma":[0.00025537497,0.000032944707,0.00025014373,0.000012138765,0.000016159991,0.0000022179838,0.000018090775,0.948355,0.041951228,0.0012208201,0.007761029,0.0001248573],"about_ca_topic_score_codex":0.000010397938,"about_ca_topic_score_gemma":0.00000982663,"teacher_disagreement_score":0.9699361,"about_ca_system_score_codex":0.00004824221,"about_ca_system_score_gemma":0.000015907839,"threshold_uncertainty_score":0.39581618},"labels":[],"label_agreement":null},{"id":"W4415210561","doi":"10.3390/s25206312","title":"VR Human-Centric Winter Lane Detection: Performance and Driving Experience Evaluation","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Autonomous Vehicle Technology and Safety","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":"Ontario Tech University; Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia e Innovación; Canada Research Chairs","keywords":"Driving simulator; Intuition; Snow; Perception; Situation awareness; Poison control; Virtual reality; Detector","score_opus":0.008012825956749134,"score_gpt":0.23942946377183436,"score_spread":0.23141663781508523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415210561","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":[0.9924482,0.00014583912,0.0006130766,0.00003182585,0.00017317195,0.00009289916,2.2335972e-7,0.0003135358,0.006181196],"genre_scores_gemma":[0.9995562,0.00004468021,0.000063667816,0.00001539349,0.000018138388,0.000012236763,0.0000010242253,0.0000060208336,0.00028266187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959725,0.000012659567,0.0001015391,0.000110579305,0.000057117104,0.00012087394],"domain_scores_gemma":[0.9998203,0.000015038832,0.000011426521,0.000119811186,0.00001902711,0.000014394599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010016273,0.000069782705,0.00007033606,0.00008986996,0.000115583454,0.000009297789,0.00005036622,0.00007749994,0.000038430495],"category_scores_gemma":[0.000015117861,0.000072287294,0.000012483123,0.00014442652,0.00004373543,0.000058186586,0.000020463127,0.00012549428,0.000013128431],"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":[0.000018426901,0.000032318796,0.13006303,0.00014754677,0.00010407811,0.000009475198,0.004018198,0.043661244,0.028626435,0.00084251864,0.00021724426,0.7922595],"study_design_scores_gemma":[0.00044708556,0.000034969107,0.27381402,0.00006665258,0.000030290266,0.000019478306,0.00052030134,0.6582027,0.0620839,0.0002456231,0.004310329,0.00022463173],"about_ca_topic_score_codex":0.000002645635,"about_ca_topic_score_gemma":0.000020836414,"teacher_disagreement_score":0.79203486,"about_ca_system_score_codex":0.00005778954,"about_ca_system_score_gemma":0.000004645136,"threshold_uncertainty_score":0.29477903},"labels":[],"label_agreement":null},{"id":"W4415353334","doi":"10.1109/icnc-fskd67701.2025.11198112","title":"An Intelligent Parallel Distributed Compensation Control Strategy for Robust Autonomous Vehicle Path-Following: Tackling Speed Variations, Road Uncertainty, and Actuator Limits","year":2025,"lang":"","type":"article","venue":"","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":"University of Ottawa","funders":"","keywords":"Control theory (sociology); Robustness (evolution); Actuator; Fuzzy control system; Adaptability; Fuzzy logic; Lyapunov function; Model predictive control; Lyapunov stability","score_opus":0.018776899896826568,"score_gpt":0.2610502120796446,"score_spread":0.242273312182818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415353334","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":[0.19112425,0.00090190826,0.80129385,0.0021677478,0.0006659375,0.0021593394,0.0003438447,0.0009863582,0.00035674407],"genre_scores_gemma":[0.9905563,0.0002143189,0.007961228,0.00022486744,0.00010109009,0.00012265037,0.0005501352,0.000069829715,0.0001995718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964024,0.00013920535,0.0013439911,0.00095261686,0.00020522819,0.0009565654],"domain_scores_gemma":[0.99797124,0.0005319553,0.00025396608,0.0007321977,0.00024254672,0.00026812588],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008795177,0.0007166124,0.000970419,0.0003063309,0.0007956148,0.00031309485,0.0004988704,0.0009738159,0.000092824295],"category_scores_gemma":[0.00021353428,0.00078734546,0.0002849626,0.00050884875,0.00016061815,0.0005187224,0.000075744174,0.0006656265,0.00001930006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033141804,0.00038436815,0.0017590363,0.00016385072,0.0010079661,0.000014926845,0.0003239813,0.8641752,0.0026955141,0.055791695,0.0001742394,0.073177814],"study_design_scores_gemma":[0.0040553086,0.00042546474,0.012606267,0.00011475537,0.00055408286,0.0000051774127,0.000865106,0.9748967,0.0010059677,0.003964023,0.00077431375,0.0007328158],"about_ca_topic_score_codex":0.0002619032,"about_ca_topic_score_gemma":0.00017793098,"teacher_disagreement_score":0.79943204,"about_ca_system_score_codex":0.0006430739,"about_ca_system_score_gemma":0.00042225124,"threshold_uncertainty_score":0.9994578},"labels":[],"label_agreement":null},{"id":"W4415366454","doi":"10.1109/jiot.2025.3623592","title":"Communication-Aware Hierarchical Driving Control for Collaborative Autonomous Driving","year":2025,"lang":"","type":"article","venue":"IEEE Internet of Things Journal","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":"University of Guelph","funders":"National University Research Fund of China; National Natural Science Foundation of China","keywords":"Reinforcement learning; Markov decision process; Convergence (economics); Process (computing); Partially observable Markov decision process; Markov process; Control (management); Adaptation (eye); Motion (physics)","score_opus":0.007173115213375081,"score_gpt":0.24891391833189416,"score_spread":0.2417408031185191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415366454","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":[0.15548274,0.0036048326,0.8269708,0.005411402,0.0030492626,0.0009373899,0.000040585724,0.0003135299,0.004189433],"genre_scores_gemma":[0.9859185,0.00055466796,0.011555542,0.00032988915,0.00015579376,0.000051919447,0.0000050185154,0.00007225843,0.0013563733],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962815,0.00029540487,0.0019468932,0.0004143331,0.00028114163,0.0007807009],"domain_scores_gemma":[0.9957484,0.001659884,0.0009161016,0.0006941483,0.0007862916,0.00019517144],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0017572533,0.0005479422,0.0011462528,0.0006698722,0.0005519535,0.0002521844,0.0018931337,0.0008050791,0.00018104093],"category_scores_gemma":[0.00056231226,0.0005923135,0.00051025854,0.00047061633,0.00062075595,0.0007237671,0.00023061143,0.00293424,0.000012638883],"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":[0.0033287772,0.0027636187,0.043195527,0.0030124274,0.026069345,0.00026417937,0.09670776,0.12212567,0.04055292,0.26721153,0.06837209,0.32639617],"study_design_scores_gemma":[0.0067548165,0.0007543595,0.0022386904,0.005390829,0.00080422417,0.00027414877,0.00270647,0.8985678,0.031060303,0.034987554,0.0153666455,0.0010941692],"about_ca_topic_score_codex":0.00002529045,"about_ca_topic_score_gemma":0.00003246472,"teacher_disagreement_score":0.8304358,"about_ca_system_score_codex":0.0007102227,"about_ca_system_score_gemma":0.00064281933,"threshold_uncertainty_score":0.9996528},"labels":[],"label_agreement":null},{"id":"W4415539267","doi":"10.1007/s11432-024-4538-3","title":"Advanced trajectory prediction framework integrating diverse driving styles for autonomous vehicles","year":2025,"lang":"en","type":"article","venue":"Science China Information Sciences","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Trajectory; Key (lock); Component (thermodynamics); Residual; Categorization; Fuzzy logic; Motion (physics)","score_opus":0.006670885274578432,"score_gpt":0.2512858876998574,"score_spread":0.24461500242527895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415539267","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":[0.6357267,0.000060056314,0.34702262,0.000228781,0.0008623081,0.0003347991,0.000014378376,0.00090558553,0.014844774],"genre_scores_gemma":[0.9776639,0.000021205358,0.022132332,0.00008584462,0.000018075803,0.000051096707,0.00000278521,0.000002908404,0.000021890297],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878776,0.000008820638,0.00036767285,0.00019553158,0.00026588407,0.00037433257],"domain_scores_gemma":[0.99946994,0.00012277275,0.000088284316,0.00017930967,0.00009049993,0.000049217826],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0012177622,0.00013577247,0.00013252583,0.0005748981,0.0013800641,0.00021560598,0.0006696578,0.00010559131,0.000009286659],"category_scores_gemma":[0.00052918814,0.00011902091,0.00004668606,0.0015539695,0.0010478646,0.0038668541,0.00007824176,0.0002168668,0.0000116468],"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":[0.000009277351,0.000017669212,0.009174866,0.0000946385,0.000013644978,1.8260093e-7,0.004600526,0.16801572,0.0047099846,0.23386237,0.0002858751,0.5792152],"study_design_scores_gemma":[0.0002587658,0.000100938916,0.072465636,0.00017111382,0.000008872798,0.0000026124726,0.008411541,0.8851533,0.014725976,0.01569804,0.0027455213,0.00025768092],"about_ca_topic_score_codex":0.0000069145985,"about_ca_topic_score_gemma":0.0000097555085,"teacher_disagreement_score":0.7171376,"about_ca_system_score_codex":0.0001844866,"about_ca_system_score_gemma":0.00025130258,"threshold_uncertainty_score":0.99992},"labels":[],"label_agreement":null},{"id":"W4415540607","doi":"10.1145/3746027.3754772","title":"OmniGen: Unified Multimodal Sensor Generation for Autonomous Driving","year":2025,"lang":"","type":"article","venue":"","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":"University of Toronto","funders":"National Key Research and Development Program of China","keywords":"Focus (optics); Generative grammar; Consistency (knowledge bases); Exploit; Decoding methods; Activity recognition; Transformer","score_opus":0.012505893275675526,"score_gpt":0.24351017709021136,"score_spread":0.23100428381453583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415540607","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":[0.15866832,0.00066022424,0.81894094,0.0024315335,0.0022513827,0.0013503039,0.000030559982,0.0016279571,0.014038782],"genre_scores_gemma":[0.94559413,0.0001348595,0.038389128,0.00025179962,0.00027448652,0.00014199397,0.00004698203,0.000062272826,0.015104338],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975543,0.00006240683,0.0008240014,0.0006775838,0.00010054345,0.0007811868],"domain_scores_gemma":[0.9987175,0.00026540484,0.000089536166,0.0006938058,0.00013806904,0.000095708856],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00053781626,0.00048032618,0.0005321333,0.00036569015,0.0005873717,0.00008968703,0.00036001313,0.0009825637,0.0002731014],"category_scores_gemma":[0.00015952592,0.0005513744,0.00025031477,0.00039658273,0.00012880165,0.0002017356,0.00012356663,0.0005063267,0.00010116905],"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":[0.0000877685,0.00028829626,0.0018213363,0.0004704424,0.0011188397,0.000017554152,0.0007182952,0.18202727,0.19534595,0.3051383,0.005302525,0.30766344],"study_design_scores_gemma":[0.0011462015,0.00006879259,0.0009858488,0.000041020572,0.0001287206,0.000004228784,0.00019991196,0.90491915,0.072959684,0.0009232624,0.018121384,0.0005017769],"about_ca_topic_score_codex":0.00004145487,"about_ca_topic_score_gemma":0.0001730566,"teacher_disagreement_score":0.7869258,"about_ca_system_score_codex":0.0004288591,"about_ca_system_score_gemma":0.00025248513,"threshold_uncertainty_score":0.99969375},"labels":[],"label_agreement":null},{"id":"W4415555224","doi":"10.1007/978-3-031-95115-2_11","title":"Track Gauge Anomaly Detection Algorithm Combining Global and Local Detection Based on LiDAR Data","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Research Canada; National Research Council Canada; Western University","funders":"","keywords":"Track (disk drive); Anomaly detection; Gauge (firearms); Ground-penetrating radar; Lidar; Radar; Nonlinear system","score_opus":0.0067813334438034615,"score_gpt":0.19734028620505983,"score_spread":0.19055895276125637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415555224","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":[0.00036029704,0.0014143519,0.98813677,0.000034888395,0.00071917265,0.0002735994,0.00022569639,0.0012685286,0.0075666727],"genre_scores_gemma":[0.9962217,0.00010286231,0.0031624835,0.00005223493,0.00013602643,0.000016710468,0.00012580321,0.00009915394,0.00008299206],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838465,0.000010936397,0.0004004836,0.0006313499,0.00017489535,0.00039768798],"domain_scores_gemma":[0.9988706,0.00024603502,0.000057459925,0.00073558727,0.000022869803,0.00006743227],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019393308,0.00058579684,0.00051640556,0.00040309815,0.00007677978,0.000035842862,0.00034212545,0.0012601084,0.000025459063],"category_scores_gemma":[0.000084828825,0.0006930687,0.00007412826,0.00018937943,0.00006529521,0.000108338725,0.00012315436,0.0015553931,0.000007826482],"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":[0.000017828146,0.000009393356,0.000027608408,0.00024221983,0.000078050456,0.000051844083,0.00001491399,0.37590703,0.0002246157,0.00032098993,0.000005037684,0.62310046],"study_design_scores_gemma":[0.00047238072,0.000096355885,0.00033209383,0.00050161703,0.00006828604,0.00003383424,0.0000010850364,0.9895774,0.002738458,0.0012802017,0.0043457607,0.00055253605],"about_ca_topic_score_codex":0.000027805005,"about_ca_topic_score_gemma":0.0014510058,"teacher_disagreement_score":0.9958614,"about_ca_system_score_codex":0.000502454,"about_ca_system_score_gemma":0.000038434013,"threshold_uncertainty_score":0.9995521},"labels":[],"label_agreement":null},{"id":"W4415598760","doi":"10.1115/detc2025-167614","title":"Decoupled Dynamics Framework With Neural Fields for 3D Spatio-Temporal Prediction of Vehicle Collisions","year":2025,"lang":"","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Collision; Generalization; Artificial neural network; Translation (biology); Rotation (mathematics); Motion (physics); Vehicle dynamics; Multilayer perceptron; Dynamics (music)","score_opus":0.006812996415297145,"score_gpt":0.2272402923453866,"score_spread":0.22042729593008945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415598760","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":[0.32862595,0.00018680388,0.6669009,0.0011270456,0.00052171055,0.0007250615,0.00016308414,0.00035064458,0.0013988343],"genre_scores_gemma":[0.9492307,0.000077503144,0.04958108,0.00009489574,0.000040842067,0.00009405402,0.000094495656,0.000033958415,0.0007524742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983826,0.00002308683,0.0006859692,0.00035988697,0.00013304067,0.00041539152],"domain_scores_gemma":[0.9986225,0.00045166002,0.00013123186,0.00054720003,0.00018109281,0.00006631348],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019499454,0.0002984286,0.00047858595,0.00022224268,0.00026054148,0.000023488172,0.00028923913,0.0009800097,0.00016731927],"category_scores_gemma":[0.00011859529,0.0002872644,0.00013453362,0.0006686509,0.00018741284,0.00016412986,0.00008469609,0.0006385844,0.0000034126942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014884399,0.0004927977,0.30237764,0.0010800483,0.0010751234,0.0000085127685,0.0005804821,0.36307096,0.00007794663,0.2754976,0.0012319414,0.05301851],"study_design_scores_gemma":[0.0012149762,0.0005196997,0.013273442,0.00023563717,0.00019118794,0.0000023861276,0.00026664164,0.9771358,0.00091207196,0.005532838,0.00050580554,0.00020956484],"about_ca_topic_score_codex":0.000100806465,"about_ca_topic_score_gemma":0.0010525087,"teacher_disagreement_score":0.62060475,"about_ca_system_score_codex":0.00018644381,"about_ca_system_score_gemma":0.00016896582,"threshold_uncertainty_score":0.999958},"labels":[],"label_agreement":null},{"id":"W4415782431","doi":"10.1177/02783649251365282","title":"Learning an interpretable logic monitor for risk-aware and socially-compliant trajectory planning","year":2025,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","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":"Artificial Intelligence in Medicine (Canada)","funders":"Toyota Research Institute, North America","keywords":"Leverage (statistics); Task (project management); Parameterized complexity; Trajectory; Software deployment; Robot; Control (management); Plan (archaeology)","score_opus":0.05053092852164519,"score_gpt":0.3791718383832804,"score_spread":0.3286409098616352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415782431","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":[0.62059724,0.0020460985,0.36948183,0.0045077233,0.0014459171,0.00028201743,0.000011084191,0.00012534129,0.0015027637],"genre_scores_gemma":[0.9960527,0.00034780498,0.0031212575,0.000025139418,0.00016460767,0.0000045503957,0.0000015362161,0.000012043075,0.00027037069],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990978,0.000100334924,0.00025153035,0.00007952007,0.00027285944,0.00019795347],"domain_scores_gemma":[0.9988786,0.00051762146,0.0000701315,0.00008178798,0.00041456684,0.000037277237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015188,0.00007452606,0.00013670532,0.0002590837,0.00023422141,0.00007549976,0.0006186502,0.000090609276,0.000008141452],"category_scores_gemma":[0.00025488436,0.00005687497,0.000053056072,0.00009428308,0.00013056073,0.000116008494,0.000098437646,0.0010378261,0.0000018350473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019502136,0.00004781954,0.00661418,0.000035105495,0.0004984019,0.000028462213,0.0012532126,0.9368679,0.0028166499,0.011003911,0.00094896555,0.03969039],"study_design_scores_gemma":[0.0010432196,0.00044872778,0.01936103,0.00030167427,0.000051780964,0.000076250166,0.0038322557,0.90723485,0.0020669084,0.063917406,0.0014860051,0.00017990805],"about_ca_topic_score_codex":0.000009389867,"about_ca_topic_score_gemma":0.0000108268705,"teacher_disagreement_score":0.37545547,"about_ca_system_score_codex":0.00015397383,"about_ca_system_score_gemma":0.0001159997,"threshold_uncertainty_score":0.45088956},"labels":[],"label_agreement":null},{"id":"W4416241114","doi":"10.1109/iccv51701.2025.02363","title":"ForeSight: Multi-View Streaming Joint Object Detection and Trajectory Forecasting","year":2025,"lang":"en","type":"article","venue":"","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":"University of Toronto","funders":"","keywords":"Leverage (statistics); Object detection; Trajectory; Limiting; Transformer; Consistency (knowledge bases); Streaming data; Kalman filter","score_opus":0.01795997568056558,"score_gpt":0.20937027915394354,"score_spread":0.19141030347337795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416241114","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":[0.7393172,0.0013887588,0.24895628,0.000035227848,0.00018666513,0.00019909303,0.0000013157754,0.0013554367,0.008560038],"genre_scores_gemma":[0.9934647,0.000057515073,0.006144886,0.000024894442,0.000009384146,0.000018420778,7.6619995e-7,0.000012747393,0.0002666807],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946564,0.000010252732,0.00017342766,0.00014177978,0.000029800989,0.00017907974],"domain_scores_gemma":[0.99979734,0.000038108305,0.000014663865,0.00011594404,0.0000114624945,0.000022457782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000107104766,0.00011601029,0.00014381131,0.00010034631,0.0001471478,0.000016491495,0.000048094706,0.00013520874,0.00001686625],"category_scores_gemma":[0.000025183317,0.00010905015,0.000033953413,0.00014111912,0.000045955894,0.00007588831,0.000028851824,0.00018724846,0.000004456736],"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":[0.000006707693,0.000019643887,0.002413695,0.00027445253,0.00009969841,0.0000089660025,0.00027190757,0.0047753872,0.014580011,0.0011419016,0.000048620437,0.976359],"study_design_scores_gemma":[0.00046188856,0.000030655752,0.031216897,0.00008780367,0.000029702742,0.000017567143,0.00018546238,0.9122577,0.053840358,0.0004907221,0.0011720218,0.00020927294],"about_ca_topic_score_codex":0.000024355724,"about_ca_topic_score_gemma":0.00041315443,"teacher_disagreement_score":0.97614974,"about_ca_system_score_codex":0.000055967437,"about_ca_system_score_gemma":0.000012408268,"threshold_uncertainty_score":0.44469357},"labels":[],"label_agreement":null},{"id":"W4416251046","doi":"10.1109/ijcnn64981.2025.11228483","title":"AGNS : Adversarial Attack against Human Trajectory Model Based on Attention-guidance and Node-selection","year":2025,"lang":"","type":"article","venue":"","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":"Artificial Intelligence in Medicine (Canada)","funders":"Nature","keywords":"Trajectory; Adversarial system; Collision; Component (thermodynamics); Code (set theory); Key (lock)","score_opus":0.01153306958931547,"score_gpt":0.24552172225021676,"score_spread":0.2339886526609013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416251046","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":[0.5178455,0.00030623667,0.38357443,0.00092719146,0.00074685615,0.0005925527,0.0000254249,0.0012694807,0.09471231],"genre_scores_gemma":[0.99269694,0.00009635385,0.0024488422,0.00053710135,0.00007801655,0.0000393685,0.00001862725,0.000044679487,0.0040400885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801296,0.000060537583,0.00057425443,0.0006639669,0.0001638388,0.00052446034],"domain_scores_gemma":[0.9992429,0.0000844819,0.00008190225,0.0004372693,0.000067576715,0.000085875305],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029485047,0.00044098432,0.00041109187,0.0004228662,0.0006088994,0.000045965095,0.00023107673,0.0007419255,0.00013593913],"category_scores_gemma":[0.000032566986,0.0005155901,0.00016898786,0.00043784856,0.00020060582,0.00017718943,0.00006211561,0.0008294101,0.000041196105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106836655,0.00018408246,0.0030512295,0.00016961731,0.00014325397,0.000004099396,0.0000623173,0.96478885,0.010483426,0.009758059,0.0014729622,0.009775264],"study_design_scores_gemma":[0.0019438987,0.00010935495,0.0109451255,0.00018456532,0.00007628671,7.2214243e-7,0.00004115354,0.98303777,0.0021586393,0.00032911255,0.0007318222,0.00044153453],"about_ca_topic_score_codex":0.000024967676,"about_ca_topic_score_gemma":0.00015773717,"teacher_disagreement_score":0.4748514,"about_ca_system_score_codex":0.00039433106,"about_ca_system_score_gemma":0.0001608222,"threshold_uncertainty_score":0.9997296},"labels":[],"label_agreement":null},{"id":"W4416602451","doi":"10.1177/03611981251372464","title":"Assessing Risk of Collision with Fleet Telematics for Usage-Based Insurance: Case Study from South Korean Car Rental Operations","year":2025,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","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":"University of Toronto","funders":"","keywords":"Telematics; Renting; Collision; Sample (material); Automotive industry; Fleet management; Purchasing","score_opus":0.053229704179525224,"score_gpt":0.3669877148059784,"score_spread":0.3137580106264532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416602451","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":[0.94179904,0.000108352084,0.05537614,0.00028086785,0.00018454166,0.0017433313,0.00042748105,0.000055968638,0.0000242924],"genre_scores_gemma":[0.9916426,0.00009638135,0.007967705,0.000005898075,0.00003466282,0.00013259225,0.00003282247,0.000044108237,0.00004323276],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9958859,0.0007067019,0.0013216961,0.00029097602,0.0013143476,0.0004803694],"domain_scores_gemma":[0.9956597,0.0013555214,0.00025765924,0.00048398855,0.0021070186,0.00013612215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003143085,0.00022810236,0.0005162923,0.0009098976,0.0010649957,0.00009100941,0.00060231274,0.00020599357,0.000027409013],"category_scores_gemma":[0.00015412955,0.00016872346,0.00022512763,0.0017432572,0.00046240195,0.00047646902,0.0000058916603,0.0016837183,9.361379e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010637603,0.0004748493,0.7797967,0.00035370048,0.0005578479,0.0002978764,0.0074026906,0.20397815,0.001790505,0.00023807758,0.00019194318,0.0038538587],"study_design_scores_gemma":[0.0051091653,0.0011181631,0.9243252,0.0007201861,0.00028779925,0.0000017917721,0.034253422,0.02326246,0.009803369,0.00071484863,0.00016093036,0.00024265479],"about_ca_topic_score_codex":0.005455341,"about_ca_topic_score_gemma":0.052556176,"teacher_disagreement_score":0.1807157,"about_ca_system_score_codex":0.0002377546,"about_ca_system_score_gemma":0.0007363733,"threshold_uncertainty_score":0.9647322},"labels":[],"label_agreement":null},{"id":"W4416662255","doi":"10.1016/j.robot.2025.105261","title":"Enhancing vehicle lane change prediction via transfer learning infused with social interaction dynamics","year":2025,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adaptability; Transfer of learning; Precision and recall; Transfer (computing); Recall; Deep learning; Dynamics (music)","score_opus":0.00735317430260381,"score_gpt":0.19807176804934531,"score_spread":0.1907185937467415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416662255","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":[0.51790017,0.00044731147,0.47663632,0.00029079572,0.0007723774,0.00045645327,0.000013573159,0.001345416,0.0021376011],"genre_scores_gemma":[0.99936205,0.000049008973,0.00012624117,0.000017525768,0.0001044251,0.00004697775,0.0000314365,0.000028517929,0.00023380623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919593,0.000030816667,0.0002728569,0.00018901203,0.00006982255,0.00024157487],"domain_scores_gemma":[0.99977267,0.000032198135,0.00003037394,0.00010021014,0.000032186737,0.000032356547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013944652,0.00017248238,0.0002527431,0.00013763257,0.0002550818,0.000051979052,0.000068149224,0.00022914654,0.0000026675125],"category_scores_gemma":[0.0000028605668,0.00016895248,0.000030521504,0.00017067039,0.00004247379,0.00014829414,0.000018294953,0.00040073646,0.000005487449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009127645,0.0001084767,0.02157431,0.0013686169,0.00058169244,0.000029761266,0.0038561213,0.8572695,0.013192443,0.038309436,0.000042033862,0.06357633],"study_design_scores_gemma":[0.00046389483,0.00007958748,0.006448523,0.00013835667,0.000058233865,0.000018500748,0.00055526773,0.99047035,0.0005497642,0.000044333967,0.0009807916,0.00019237988],"about_ca_topic_score_codex":0.00009473584,"about_ca_topic_score_gemma":0.00020581696,"teacher_disagreement_score":0.4814619,"about_ca_system_score_codex":0.00021635866,"about_ca_system_score_gemma":0.000019199973,"threshold_uncertainty_score":0.68896824},"labels":[],"label_agreement":null},{"id":"W4416702585","doi":"10.21203/rs.3.rs-8066940/v1","title":"ACRerNet:Implement adaptive convolution in anchor-based lane detection","year":2025,"lang":"","type":"preprint","venue":"Research Square","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; British Columbia Innovation Council","keywords":"Robustness (evolution); Convolution (computer science); Kernel (algebra); Convolutional neural network; Rotation (mathematics); Representation (politics); Feature (linguistics)","score_opus":0.053250079725740786,"score_gpt":0.36617942657830294,"score_spread":0.31292934685256213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416702585","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":[0.5564173,0.012629094,0.3724703,0.0036552828,0.0027689864,0.018348593,0.002225532,0.0031698768,0.028315086],"genre_scores_gemma":[0.9960183,0.0011285562,0.0007218977,0.000016693744,0.00016152176,0.001165215,0.00019265994,0.000058447324,0.0005367361],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9938288,0.0010950248,0.0010325939,0.0012798385,0.0009805679,0.0017832014],"domain_scores_gemma":[0.99710155,0.00072813884,0.00013621054,0.0012802873,0.00055276655,0.00020107298],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0039060574,0.00062308833,0.00079930987,0.0021920565,0.0004430367,0.00007543549,0.00085732865,0.0019083266,0.0005913477],"category_scores_gemma":[0.00034759735,0.0007532519,0.0002509568,0.0017475041,0.00032135763,0.00012898522,0.0009897205,0.0067223883,0.00027672082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027393238,0.0012076126,0.021284185,0.008295156,0.00081711117,0.00039167044,0.0021743784,0.40638757,0.0045781434,0.02577946,0.0008188169,0.5255266],"study_design_scores_gemma":[0.0032927603,0.0012249093,0.0906973,0.0035283803,0.000055631255,0.0000053754015,0.0019709398,0.84986734,0.024802297,0.007695617,0.015650226,0.001209215],"about_ca_topic_score_codex":0.0025089544,"about_ca_topic_score_gemma":0.0052917073,"teacher_disagreement_score":0.5243174,"about_ca_system_score_codex":0.0047091125,"about_ca_system_score_gemma":0.0010829373,"threshold_uncertainty_score":0.9994919},"labels":[],"label_agreement":null},{"id":"W4416748915","doi":"10.1109/iros60139.2025.11247647","title":"Risk-Aware Autonomous Driving with Linear Temporal Logic Specifications","year":2025,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"CODE","keywords":"Reachability; Linear temporal logic; Temporal logic; Metric (unit); Collision avoidance; Extension (predicate logic); Collision; Control (management)","score_opus":0.011471335335424528,"score_gpt":0.2152134594113878,"score_spread":0.2037421240759633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416748915","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":[0.17398855,0.0001930274,0.7040929,0.0011562321,0.00019492592,0.00030088366,0.000011906377,0.0050436603,0.115017876],"genre_scores_gemma":[0.9850957,0.0000446458,0.011996681,0.00005417002,0.000024066223,0.000023404518,0.000010317925,0.00001669867,0.0027343363],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993695,0.000011626216,0.00018265085,0.00018043788,0.0000461211,0.00020968184],"domain_scores_gemma":[0.9995136,0.000043455613,0.0000263029,0.000356502,0.000027530012,0.00003257837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008171102,0.00013582954,0.00014668323,0.00012240058,0.00015094788,0.000012833905,0.00019121404,0.0001455596,0.00016292237],"category_scores_gemma":[0.000011048265,0.00011358605,0.000034464723,0.00030001524,0.00007562395,0.000069999725,0.000033965065,0.00031695457,0.00013885162],"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":[0.00003572516,0.00026042687,0.49521104,0.0001319232,0.00068373507,0.00006563971,0.0005182756,0.1632457,0.00083653047,0.25232968,0.011075472,0.07560587],"study_design_scores_gemma":[0.0012321194,0.0001439654,0.24800701,0.00009152515,0.00014058406,0.00002956623,0.000800651,0.616156,0.009958712,0.008632418,0.1137498,0.001057638],"about_ca_topic_score_codex":0.00002271584,"about_ca_topic_score_gemma":0.00017762442,"teacher_disagreement_score":0.81110716,"about_ca_system_score_codex":0.000075578675,"about_ca_system_score_gemma":0.00004037438,"threshold_uncertainty_score":0.46319047},"labels":[],"label_agreement":null},{"id":"W4416749103","doi":"10.1109/iros60139.2025.11247412","title":"Non-differentiable Reward Optimization for Diffusion-based Autonomous Motion Planning","year":2025,"lang":"","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Kootenay Association for Science & Technology","funders":"Korea Health Industry Development Institute; National Research Foundation; Ministry of Health","keywords":"Reinforcement learning; Motion planning; Motion (physics); Measure (data warehouse); Differentiable function; Thresholding","score_opus":0.007735008664649107,"score_gpt":0.231361508955214,"score_spread":0.2236265002905649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416749103","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":[0.033467907,0.00038764792,0.951649,0.0009442347,0.0010699406,0.0012285595,0.000027066357,0.0012793505,0.0099462615],"genre_scores_gemma":[0.94985247,0.000040172225,0.044842478,0.00023171991,0.00007150633,0.00020880175,0.00016056625,0.00006908885,0.0045231855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977152,0.00002657465,0.0007786893,0.0006288953,0.00012583224,0.0007248163],"domain_scores_gemma":[0.9988298,0.0002080207,0.00013312763,0.00060630555,0.00013122684,0.00009156554],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029531695,0.00048434414,0.0005607914,0.00055942306,0.0005920071,0.00009069785,0.00037877352,0.0009402932,0.00067454367],"category_scores_gemma":[0.00007017048,0.0005387812,0.00023515394,0.0005494918,0.00009981392,0.00018809036,0.00010432708,0.000461713,0.00002786006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103670136,0.00016636346,0.0026585183,0.00051431125,0.00015275207,0.0000024253375,0.00010037904,0.97373164,0.0006878639,0.0039268164,0.0015186403,0.01643661],"study_design_scores_gemma":[0.002119431,0.00011215396,0.0022486846,0.00031310713,0.00018967496,8.913181e-7,0.00010725517,0.9811249,0.010391363,0.0009464728,0.0019734418,0.00047262674],"about_ca_topic_score_codex":0.000029313009,"about_ca_topic_score_gemma":0.0000056070776,"teacher_disagreement_score":0.9163846,"about_ca_system_score_codex":0.00042274027,"about_ca_system_score_gemma":0.00020376767,"threshold_uncertainty_score":0.9997064},"labels":[],"label_agreement":null},{"id":"W4416749558","doi":"10.1109/iros60139.2025.11247054","title":"IGDrivSim: A Benchmark for the Imitation Gap in Autonomous Driving","year":2025,"lang":"","type":"article","venue":"","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":"Vector Institute","funders":"","keywords":"Imitation; Benchmark (surveying); Reinforcement learning; Perception; Autonomous agent; Key (lock)","score_opus":0.010941456652818062,"score_gpt":0.24826583662307797,"score_spread":0.2373243799702599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416749558","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":[0.10532632,0.005676153,0.77504796,0.017487943,0.0027440996,0.0033462883,0.00001828092,0.0011511052,0.08920184],"genre_scores_gemma":[0.9921224,0.00037803274,0.0036532762,0.00029808847,0.000053578657,0.00033044998,0.000007981593,0.000031587715,0.0031245719],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980981,0.000042388787,0.00071410293,0.00041832254,0.00008653189,0.00064059807],"domain_scores_gemma":[0.9979155,0.001341705,0.00007446203,0.00057294755,0.00005798239,0.000037408096],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00073911034,0.000322321,0.0003649193,0.00035341282,0.00035513175,0.00006303836,0.0005221179,0.00052531314,0.00021076438],"category_scores_gemma":[0.00024514768,0.0002804916,0.00016625709,0.00076145143,0.00015390024,0.00018509179,0.00013859927,0.00061799044,0.000033316996],"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":[0.00006252201,0.00014200572,0.012139327,0.00030355304,0.00039792524,0.000007012859,0.001364248,0.09855893,0.00081138697,0.49345797,0.0034214938,0.38933364],"study_design_scores_gemma":[0.0011291305,0.000062739,0.055226184,0.0001600791,0.000117007665,0.000002715829,0.0009714259,0.896393,0.0015392933,0.022582391,0.021441923,0.00037407817],"about_ca_topic_score_codex":0.000079454745,"about_ca_topic_score_gemma":0.0010140858,"teacher_disagreement_score":0.8867961,"about_ca_system_score_codex":0.0003921758,"about_ca_system_score_gemma":0.00019631474,"threshold_uncertainty_score":0.9999647},"labels":[],"label_agreement":null},{"id":"W4416749619","doi":"10.1109/iros60139.2025.11247503","title":"TrajFlow: Multi-modal Motion Prediction via Flow Matching","year":2025,"lang":"","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of British Columbia","funders":"","keywords":"Scalability; Trajectory; Key (lock); Generalization; Motion (physics); Inference; Overhead (engineering); Matching (statistics); Construct (python library)","score_opus":0.0060603508496145314,"score_gpt":0.2128181915301372,"score_spread":0.20675784068052266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416749619","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":[0.06418412,0.0005539681,0.919392,0.00071716896,0.0022749247,0.00044219694,0.000031039348,0.0022848852,0.010119713],"genre_scores_gemma":[0.97343045,0.0001879937,0.022650154,0.0001041712,0.000104314604,0.00003576928,0.000029656256,0.000042945838,0.0034145196],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980204,0.00005481316,0.00069716,0.00051701814,0.00014041379,0.00057019043],"domain_scores_gemma":[0.99924695,0.0000549261,0.000055167613,0.0005103169,0.00005215651,0.000080471764],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035029242,0.00039853677,0.0003830454,0.0003637685,0.0003838287,0.000046408295,0.00029172588,0.0009611754,0.0005466778],"category_scores_gemma":[0.000022447317,0.00045168464,0.00018453332,0.00049654057,0.00010309976,0.0003701407,0.00011090243,0.0009628685,0.00027321093],"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":[0.000053920347,0.0003146466,0.0023036785,0.000340873,0.00044959874,0.000014956433,0.0008288035,0.38060406,0.011565892,0.010004417,0.00041913972,0.5931],"study_design_scores_gemma":[0.0011112712,0.000042508826,0.023576941,0.00010665751,0.00011645089,0.000012172246,0.00018617652,0.96228826,0.0076509626,0.0037546316,0.0008486567,0.00030528792],"about_ca_topic_score_codex":0.000092414724,"about_ca_topic_score_gemma":0.000087742716,"teacher_disagreement_score":0.9092464,"about_ca_system_score_codex":0.00037039263,"about_ca_system_score_gemma":0.000054934702,"threshold_uncertainty_score":0.99979347},"labels":[],"label_agreement":null},{"id":"W4416749638","doi":"10.1109/iros60139.2025.11247498","title":"Delving into Mapping Uncertainty for Mapless Trajectory Prediction","year":2025,"lang":"","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Trajectory; Interpretability; Reliability (semiconductor); Kinematics; Uncertainty quantification","score_opus":0.009375388249662692,"score_gpt":0.22610862439358986,"score_spread":0.21673323614392717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416749638","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":[0.063236676,0.0018068332,0.91200083,0.0010933625,0.0021190294,0.0009802014,0.000039624516,0.001739753,0.016983677],"genre_scores_gemma":[0.985275,0.00026082405,0.010709025,0.00017930783,0.00014161854,0.00019744021,0.000028252558,0.00004293023,0.0031656509],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797904,0.000036420915,0.0007285212,0.0005312043,0.00010029335,0.00062453724],"domain_scores_gemma":[0.99900633,0.0002724794,0.00006992343,0.00047829954,0.00010182554,0.000071150374],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00051661994,0.00037107797,0.0004417101,0.0004400146,0.0005256769,0.000045202152,0.00036856125,0.0007846968,0.00022043003],"category_scores_gemma":[0.00009795349,0.0004254827,0.00022410855,0.0005334535,0.00014993171,0.00019419735,0.00011789167,0.00055953674,0.000023882112],"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":[0.00017047867,0.00016082454,0.004753274,0.002933611,0.0012636755,0.0000067381084,0.0026437384,0.21316515,0.011948131,0.138809,0.0069504185,0.61719495],"study_design_scores_gemma":[0.0013277659,0.00008213184,0.005353399,0.0004060405,0.00015630438,0.000003719796,0.002339801,0.9195943,0.007943668,0.012342838,0.049969364,0.00048065776],"about_ca_topic_score_codex":0.00010274655,"about_ca_topic_score_gemma":0.00016536925,"teacher_disagreement_score":0.92203826,"about_ca_system_score_codex":0.00067422347,"about_ca_system_score_gemma":0.00018450357,"threshold_uncertainty_score":0.9998197},"labels":[],"label_agreement":null},{"id":"W4416749873","doi":"10.1109/iros60139.2025.11245837","title":"RALAD: Bridging the Real-to-Sim Domain Gap in Autonomous Driving with Retrieval-Augmented Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"National Natural Science Foundation of China","keywords":"Bridging (networking); Feature (linguistics); Domain (mathematical analysis); Bridge (graph theory); Key (lock); Generalization; Feature extraction; Limiting; Advanced driver assistance systems","score_opus":0.005302823044906852,"score_gpt":0.2210648261789303,"score_spread":0.21576200313402347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416749873","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":[0.78497756,0.00050805113,0.10795251,0.007784899,0.0004280612,0.0013774765,0.0000028008687,0.0018134803,0.09515515],"genre_scores_gemma":[0.984807,0.00037867928,0.002472914,0.00036867266,0.000072461524,0.00005268853,0.0000066451894,0.000104887215,0.011736011],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958647,0.0002908823,0.0011043324,0.000954137,0.00031576786,0.0014702097],"domain_scores_gemma":[0.9980637,0.000484355,0.00016931247,0.0010451473,0.000079983234,0.00015753073],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0017314588,0.0007198089,0.0008260682,0.0008583801,0.000768379,0.00016169375,0.000893626,0.00060460856,0.00036366077],"category_scores_gemma":[0.00019701803,0.0005911304,0.00014900889,0.0029809675,0.00029668392,0.00026021813,0.0005327686,0.0025515682,0.00014778522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008657022,0.0002850218,0.18407121,0.0004341025,0.0011158282,0.00042447363,0.0064939586,0.6435635,0.013959998,0.07237082,0.0013426192,0.07507278],"study_design_scores_gemma":[0.0065159476,0.0008031805,0.25495666,0.003374163,0.0003423876,0.00008327476,0.009617111,0.67425966,0.0155192595,0.0030836312,0.028597556,0.002847155],"about_ca_topic_score_codex":0.00045668392,"about_ca_topic_score_gemma":0.0009492246,"teacher_disagreement_score":0.19982947,"about_ca_system_score_codex":0.0010490458,"about_ca_system_score_gemma":0.00033731415,"threshold_uncertainty_score":0.9997496},"labels":[],"label_agreement":null},{"id":"W4416750974","doi":"10.1109/iros60139.2025.11246348","title":"AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework","year":2025,"lang":"","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","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 Toronto","funders":"","keywords":"Key (lock); Control (management); Domain (mathematical analysis); Quality (philosophy); Scenario testing; Scratch; Scale (ratio)","score_opus":0.024812267484670348,"score_gpt":0.2696134597636205,"score_spread":0.24480119227895014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416750974","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":[0.6672263,0.0009962525,0.3270344,0.0005220424,0.0004671326,0.00060953054,0.000009812553,0.00048028768,0.0026541762],"genre_scores_gemma":[0.9893447,0.00065455894,0.00884151,0.0001517183,0.00008135197,0.000048343634,0.000029940153,0.000058225538,0.0007896277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766237,0.00010727723,0.0007592548,0.0006245009,0.00025155648,0.0005950587],"domain_scores_gemma":[0.99881184,0.000093823044,0.00016178713,0.0007060333,0.00011547937,0.00011100837],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031084893,0.00048907544,0.0005991453,0.00043446445,0.00034701536,0.000046859554,0.00039538357,0.00068238645,0.0008700569],"category_scores_gemma":[0.00003122342,0.00048716803,0.00012268512,0.00079101784,0.00026518758,0.00036078668,0.00008798507,0.00081442954,0.00003670285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001506461,0.0010667093,0.00042394546,0.0005199806,0.0014255503,0.000041415624,0.007483892,0.8490171,0.002319503,0.062378574,0.00016758512,0.07500513],"study_design_scores_gemma":[0.0010862078,0.0007110404,0.0018430297,0.000490353,0.00034707424,0.000006527293,0.0040072515,0.9711646,0.01889435,0.00069719483,0.00020438865,0.00054797943],"about_ca_topic_score_codex":0.000026337571,"about_ca_topic_score_gemma":0.000119662414,"teacher_disagreement_score":0.32211837,"about_ca_system_score_codex":0.0002772828,"about_ca_system_score_gemma":0.00015187683,"threshold_uncertainty_score":0.999758},"labels":[],"label_agreement":null},{"id":"W4416766854","doi":"10.48550/arxiv.2511.19893","title":"Frailty-Aware Transformer for Recurrent Survival Modeling of Driver Retention in Ride-Hailing Platforms","year":2025,"lang":"","type":"preprint","venue":"ArXiv.org","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transformer; Idle; Process (computing); Data modeling; Brier score; Survival analysis; Deep learning","score_opus":0.06978546407509906,"score_gpt":0.2847032363290826,"score_spread":0.21491777225398356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416766854","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":[0.70197403,0.00080211007,0.2927356,0.000115878705,0.0018447497,0.001355147,0.00024048182,0.00026927955,0.000662737],"genre_scores_gemma":[0.9955517,0.0021348188,0.0013018891,0.000014756386,0.00011655896,0.00027642047,0.00029134515,0.00008773743,0.00022474953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99529475,0.00004200293,0.0022309518,0.0011474348,0.00030094103,0.0009839422],"domain_scores_gemma":[0.99813044,0.00022292808,0.0003441931,0.0009325651,0.00026110312,0.00010876275],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0011575604,0.00085089105,0.0014799712,0.00071375514,0.00021977226,0.000018332026,0.00081952673,0.0020531067,0.000081484606],"category_scores_gemma":[0.0001168972,0.00096134836,0.0007820882,0.00049670896,0.00015368487,0.00035011378,0.00025451995,0.0025016915,0.000020068057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007325356,0.00026085152,0.06444488,0.0042814785,0.00067900575,0.000008746801,0.0023174908,0.878506,0.0022047465,0.003009676,0.0000092963,0.043545306],"study_design_scores_gemma":[0.0021055527,0.0001720074,0.008225523,0.0025661935,0.0002822076,0.0000019694262,0.00081772706,0.96874744,0.008285373,0.0075992374,0.00021059017,0.0009861868],"about_ca_topic_score_codex":0.00022148444,"about_ca_topic_score_gemma":0.0006249066,"teacher_disagreement_score":0.2935777,"about_ca_system_score_codex":0.00055067556,"about_ca_system_score_gemma":0.00030444007,"threshold_uncertainty_score":0.99979955},"labels":[],"label_agreement":null},{"id":"W4417169366","doi":"10.1109/dsd67783.2025.00056","title":"ShapeFuture - Technical Progress After Year 1","year":2025,"lang":"en","type":"article","venue":"","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":"Resverlogix (Canada)","funders":"","keywords":"Automation; Technical progress; Process (computing); Sovereignty; Work in process","score_opus":0.0017234105724900479,"score_gpt":0.19491533734554056,"score_spread":0.19319192677305053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417169366","genre_codex":"other","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":[0.3328624,0.0034360706,0.027456863,0.0051854667,0.0007984367,0.00059957174,0.0000111338295,0.013818799,0.61583126],"genre_scores_gemma":[0.9955096,0.000013255882,0.0028101243,0.00012446068,0.000017752274,0.000046311787,0.0000011033097,0.00000786361,0.001469523],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99966806,0.000002723559,0.00008217991,0.000088250876,0.00002928188,0.00012951509],"domain_scores_gemma":[0.9998026,0.0000068546683,0.0000030231488,0.00016554608,0.0000071899362,0.0000148063245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000033384935,0.00006687741,0.000074574295,0.000051058578,0.0000194304,0.0000064055835,0.00010863202,0.00018953257,0.00030557092],"category_scores_gemma":[0.0000027864508,0.00005773452,0.000027935786,0.00013030756,0.00005615532,0.000029432329,0.00003845802,0.00018762688,0.00012535776],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011338302,0.00018001348,0.11517558,0.00029969928,0.0002790982,0.0001183601,0.00011455154,0.00044314738,0.002211114,0.4868544,0.10766758,0.28654304],"study_design_scores_gemma":[0.0009443382,0.000060888193,0.39919308,0.00010025236,0.000060597307,0.000021182665,0.00010449132,0.011694503,0.012275016,0.013914593,0.56089133,0.0007397383],"about_ca_topic_score_codex":2.9460955e-7,"about_ca_topic_score_gemma":0.0000062154795,"teacher_disagreement_score":0.6626472,"about_ca_system_score_codex":0.000023216007,"about_ca_system_score_gemma":0.0000075526705,"threshold_uncertainty_score":0.33457872},"labels":[],"label_agreement":null},{"id":"W4417347357","doi":"10.48550/arxiv.2506.09042","title":"Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pipeline (software); Suite; Scalability; Domain (mathematical analysis); Downstream (manufacturing); Generalization; Software; Object (grammar); Scale (ratio)","score_opus":0.05412283681051307,"score_gpt":0.252163083235779,"score_spread":0.19804024642526596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417347357","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":[0.66265535,0.00042654862,0.32449523,0.00038114912,0.00075550645,0.0006016375,0.0000734713,0.0018247113,0.008786385],"genre_scores_gemma":[0.9915914,0.00027536807,0.0053290813,0.000058344576,0.00015784924,0.000099257406,0.0010339876,0.000059368926,0.0013953212],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823004,0.000045656827,0.0004157087,0.0007814062,0.00016467876,0.00036251987],"domain_scores_gemma":[0.99765694,0.00006301926,0.00011558238,0.0020310732,0.000076822966,0.00005656221],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002846189,0.00038473847,0.00041037484,0.00026392605,0.00019724341,0.00006907984,0.0008995383,0.00042329158,0.00006619433],"category_scores_gemma":[0.000030707753,0.00039479331,0.000049766288,0.00028736773,0.000104855804,0.00040908004,0.00088443223,0.0009514467,0.00009823325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012318104,0.000062096595,0.041621536,0.00027662655,0.00036882685,0.000015587686,0.00016163314,0.9352197,0.0008441754,0.0027997673,0.0011756774,0.01744204],"study_design_scores_gemma":[0.00021475606,0.0000136491835,0.0035270373,0.00039425254,0.00015556841,0.000006278654,0.000018739645,0.9892853,0.002560012,0.0013092805,0.0020416044,0.00047353448],"about_ca_topic_score_codex":0.0000687331,"about_ca_topic_score_gemma":0.00076145993,"teacher_disagreement_score":0.32893604,"about_ca_system_score_codex":0.00026088022,"about_ca_system_score_gemma":0.00015769532,"threshold_uncertainty_score":0.9998504},"labels":[],"label_agreement":null},{"id":"W4417459645","doi":"10.1002/rnc.70330","title":"Stochastic Hierarchical Game (SHG) for Multi‐Agent Autonomous Driving With Uncertain Driver Intentions","year":2025,"lang":"en","type":"article","venue":"International Journal of Robust and Nonlinear Control","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":"Greenfield Research (Canada)","funders":"Ford Motor Company","keywords":"Scalability; Probabilistic logic; Collocation (remote sensing); Game theory; Stochastic process; Inference","score_opus":0.011899745908341896,"score_gpt":0.25420071070522504,"score_spread":0.24230096479688315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417459645","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":[0.108672574,0.0002839556,0.888783,0.0014607656,0.00049445877,0.00014991719,0.00002545673,0.00005868935,0.00007118372],"genre_scores_gemma":[0.9760819,0.000034866192,0.023440937,0.00011809475,0.00015063887,0.00001120091,0.0000044023172,0.000013181541,0.00014476023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999259,0.000014970398,0.00033594048,0.00010118377,0.00013894787,0.00014997824],"domain_scores_gemma":[0.9993445,0.00015473152,0.000087034794,0.00006765343,0.0002915145,0.000054554064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018286951,0.00012050794,0.00021633646,0.000244217,0.000053336695,0.000034823996,0.00022123649,0.0000906115,0.000011046517],"category_scores_gemma":[0.00009364873,0.00009900277,0.0000949364,0.000059374885,0.00009334005,0.000107611784,0.000025322885,0.00030930695,0.0000012271734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010118125,0.0003466801,0.007748136,0.00005442791,0.0029515708,0.0001405013,0.00035083745,0.9038414,0.003576172,0.008752384,0.00045832476,0.070767745],"study_design_scores_gemma":[0.004095778,0.00012906955,0.0053471006,0.0001586913,0.00010519215,0.000115475275,0.00007023636,0.9868126,0.00012409521,0.0003358354,0.0025817798,0.00012413347],"about_ca_topic_score_codex":0.0000022081672,"about_ca_topic_score_gemma":0.00003231933,"teacher_disagreement_score":0.86740935,"about_ca_system_score_codex":0.000098283104,"about_ca_system_score_gemma":0.00009141995,"threshold_uncertainty_score":0.40372157},"labels":[],"label_agreement":null},{"id":"W587597604","doi":"","title":"IMSA TRAFFIC SIGNALS : ANOTHER METHOD OF DILEMMA ZONE PROTECTION","year":2001,"lang":"en","type":"article","venue":"IMSA journal","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Dilemma; Toolbox; Warning signs; Computer security; Computer science; SIGNAL (programming language); Traffic signal; Transport engineering; Simulation; Telecommunications; Engineering; Real-time computing; Mathematics","score_opus":0.01546413213642321,"score_gpt":0.23979686890362736,"score_spread":0.22433273676720417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W587597604","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":[0.56351507,0.00028661903,0.43465585,0.00025747105,0.00015583744,0.0000987048,0.0000019423587,0.00021100654,0.00081751955],"genre_scores_gemma":[0.99264747,0.00011204603,0.006880654,0.000025342319,0.00012813834,0.000007305952,3.8255246e-7,0.000027162767,0.00017149928],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992019,0.000052850886,0.00031611652,0.00008860579,0.00011277602,0.00022774101],"domain_scores_gemma":[0.9996605,0.000028232476,0.000089318455,0.00013097277,0.0000473015,0.00004368575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043692687,0.00011728564,0.00019926032,0.00014971892,0.00008479792,0.000013710527,0.00014370756,0.00017117386,0.0006117181],"category_scores_gemma":[0.000017614055,0.0001062453,0.00009140503,0.00019875292,0.0000316202,0.00013348155,0.000011376342,0.00048099365,0.000031254694],"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":[0.000067059904,0.00007240448,0.00025776884,0.000025170004,0.00016822988,0.00006717695,0.00032959442,0.17648037,0.1423068,0.00020601957,0.00056021335,0.6794592],"study_design_scores_gemma":[0.004924364,0.001097181,0.02105347,0.00030555183,0.0002388168,0.014741481,0.0005062668,0.39590415,0.3620276,0.00532877,0.19235545,0.0015169105],"about_ca_topic_score_codex":0.0000045437623,"about_ca_topic_score_gemma":0.000008722623,"teacher_disagreement_score":0.6779423,"about_ca_system_score_codex":0.000055722245,"about_ca_system_score_gemma":0.000020905041,"threshold_uncertainty_score":0.6697884},"labels":[],"label_agreement":null},{"id":"W632532863","doi":"10.32920/ryerson.14660670.v1","title":"Development Of In-Vehicle Collision Warning System For Intersections","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Federal Highway Administration; U.S. Department of Transportation","keywords":"Intersection (aeronautics); Collision; Acceleration; Warning system; Computer science; Simulation; Lane departure warning system; Reliability (semiconductor); Real-time computing; Engineering; Computer security; Artificial intelligence; Transport engineering; Telecommunications","score_opus":0.014679561408735253,"score_gpt":0.2271598311676616,"score_spread":0.21248026975892637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W632532863","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":[0.7947199,0.00012556755,0.20230265,0.000011474112,0.0004353744,0.00025427033,0.000004655413,0.00044338254,0.0017027499],"genre_scores_gemma":[0.9566863,0.000011677179,0.043038417,0.0000018130393,0.000011268023,0.0001440397,0.000027411128,0.000020542077,0.00005850581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919105,0.000009192365,0.00042495265,0.00018055626,0.00005084939,0.00014341409],"domain_scores_gemma":[0.99963814,0.000047040037,0.00005140436,0.00019959066,0.000045323355,0.000018530636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017826523,0.00012853956,0.00028530898,0.00015443095,0.000041174302,0.000008148474,0.00013975924,0.0003638582,0.000011222418],"category_scores_gemma":[0.000015459564,0.00013895222,0.00006726728,0.00010096965,0.0000145195345,0.000025663838,0.00020254785,0.00033747175,0.0000026312928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004781558,0.00017131817,0.0035596546,0.008515889,0.00061420654,0.000019879053,0.013683539,0.8619482,0.035170496,0.0043517128,0.00018998534,0.07172733],"study_design_scores_gemma":[0.00067502115,0.000021122558,0.005169109,0.0018991131,0.000035058583,0.000008427262,0.012187558,0.6154675,0.36179224,0.0000790264,0.002115581,0.0005502489],"about_ca_topic_score_codex":0.00001678728,"about_ca_topic_score_gemma":0.00033097842,"teacher_disagreement_score":0.32662177,"about_ca_system_score_codex":0.00031300867,"about_ca_system_score_gemma":0.00009403379,"threshold_uncertainty_score":0.5666307},"labels":[],"label_agreement":null},{"id":"W6888022694","doi":"10.18280/ijsdp.200629","title":"Developing Pro-Environmental Attitudes Through Integrated Environmental Education: Insights from a High School in Indonesia","year":2025,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Developing country; Work (physics); Sustainability; Environmental education; Government (linguistics)","score_opus":0.006392809215905371,"score_gpt":0.22560162169373113,"score_spread":0.21920881247782575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6888022694","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":[0.99153966,0.002740849,0.004653556,0.00019699005,0.0003831416,0.000106322696,0.0000021986377,0.00003125594,0.0003460557],"genre_scores_gemma":[0.9913065,0.00020028843,0.008065525,0.00013630047,0.00005470764,0.000013053535,0.000046017904,0.000009711123,0.00016791174],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99908173,0.000018591692,0.0004494575,0.00012466928,0.0001626402,0.00016291626],"domain_scores_gemma":[0.9997392,0.000047402627,0.000095718395,0.000049940172,0.000033665172,0.00003403715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000101115664,0.00014908247,0.00018026865,0.00039752133,0.000080991,0.00005243293,0.00023765904,0.00011683873,0.000028785216],"category_scores_gemma":[0.000018893406,0.00014490777,0.000020870117,0.00011791771,0.000043844095,0.0004542301,0.00008896061,0.00034337727,0.000003702257],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002130108,0.00020051864,0.9425456,0.00006656676,0.0007288076,0.0012713684,0.0072639836,0.0032376922,0.0011681423,0.0102983285,0.00032655147,0.032679457],"study_design_scores_gemma":[0.0009634763,0.000015407635,0.94987077,0.000491127,0.000013252701,0.0000419569,0.025857704,0.00024334571,0.004594689,0.0043342444,0.01332471,0.00024933807],"about_ca_topic_score_codex":0.000017636658,"about_ca_topic_score_gemma":0.000003018893,"teacher_disagreement_score":0.03243012,"about_ca_system_score_codex":0.0009363044,"about_ca_system_score_gemma":0.00038422865,"threshold_uncertainty_score":0.5909167},"labels":[],"label_agreement":null},{"id":"W6891736215","doi":"10.4224/40002963","title":"Utilisation de la mousse à air comprimé dans des systèmes d’extinction d’incendie à canalisations fixes","year":2014,"lang":"fr","type":"article","venue":"National Research Council Canada (Government of Canada)","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":"National Research Council Canada","funders":"","keywords":"Base (topology); Statistical analysis; Air temperature","score_opus":0.03510788844090886,"score_gpt":0.24842417949034767,"score_spread":0.2133162910494388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6891736215","genre_codex":"other","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":[0.09582894,0.0019868305,0.009873737,0.013931046,0.00085708307,0.00049893506,0.0009757165,0.000091388836,0.8759563],"genre_scores_gemma":[0.9963324,0.00016601667,0.0006872445,0.00016269098,0.00018454307,0.00005871697,0.000005602371,0.000040109622,0.002362688],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.93296736,0.0005840143,0.00054463174,0.00034172813,0.06470401,0.0008582302],"domain_scores_gemma":[0.98266447,0.0023750332,0.00017027823,0.00033527592,0.014102947,0.0003520274],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.02307126,0.00026271353,0.00030037772,0.00008524055,0.00080037466,0.000034198933,0.00044541463,0.00030490206,0.0002746486],"category_scores_gemma":[0.027046561,0.00032301637,0.00005495365,0.0004762953,0.00070974976,0.00020348215,0.00008639055,0.00081992755,6.686095e-7],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.0000996472,0.00013366883,0.0025280651,0.00068878324,0.00031598858,0.00005083279,0.0006102535,0.16023344,0.0040464904,0.3939426,0.42422965,0.013120581],"study_design_scores_gemma":[0.0005384947,0.00007920762,0.05508297,0.00015995225,0.000041088937,0.00003593992,0.0023217092,0.15499654,0.0077922423,0.012521967,0.766076,0.00035386876],"about_ca_topic_score_codex":0.95261365,"about_ca_topic_score_gemma":0.998808,"teacher_disagreement_score":0.90050346,"about_ca_system_score_codex":0.31382582,"about_ca_system_score_gemma":0.19748925,"threshold_uncertainty_score":0.9999222},"labels":[],"label_agreement":null},{"id":"W6891806845","doi":"10.48336/vs0r-4z04","title":"COVID-19 vaccine willingness and long COVID predictors among Chinese residents in Canada","year":2025,"lang":"en","type":"article","venue":"Memorial University Research Repository (Memorial University)","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":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Vaccination; Pandemic; Logistic regression; Public health; Coronavirus disease 2019 (COVID-19); China","score_opus":0.008622372098374333,"score_gpt":0.2256710151668014,"score_spread":0.21704864306842708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6891806845","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":[0.9850524,0.000097205935,0.0005339531,0.00027149246,0.0037979505,0.0005628339,0.000030101417,0.0003553112,0.00929875],"genre_scores_gemma":[0.99400324,0.00021657231,0.000024515739,0.000018220495,0.00037581837,6.193232e-7,0.000016036929,0.00002060473,0.005324391],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9972906,0.000520934,0.00028867373,0.00064955576,0.00050965097,0.00074055605],"domain_scores_gemma":[0.9980768,0.0005041709,0.00006654334,0.0005291409,0.00016735123,0.0006560134],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006734037,0.00030775054,0.0004813701,0.001261932,0.0010288875,0.00005325757,0.0010400615,0.0004079691,0.000037699396],"category_scores_gemma":[0.00044355952,0.00038017484,0.000077703815,0.0024770265,0.00038424222,0.00055625284,0.000651526,0.0011601737,0.000002895589],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018822455,0.000064366155,0.97281146,0.00031885022,0.00023951316,0.007919579,0.00043237556,0.008422829,0.00066506205,0.0017816628,0.005176592,0.00028546003],"study_design_scores_gemma":[0.016982652,0.0002575451,0.8853519,0.0002566631,0.00020870334,0.000057748326,0.0067772362,0.011573157,0.0021119816,0.00071217254,0.074279465,0.0014307563],"about_ca_topic_score_codex":0.5764746,"about_ca_topic_score_gemma":0.7822765,"teacher_disagreement_score":0.20580189,"about_ca_system_score_codex":0.007157321,"about_ca_system_score_gemma":0.0047353483,"threshold_uncertainty_score":0.999865},"labels":[],"label_agreement":null},{"id":"W6931248427","doi":"10.5281/zenodo.3755782","title":"RNA-seq-base causal gene prediction with alpha and beta scoring","year":2020,"lang":"en","type":"other","venue":"Figshare","topic":"Autonomous Vehicle Technology and Safety","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":"Kootenay Association for Science & Technology","funders":"","keywords":"BETA (programming language); Alpha (finance); Gene; Gene prediction; Gene interaction","score_opus":0.013982899007427257,"score_gpt":0.1834027171508323,"score_spread":0.16941981814340507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931248427","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022456341,0.010048842,0.00028664072,0.00018924857,0.00030525101,0.0010110532,0.58666795,0.011423899,0.38984257],"genre_scores_gemma":[0.16337216,0.0012063518,0.0073394408,0.0006400522,0.005836193,0.0018895454,0.5992307,0.007150629,0.21333495],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994007,0.000005617079,0.000100725345,0.00023854975,0.000077679215,0.00017678253],"domain_scores_gemma":[0.99966955,0.000011001965,0.000041403004,0.0002008001,0.0000074478467,0.000069776695],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000009725967,0.00022060356,0.00020553448,0.00008661306,0.000034163608,0.000015549407,0.00011352703,0.00037808588,0.043544512],"category_scores_gemma":[0.000014256578,0.00021426802,0.000022809094,0.000089106776,0.000010898358,0.000040481544,0.0000533366,0.00036202066,0.00047057774],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045907364,0.0000042178317,0.00005540252,0.00048526243,0.00013133128,0.00009699459,0.000030264911,0.00025492086,0.000084368585,0.0000109855855,0.9940101,0.0048315595],"study_design_scores_gemma":[0.00044891099,0.000090916496,0.0007760703,0.0024135313,0.000057354955,0.000047263708,0.000009434343,0.004623984,0.0029811682,0.000007358032,0.98805666,0.00048734102],"about_ca_topic_score_codex":0.0000107307715,"about_ca_topic_score_gemma":0.00004153502,"teacher_disagreement_score":0.1765076,"about_ca_system_score_codex":0.00003074307,"about_ca_system_score_gemma":0.000023532986,"threshold_uncertainty_score":0.9573298},"labels":[],"label_agreement":null},{"id":"W6958241808","doi":"10.60692/t33q4-yb286","title":"A Comprehensive Survey on the Application of Deep and Reinforcement Learning Approaches in Autonomous Driving","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","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":"Université de Montréal","funders":"","keywords":"Reinforcement learning; Open research; Intelligent decision support system; Taxonomy (biology); Intelligent transportation system; Deep learning","score_opus":0.03301173413419804,"score_gpt":0.1900426579110031,"score_spread":0.15703092377680505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958241808","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":[0.9409322,0.0000057630236,0.05727402,0.000016857151,0.000037761027,0.0003630817,0.000007197194,0.00018983267,0.0011732979],"genre_scores_gemma":[0.99975455,1.6531642e-7,0.000029383371,0.000015615367,0.0000031909997,0.00016334759,0.00002110662,0.0000063172183,0.0000063133443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928206,0.00007862557,0.00035210565,0.0000667481,0.000107068015,0.000113404094],"domain_scores_gemma":[0.99965185,0.000029999215,0.00011989781,0.00016574343,0.000018920038,0.000013573162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043420424,0.000088680266,0.0001367259,0.00015350625,0.00014232812,0.000013447014,0.00010847869,0.000046364534,0.0000050230187],"category_scores_gemma":[0.000006773318,0.00007445692,0.000017538103,0.00017837096,0.000025014406,0.000095703785,0.00007298514,0.00021336395,0.000013170416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014554702,8.327186e-7,0.15526184,0.00008718396,0.000018366247,2.106356e-7,0.023392588,0.8181865,0.0000021204166,0.0011402932,0.0000023068094,0.0018931895],"study_design_scores_gemma":[0.0001985857,0.000026599102,0.27372324,0.000009326778,0.0000026329642,0.000004657872,0.010257227,0.71550906,0.000097557175,0.0000012916729,0.00010028442,0.00006953143],"about_ca_topic_score_codex":0.000016901888,"about_ca_topic_score_gemma":0.0000014008541,"teacher_disagreement_score":0.11846141,"about_ca_system_score_codex":0.00012553444,"about_ca_system_score_gemma":0.000006384955,"threshold_uncertainty_score":0.3036265},"labels":[],"label_agreement":null},{"id":"W6967480280","doi":"10.5281/zenodo.1222199","title":"A \"Driver-More\" Approach To Vehicle Automation","year":2018,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","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":"L'Alliance Boviteq","funders":"European Commission","keywords":"Automation; Process (computing); Control system; Process automation system; Work (physics)","score_opus":0.01812082184502528,"score_gpt":0.2177661685096544,"score_spread":0.19964534666462913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6967480280","genre_codex":"other","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":[0.33412543,0.00006259546,0.16701677,0.0014453618,0.00022311091,0.00083414547,0.00010316479,0.013416572,0.48277286],"genre_scores_gemma":[0.9968311,0.00001112402,0.001796631,0.00013459823,0.00011314554,7.316733e-8,0.00018828758,0.0006871053,0.00023792652],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990852,0.00005706191,0.00015993929,0.00024209465,0.0001580094,0.0002977276],"domain_scores_gemma":[0.9993092,0.000005201981,0.000023488345,0.00035622602,0.00019144407,0.0001144483],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00026805516,0.00010956959,0.00010126571,0.00019227856,0.0010203191,0.0001517801,0.00060431205,0.00009225904,0.0014282186],"category_scores_gemma":[0.00009398728,0.00012258836,0.000026987884,0.00052121934,0.00014653995,0.00016437129,0.0003841713,0.00018937269,0.012486076],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006011256,0.00025601292,0.000015132344,0.00013097608,0.00013221997,0.000009128758,0.008110269,0.002671423,0.059032217,0.0423078,0.32607365,0.5612011],"study_design_scores_gemma":[0.00031405507,0.00017079854,0.0035788717,0.00001268989,0.000008458986,0.000050636245,0.00019339063,0.045032926,0.004386479,0.00025742585,0.94576746,0.00022679738],"about_ca_topic_score_codex":0.0000027164738,"about_ca_topic_score_gemma":1.202278e-7,"teacher_disagreement_score":0.66270566,"about_ca_system_score_codex":0.00012598676,"about_ca_system_score_gemma":0.000001372916,"threshold_uncertainty_score":0.9994846},"labels":[],"label_agreement":null},{"id":"W6977111005","doi":"10.6084/m9.figshare.26047018","title":"Additional file 1 of Identification of brain-enriched proteins in CSF as biomarkers of relapsing remitting multiple sclerosis","year":2024,"lang":"en","type":"article","venue":"Open MIND","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":"Lunenfeld-Tanenbaum Research Institute; University Health Network; University of Toronto","funders":"","keywords":"Multiple sclerosis; Relapsing remitting; Identification (biology); Biomarker; Disease","score_opus":0.02257270936158908,"score_gpt":0.2405506133645402,"score_spread":0.2179779040029511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977111005","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":[0.92000127,0.00024399896,0.0007107253,0.00019119198,0.00011844944,0.0009252744,0.06387303,0.000043027125,0.013893049],"genre_scores_gemma":[0.98697823,0.0000037954455,0.010786141,0.0000014074413,0.000005904231,0.00006983622,0.0018468224,0.000013442918,0.00029441374],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923456,0.000020341193,0.0004271836,0.00014397822,0.00008011046,0.000093853036],"domain_scores_gemma":[0.99911845,0.00057908864,0.000097250275,0.00016030572,0.000031663232,0.000013274056],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00026488147,0.00006812624,0.00015253104,0.00015790414,0.00001960367,0.000011594266,0.00018820871,0.00011638441,0.070637],"category_scores_gemma":[0.0006141498,0.00007617547,0.00003986312,0.00037510475,0.000073655305,0.00017227192,0.000061544306,0.00011686382,0.000056818975],"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":[0.00003966538,0.0000852807,0.0002104387,0.00022446337,0.0001556893,0.0000041267836,0.001020539,0.0013039202,0.6702319,0.00010942782,0.070903294,0.2557113],"study_design_scores_gemma":[0.0007606008,0.00009627213,0.04510494,0.0041399384,0.000038792343,0.000009457067,0.0012739679,0.136743,0.7734071,0.00093521393,0.03708055,0.00041015435],"about_ca_topic_score_codex":0.000031142652,"about_ca_topic_score_gemma":0.00003482724,"teacher_disagreement_score":0.25530112,"about_ca_system_score_codex":0.000042144442,"about_ca_system_score_gemma":0.00005692855,"threshold_uncertainty_score":0.93021256},"labels":[],"label_agreement":null},{"id":"W6979356377","doi":"","title":"SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees","year":2024,"lang":"en","type":"article","venue":"arXiv (Cornell University)","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":"Hudbay Minerals (Canada)","funders":"","keywords":"Controller (irrigation); Crash; Headway; Reinforcement learning; Training (meteorology); Control (management); Software deployment; Active safety; Vehicle safety; Vehicle dynamics","score_opus":0.011816151040478865,"score_gpt":0.1478137211795839,"score_spread":0.13599757013910505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979356377","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":[0.8619248,0.00031748242,0.133054,0.00004099451,0.00015622862,0.00014428222,0.000008906005,0.0014083702,0.0029449016],"genre_scores_gemma":[0.9991237,0.000033792046,0.00022494601,0.00002104739,0.000015434816,7.8039653e-7,0.000008466403,0.000039658647,0.0005321637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989218,0.000021716949,0.00016200416,0.00044997985,0.000054286076,0.0003901751],"domain_scores_gemma":[0.9994446,0.00009911325,0.000024307967,0.0003233644,0.000015421754,0.00009316348],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017391733,0.00024525938,0.00027076414,0.00026208264,0.00021315296,0.000050163355,0.00020689867,0.00015856672,0.00004491443],"category_scores_gemma":[0.000005820805,0.00024649003,0.00010291229,0.00067003624,0.00012332865,0.0001793034,0.000058123624,0.0003390705,0.000030197725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027112912,0.00002331696,0.014701249,0.00008602867,0.00021937523,0.00073298835,0.00013267384,0.96696025,0.00020360868,0.016051665,0.000028783503,0.00083295815],"study_design_scores_gemma":[0.0005139573,0.0000551623,0.004074237,0.00014209012,0.00016878013,0.000014695177,0.0001777584,0.99261576,0.00045088495,0.0002491085,0.0012071764,0.00033041066],"about_ca_topic_score_codex":0.000037492773,"about_ca_topic_score_gemma":0.00011298031,"teacher_disagreement_score":0.13719888,"about_ca_system_score_codex":0.0002515225,"about_ca_system_score_gemma":0.00007617218,"threshold_uncertainty_score":0.99999875},"labels":[],"label_agreement":null},{"id":"W6989067501","doi":"","title":"Aggressiveness-regulated Multi-agent Stress Testing of Autonomous Vehicles","year":2023,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","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":"Blackberry (Canada)","funders":"","keywords":"Reinforcement learning; Accident (philosophy); Quality (philosophy); Reinforcement; Task (project management); Stress test","score_opus":0.014728692811628297,"score_gpt":0.20237416692375995,"score_spread":0.18764547411213164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6989067501","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":[0.99794054,0.00030076102,0.000038645467,0.000036936257,0.00033701994,0.00021692757,0.0001260442,0.0008821382,0.00012095767],"genre_scores_gemma":[0.9276585,0.0001637397,0.0021408251,0.0000010214075,0.000016354334,0.0000010251788,0.00048303563,0.00008308345,0.069452405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989707,0.000029108527,0.00023172417,0.00029279725,0.00016467943,0.0003109922],"domain_scores_gemma":[0.99907005,0.000066336775,0.00028758435,0.0003560776,0.00015843446,0.000061494255],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000096854856,0.0002895575,0.00055151846,0.0003832153,0.00013530889,0.0000058858677,0.0004695676,0.0006420525,0.00004251533],"category_scores_gemma":[0.00002283127,0.0003654064,0.00014785811,0.0003711846,0.00013222391,0.0001012406,0.00007459618,0.00037014962,0.000051960298],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052113103,0.000683313,0.031628072,0.012038072,0.003534517,0.0011774154,0.24945451,0.26633972,0.23406684,0.00036725757,0.0012925017,0.19889665],"study_design_scores_gemma":[0.003283494,0.0003209408,0.37386096,0.0044997907,0.00091750995,0.000011612403,0.19231725,0.16259843,0.25953975,0.0002790685,0.00026574373,0.0021054586],"about_ca_topic_score_codex":0.0066387057,"about_ca_topic_score_gemma":0.0119477715,"teacher_disagreement_score":0.34223285,"about_ca_system_score_codex":0.00009991417,"about_ca_system_score_gemma":0.000068899324,"threshold_uncertainty_score":0.99997616},"labels":[],"label_agreement":null},{"id":"W6991091252","doi":"","title":"Field test of vehicle detection technologies for use at signalized intersections in Winnipeg","year":2013,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Intersection (aeronautics); Measure (data warehouse); Sensitivity (control systems); Field (mathematics); Bar (unit)","score_opus":0.009632588513032439,"score_gpt":0.18767952966291745,"score_spread":0.178046941149885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991091252","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":[0.9947884,0.00010484665,0.0037250288,0.000082626175,0.00016568099,0.0003729737,0.000025954267,0.00042731746,0.00030716174],"genre_scores_gemma":[0.9986507,0.00011101348,0.0006111631,0.000001625713,0.0000050116814,0.0000045074116,0.00003979536,0.00001937222,0.0005568078],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947494,0.000009452446,0.0001301918,0.00016663391,0.000064314394,0.00015448993],"domain_scores_gemma":[0.9993829,0.00019074041,0.00012364444,0.00021325197,0.00007595138,0.00001350959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004840254,0.0001453516,0.00028781476,0.00039729662,0.00010434586,0.000005397114,0.0002198657,0.00062425865,0.000010220352],"category_scores_gemma":[0.00006689234,0.00019960829,0.000107086904,0.000221076,0.00007960341,0.00016441938,0.000043573244,0.0002876718,0.000008033948],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002310414,0.00050518865,0.1510463,0.003613253,0.00084066414,0.000040040606,0.0028682337,0.0063648876,0.7131616,0.0005882297,0.0060114963,0.112649694],"study_design_scores_gemma":[0.0024792524,0.0010056464,0.30867139,0.00060387456,0.00028318103,0.0000063155253,0.06857491,0.067169756,0.54564416,0.00086036255,0.0037839024,0.0009172394],"about_ca_topic_score_codex":0.002235759,"about_ca_topic_score_gemma":0.54663473,"teacher_disagreement_score":0.54439896,"about_ca_system_score_codex":0.00017965252,"about_ca_system_score_gemma":0.000017113469,"threshold_uncertainty_score":0.81397897},"labels":[],"label_agreement":null},{"id":"W6991975113","doi":"","title":"Intelligent Mobility Research at Defence R&amp;D Canada for Autonomous UGV Mobility in Complex Terrain","year":2007,"lang":"en","type":"article","venue":"Defense Technical Information Center (DTIC)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Terrain; Intelligent transportation system; Intelligent decision support system; Focus (optics); Robotics; Robot; Unmanned ground vehicle; Mobile robot","score_opus":0.055760561936711,"score_gpt":0.3119831762390138,"score_spread":0.25622261430230275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991975113","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":[0.8832339,0.00005378916,0.10627007,0.00053060014,0.00021393264,0.0018902553,0.00020523451,0.0010096917,0.0065925377],"genre_scores_gemma":[0.9960716,0.000014702763,0.0031071038,0.00036797416,0.000020157038,0.00008553184,0.00027549895,0.000020323057,0.00003712617],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99716806,0.00005565474,0.0012030252,0.0002742346,0.00038004166,0.000918972],"domain_scores_gemma":[0.99832296,0.0005009234,0.000087238004,0.0007082272,0.00019473328,0.00018589577],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0027461029,0.0002551378,0.00033455362,0.00030228132,0.00021147286,0.000031376014,0.0004863392,0.00036850548,0.0001401551],"category_scores_gemma":[0.00040107753,0.00026339552,0.00010107015,0.00049179216,0.00028904944,0.00034198037,0.00023617184,0.0007787608,0.00010502211],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005299343,0.0049706073,0.121192984,0.0054619284,0.00056616124,0.00014763798,0.01694249,0.10089193,0.019034926,0.0832547,0.2690421,0.3731952],"study_design_scores_gemma":[0.0022688778,0.00026312665,0.18685293,0.00010803082,0.000019745914,0.0001320212,0.0011502148,0.07011216,0.009099697,0.0036444606,0.72508645,0.0012622861],"about_ca_topic_score_codex":0.008124316,"about_ca_topic_score_gemma":0.29722905,"teacher_disagreement_score":0.45604435,"about_ca_system_score_codex":0.0028133385,"about_ca_system_score_gemma":0.00015540543,"threshold_uncertainty_score":0.9999818},"labels":[],"label_agreement":null},{"id":"W6999931786","doi":"","title":"Dual-Camera Intersection Monitoring: Detection, Tracking, and Predictive Safety Alerts for Road Users","year":2024,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Intersection (aeronautics); Global Positioning System; Identification (biology); Deep learning; Focus (optics); Trajectory; Object detection; Key (lock); Intelligent transportation system; Feature (linguistics); Video tracking","score_opus":0.007625806013104653,"score_gpt":0.1977311477542886,"score_spread":0.19010534174118396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6999931786","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":[0.52690524,0.0026950845,0.053860236,0.00011505614,0.01693705,0.0030632913,0.00043822484,0.006082787,0.389903],"genre_scores_gemma":[0.8325114,0.0003433701,0.00015883423,0.000004274541,0.0002230087,0.000011930624,0.00013980562,0.000112214046,0.16649514],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99888575,0.000023039252,0.00022704015,0.00046690108,0.000101185695,0.00029611078],"domain_scores_gemma":[0.9995605,0.00003523522,0.00007756009,0.00017241645,0.00007604745,0.00007829436],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000651789,0.0003696475,0.00031436124,0.00032265086,0.0002045078,0.000050892006,0.0001424957,0.000704938,0.0009293091],"category_scores_gemma":[0.000009797583,0.00045415518,0.00014352716,0.0003135836,0.00005656342,0.00028122097,0.000037116562,0.0006244793,0.000018507379],"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":[0.0005936712,0.000021386179,0.00035145937,0.00066063437,0.00072045805,0.000050275496,0.0030989859,0.001681409,0.0012658678,0.00018429085,0.00027798887,0.9910936],"study_design_scores_gemma":[0.003319226,0.000920062,0.028720293,0.0016862368,0.0021434322,0.000089011744,0.05149642,0.048782896,0.049720034,0.0008141757,0.80966604,0.0026422048],"about_ca_topic_score_codex":0.000035719615,"about_ca_topic_score_gemma":0.0005346425,"teacher_disagreement_score":0.98845136,"about_ca_system_score_codex":0.0004570209,"about_ca_system_score_gemma":0.000032414428,"threshold_uncertainty_score":0.99998397},"labels":[],"label_agreement":null},{"id":"W7017009689","doi":"","title":"Activity of Little Brown Bats in Coastal Forests","year":2009,"lang":"en","type":"article","venue":"Research Exchange (Washington State University)","topic":"Autonomous Vehicle Technology and Safety","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":"Pacific Institute for the Mathematical Sciences","funders":"","keywords":"Habitat; Temperate rainforest; Forest management; Temperate climate; Natural (archaeology); Natural forest","score_opus":0.03300491564866944,"score_gpt":0.269194113415078,"score_spread":0.23618919776640854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7017009689","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":[0.9813073,0.00008431836,0.0012504687,0.00019211168,0.000040666735,0.00021207871,0.000036608675,0.00022496199,0.016651435],"genre_scores_gemma":[0.9975406,0.0005209793,0.00010885144,0.000003773529,0.000012910715,6.338644e-7,0.0000054022084,0.000013650464,0.0017932075],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987289,0.00012201938,0.0001179123,0.00021332566,0.00026694065,0.0005509253],"domain_scores_gemma":[0.99939525,0.00012100575,0.000026797275,0.00027151522,0.000085103864,0.00010030324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005642174,0.00012266573,0.0002127209,0.0010041372,0.00009039057,0.000012129751,0.0003743289,0.00015938171,0.000055009223],"category_scores_gemma":[0.000043838158,0.00014579401,0.00004911515,0.0011624583,0.0001623918,0.00035261846,0.00013753604,0.00066489715,0.000021270112],"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":[0.0011616645,0.0007385904,0.021986958,0.00067415816,0.00017814052,0.0018102609,0.008574438,0.010639318,0.07072287,0.014099584,0.004667207,0.8647468],"study_design_scores_gemma":[0.0063013374,0.0019281851,0.52657,0.00053056923,0.000024645291,0.000023038394,0.0021981949,0.062484194,0.25296962,0.01127562,0.13411368,0.0015808895],"about_ca_topic_score_codex":0.00021813068,"about_ca_topic_score_gemma":0.0028178543,"teacher_disagreement_score":0.8631659,"about_ca_system_score_codex":0.00030568804,"about_ca_system_score_gemma":0.00007133536,"threshold_uncertainty_score":0.5945307},"labels":[],"label_agreement":null},{"id":"W7024414534","doi":"","title":"Repercussions of the COVID-19 Pandemic on the Well-being and Training of Medical Clerks: A Pan-Canadian Survey","year":2020,"lang":"en","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Training (meteorology); Coronavirus disease 2019 (COVID-19); MEDLINE","score_opus":0.028613946042891213,"score_gpt":0.25666181200182187,"score_spread":0.22804786595893065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024414534","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015981115,0.0000138573,0.00061289547,0.0032036195,0.00026464302,0.0009077845,0.00010553347,0.00041786997,0.9784927],"genre_scores_gemma":[0.91762304,0.0007672193,0.00008908964,0.00086671626,0.000052062005,0.000074737014,0.000014357984,0.00022734652,0.080285445],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99711955,0.00067334593,0.0005621592,0.0008766782,0.0004131486,0.00035510585],"domain_scores_gemma":[0.9959591,0.0011592061,0.00023093297,0.0023085293,0.000019993084,0.00032224169],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004813993,0.00032367895,0.0005704079,0.00018735863,0.0001443508,0.000014306902,0.0016476425,0.001012814,0.14748406],"category_scores_gemma":[0.005831637,0.00023474947,0.00015793742,0.00028347794,0.00057656545,0.000023725674,0.0006932684,0.0014315973,0.013413091],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001393937,0.00018999152,0.22008722,0.0013899104,0.002476879,0.00009487115,0.019118981,0.0038017384,0.0011005251,0.029948426,0.64440453,0.077247515],"study_design_scores_gemma":[0.0009605529,0.0000041921644,0.063921124,0.0011588853,0.00019194098,0.00012973536,0.00093396165,0.007706231,0.000686577,0.0046113646,0.9187683,0.0009271368],"about_ca_topic_score_codex":0.014242444,"about_ca_topic_score_gemma":0.042096864,"teacher_disagreement_score":0.9016419,"about_ca_system_score_codex":0.00017315232,"about_ca_system_score_gemma":0.0006365366,"threshold_uncertainty_score":0.9923218},"labels":[],"label_agreement":null},{"id":"W7024643332","doi":"","title":"Speed comparison of solution methods for the obstacle problem","year":2017,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Autonomous Vehicle Technology and Safety","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Obstacle; MATLAB; Newton's method; Iterative method; Euler's formula","score_opus":0.03291650974885806,"score_gpt":0.3216780406884207,"score_spread":0.28876153093956264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024643332","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":[0.70138806,0.011696342,0.0011760257,0.00012814494,0.0088110445,0.008034095,0.0026256167,0.004135616,0.26200503],"genre_scores_gemma":[0.9763593,0.00022477908,0.018891519,0.000013307012,0.000037400856,0.00020861332,0.00042012462,0.00019292548,0.0036520483],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976228,0.0001422432,0.00090984657,0.0005067678,0.0002399142,0.00057842577],"domain_scores_gemma":[0.9973703,0.0005824592,0.0006286756,0.0010751582,0.00025354535,0.0000898865],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0013782482,0.00054355,0.000883997,0.00020029959,0.0014006408,0.000038369868,0.0011171722,0.001238344,0.000056353383],"category_scores_gemma":[0.00047687517,0.00049268804,0.00039036054,0.0001773853,0.00012197403,0.00032219294,0.00006689847,0.001459006,0.000038228995],"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":[0.000121040066,0.000078418285,0.000018644514,0.00064639066,0.00044219944,0.0000012628442,0.000023971294,0.0013434794,0.07940941,0.04119752,0.000019695397,0.87669796],"study_design_scores_gemma":[0.001214053,0.00022595185,0.0016503267,0.0004212286,0.00075665076,0.000010615356,0.0004139071,0.023456464,0.7444388,0.048661236,0.17752253,0.0012281893],"about_ca_topic_score_codex":0.000084282954,"about_ca_topic_score_gemma":0.0005200974,"teacher_disagreement_score":0.87546974,"about_ca_system_score_codex":0.0003332831,"about_ca_system_score_gemma":0.00003430876,"threshold_uncertainty_score":0.9998994},"labels":[],"label_agreement":null},{"id":"W7034065395","doi":"","title":"Solving the Student Housing Crisis in Ontario: An Analytical Policy Brief","year":2024,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Proteogenomics; Dysgeusia; Hyporeflexia; Diafiltration; Fusible alloy; Hemopericardium","score_opus":0.01765462076885399,"score_gpt":0.24088717135094362,"score_spread":0.22323255058208963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034065395","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":[0.99424076,0.0002558542,0.00032612187,0.0014198709,0.00014213052,0.00014782698,0.00000936598,0.00045048236,0.0030075696],"genre_scores_gemma":[0.99905044,0.000029885921,0.00028967785,0.00007618268,0.000042217125,4.386824e-7,0.0000072865823,0.000031812357,0.00047205167],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99863434,0.00008364248,0.00021098816,0.00036256755,0.0002648072,0.00044368402],"domain_scores_gemma":[0.9992539,0.000081921404,0.000032823766,0.00048441798,0.00003338413,0.00011351683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006396177,0.00020925095,0.00028321458,0.0004986551,0.00030308322,0.00006443154,0.00069763185,0.0003148145,0.00029134259],"category_scores_gemma":[0.000020769578,0.00022086012,0.00014903636,0.0007152372,0.0001981132,0.000724263,0.00025980486,0.0011887002,0.00006824507],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020877268,0.0003547173,0.88192743,0.00024463062,0.0008299108,0.0011696123,0.045901068,0.018465428,0.0052061216,0.027405983,0.0009083677,0.017377978],"study_design_scores_gemma":[0.00056295324,0.00008184224,0.97528166,0.00011825352,0.00010736514,0.000031164578,0.004781656,0.011040542,0.00040188787,0.0014114501,0.0057841996,0.00039702342],"about_ca_topic_score_codex":0.008881945,"about_ca_topic_score_gemma":0.13038251,"teacher_disagreement_score":0.12150057,"about_ca_system_score_codex":0.00088949857,"about_ca_system_score_gemma":0.00018571093,"threshold_uncertainty_score":0.997718},"labels":[],"label_agreement":null},{"id":"W7038991659","doi":"","title":"L'Animation religieuse : pourquoi?, comment? : rapport d'expertise soumis à un cégep de la banlieue de Montréal /","year":2015,"lang":"fr","type":"other","venue":"Bibliothèque et Archives nationales du Québec (Québec government)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Subject (documents); Field (mathematics); Perspective (graphical); Ethnography; Identity (music)","score_opus":0.006138523991000097,"score_gpt":0.20029015861848498,"score_spread":0.1941516346274849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7038991659","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11545296,0.048884016,0.036354724,0.060065515,0.00029367412,0.0012225413,0.000681518,0.0018955636,0.7351495],"genre_scores_gemma":[0.6919432,0.0077907355,0.017636498,0.0030901306,0.0006492234,0.00048509188,0.0001390081,0.00085303804,0.27741304],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9960214,0.00056262186,0.00096628745,0.0007378918,0.0008364705,0.00087533024],"domain_scores_gemma":[0.99320644,0.0049723405,0.0005514533,0.000745402,0.00006455813,0.000459821],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000980072,0.00092694856,0.0007687637,0.0011210666,0.0003087435,0.00023088486,0.0011067524,0.0007280418,0.006230411],"category_scores_gemma":[0.0022410862,0.0010396189,0.00036198396,0.0006985053,0.0006063718,0.0004004859,0.00047693652,0.0012663971,0.00086272653],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00023317918,0.0007769907,0.04671073,0.00031220584,0.0010654859,0.00020379141,0.009815091,0.006945801,0.00227695,0.029426504,0.8962603,0.0059729335],"study_design_scores_gemma":[0.0019495655,0.000150893,0.06186062,0.00032505064,0.00022944118,0.00024345597,0.00027421588,0.01994838,0.0013240824,0.002163795,0.9104186,0.0011118625],"about_ca_topic_score_codex":0.12896323,"about_ca_topic_score_gemma":0.67734724,"teacher_disagreement_score":0.5764903,"about_ca_system_score_codex":0.014360065,"about_ca_system_score_gemma":0.03620907,"threshold_uncertainty_score":0.99991524},"labels":[],"label_agreement":null},{"id":"W7066503454","doi":"","title":"Hairy nanocelluloses for synthesis of noble metal nanoparticles","year":2023,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Autonomous Vehicle Technology and Safety","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":"McGill University","funders":"McGill University","keywords":"Noble metal; Nanoparticle; Cellulose; Nanostructure; Monomer","score_opus":0.0140434670863582,"score_gpt":0.21864787226005147,"score_spread":0.20460440517369327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7066503454","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":[0.9826349,0.0005428259,7.428086e-7,0.000008623199,0.0012049362,0.0006199375,0.0015255039,0.0018540003,0.011608551],"genre_scores_gemma":[0.9939857,0.00030402857,0.00087142194,0.000012596832,0.000025442583,0.0004985627,0.00030771908,0.00032599675,0.0036685474],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974098,0.00006885874,0.0009199256,0.0006034856,0.0003166317,0.0006812483],"domain_scores_gemma":[0.998177,0.0006087175,0.00027515052,0.0006283094,0.00018167667,0.00012918373],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005754573,0.000608462,0.0009439887,0.00042775803,0.0004720844,0.00001820277,0.0006217558,0.0011085898,0.00008141485],"category_scores_gemma":[0.00063806295,0.0006737654,0.0004731782,0.0004993141,0.00008286242,0.0002985858,0.00006701512,0.00071757927,0.00024817232],"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":[0.00019080163,0.0001472326,0.000019856432,0.0021517114,0.0010208068,0.000030083727,0.00001447004,0.000993999,0.7209712,0.047569968,0.00002576194,0.22686405],"study_design_scores_gemma":[0.00033736412,0.00008360546,0.00034464276,0.00029370261,0.00039955782,0.0000054981024,0.00027426585,0.00044377262,0.9772446,0.012523499,0.0073557454,0.00069374795],"about_ca_topic_score_codex":0.000052737323,"about_ca_topic_score_gemma":0.0005211074,"teacher_disagreement_score":0.25627336,"about_ca_system_score_codex":0.0002368593,"about_ca_system_score_gemma":0.00003468689,"threshold_uncertainty_score":0.9995714},"labels":[],"label_agreement":null},{"id":"W7093074261","doi":"10.1109/jiot.2025.3624038","title":"Multiagent Collaborative Decision-Making Using Small Vision–Language Models for Autonomous Driving","year":2025,"lang":"","type":"article","venue":"IEEE Internet of Things Journal","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Adaptability; Inefficiency; Intersection (aeronautics); Autonomous agent; Negotiation; Multi-agent system; Perception; Visualization","score_opus":0.01189474326829279,"score_gpt":0.2896438402046393,"score_spread":0.27774909693634653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7093074261","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":[0.38883013,0.0032622116,0.6036976,0.0001133006,0.0029137337,0.00045577742,0.000016816964,0.00012217295,0.0005882689],"genre_scores_gemma":[0.8263031,0.0001955242,0.17280827,0.00014318377,0.00015906803,0.0000120965215,0.0000011221932,0.000084242034,0.00029338643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99591243,0.0001251094,0.0021354128,0.0005969558,0.00033869356,0.00089141104],"domain_scores_gemma":[0.9964228,0.0011527442,0.0010512279,0.0005249555,0.0006791548,0.00016910602],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016123969,0.0006628111,0.001122205,0.0010616474,0.00045161363,0.00030858617,0.0013176354,0.00080895895,0.00011072694],"category_scores_gemma":[0.00053218246,0.00069449836,0.00060059753,0.0005955427,0.000261118,0.00097584963,0.0003470827,0.0017555528,0.000006240914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005639914,0.00026037966,0.0001849355,0.0003551148,0.0015874783,0.0001689602,0.022044405,0.7039086,0.021280922,0.0024615806,0.000752772,0.24643084],"study_design_scores_gemma":[0.001456554,0.00027232867,0.00006163333,0.006408398,0.0003330131,0.00020454997,0.002207429,0.94613075,0.029574625,0.012465025,0.00035238956,0.0005332715],"about_ca_topic_score_codex":0.000047446767,"about_ca_topic_score_gemma":0.000033634915,"teacher_disagreement_score":0.43747297,"about_ca_system_score_codex":0.0011560809,"about_ca_system_score_gemma":0.00056718226,"threshold_uncertainty_score":0.99955064},"labels":[],"label_agreement":null},{"id":"W7099778149","doi":"","title":"BENEFITS of CHESS for ACADEMIC PERFOMANCE and CREATIVE THINKING","year":2015,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Globe; Reading (process); Computer chess; Value (mathematics); Logical reasoning","score_opus":0.02731982517535392,"score_gpt":0.24336915840441378,"score_spread":0.21604933322905986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099778149","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":[0.97890306,0.0013054082,0.009614046,0.000109878165,0.000038119866,0.0001042348,0.0000043455902,0.0002565636,0.009664346],"genre_scores_gemma":[0.9959965,0.000065892236,0.0036555629,0.000018875304,0.000015924672,0.00001138685,0.0000010742596,0.000008478029,0.00022634368],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971646,0.0000023278437,0.000090564434,0.00006692915,0.000032986827,0.00009073257],"domain_scores_gemma":[0.99984396,0.000030667212,0.000014912007,0.0000627428,0.000024112089,0.000023585408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000120688324,0.00005445453,0.00010013445,0.000026035885,0.00002098024,0.0000014525742,0.000071786315,0.00012507898,0.000004570587],"category_scores_gemma":[0.000017862527,0.000049494953,0.000010892142,0.000037533522,0.00003982908,0.00006274799,0.000021073676,0.00010545719,0.0000015954474],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015028047,0.00004417052,0.048583955,0.0004846568,0.00029002552,0.0000015404069,0.017151903,0.07195464,0.004797209,0.64697176,0.004842679,0.20472717],"study_design_scores_gemma":[0.003856346,0.00048783544,0.14960611,0.00024573645,0.000108242486,0.00004759404,0.0035099813,0.32679015,0.41208732,0.09449941,0.0077680284,0.0009932348],"about_ca_topic_score_codex":0.0000027793121,"about_ca_topic_score_gemma":0.0000040125747,"teacher_disagreement_score":0.55247235,"about_ca_system_score_codex":0.000014217313,"about_ca_system_score_gemma":0.0000067016786,"threshold_uncertainty_score":0.20183456},"labels":[],"label_agreement":null},{"id":"W7099894228","doi":"","title":"© 2004 European Society for Medical Oncology Hodgkin’s lymphoma in Chinese migrants to British Columbia: a 25-year survey","year":2016,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Incidence (geometry); Lymphoma; Referral; Cancer registry; Population; Government (linguistics)","score_opus":0.007257885165219156,"score_gpt":0.22932194231066605,"score_spread":0.22206405714544689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099894228","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":[0.97989184,0.00009335789,0.0132652,0.0008170103,0.00024487294,0.00034807378,0.00010756945,0.0006935159,0.004538545],"genre_scores_gemma":[0.9956397,0.00010250116,0.0020737294,0.00045999375,0.00006588474,0.000048584243,0.000010561825,0.00005069483,0.0015483748],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99871236,0.00008031511,0.0003435155,0.00028587127,0.00012351881,0.0004543921],"domain_scores_gemma":[0.9992565,0.00030200483,0.000021814361,0.00022141157,0.000040345207,0.00015796207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001261107,0.00012376314,0.00030728435,0.000034900182,0.00006339853,0.000023789464,0.00034621242,0.0003403908,0.0006617116],"category_scores_gemma":[0.00036626917,0.00013327914,0.00011629418,0.00022595574,0.000099726014,0.000066845874,0.000098625176,0.00012199036,0.00017375221],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003911915,0.00019465422,0.31197003,0.00003404514,0.000117738324,0.00011935114,0.00019409116,0.000036207803,0.0003347214,0.000056472178,0.1326018,0.5543018],"study_design_scores_gemma":[0.0030682709,0.00012218097,0.9723937,0.00005126604,0.000004719377,0.00005128253,0.00002772038,0.000956908,0.000021515429,0.00020006647,0.02284231,0.00026008816],"about_ca_topic_score_codex":0.004663042,"about_ca_topic_score_gemma":0.3068011,"teacher_disagreement_score":0.66042364,"about_ca_system_score_codex":0.0002279267,"about_ca_system_score_gemma":0.00009166955,"threshold_uncertainty_score":0.7245278},"labels":[],"label_agreement":null},{"id":"W7114913983","doi":"10.1145/3748636.3769291","title":"Generative Trajectory Forecasting via Transformers: The TrajLearn Framework","year":2025,"lang":"","type":"article","venue":"","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":"York University","funders":"","keywords":"Trajectory; Granularity; Scalability; Path (computing); Generative grammar; Data-driven","score_opus":0.013647799300744301,"score_gpt":0.22842415809453245,"score_spread":0.21477635879378815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7114913983","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":[0.02599206,0.0040144837,0.8774504,0.005157259,0.0011742907,0.00068639766,0.0000096698095,0.0007721239,0.084743336],"genre_scores_gemma":[0.9870999,0.00049773866,0.008397275,0.000776284,0.00017159885,0.000076236734,0.0000034700984,0.00005435139,0.0029231764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976255,0.00011859192,0.00072400214,0.0005033876,0.00016770004,0.0008607612],"domain_scores_gemma":[0.99864113,0.00064763316,0.00006576892,0.00051031116,0.000056969133,0.00007820144],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005387177,0.00052525394,0.00050235674,0.00020793686,0.00087175256,0.00007103712,0.0005899001,0.0010393163,0.001184442],"category_scores_gemma":[0.00008320691,0.00040661063,0.00033105403,0.0009481174,0.00047562722,0.00019820905,0.000060396284,0.0022838323,0.000099562065],"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":[0.00007836747,0.00010900844,0.00033142892,0.00021309993,0.0011392848,0.000021394604,0.005861193,0.028222516,0.0015312583,0.10832849,0.0009182795,0.8532457],"study_design_scores_gemma":[0.0009120039,0.00021101563,0.0014352682,0.00039288163,0.0004311886,0.000035670302,0.0058139297,0.89435214,0.029912429,0.04472827,0.020780193,0.0009950302],"about_ca_topic_score_codex":0.00003825755,"about_ca_topic_score_gemma":0.00013224166,"teacher_disagreement_score":0.9611078,"about_ca_system_score_codex":0.00022548495,"about_ca_system_score_gemma":0.00016552507,"threshold_uncertainty_score":0.9998386},"labels":[],"label_agreement":null},{"id":"W7116395234","doi":"10.18280/ijsse.150915","title":"YOLOv12-Based Driver Monitoring System: Real-Time Applications in ADAS","year":2025,"lang":"","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Advanced driver assistance systems; Poison control; Occupational safety and health","score_opus":0.003146617433694593,"score_gpt":0.21596020532099108,"score_spread":0.21281358788729648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116395234","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":[0.75828904,0.009337837,0.2181434,0.002167452,0.0056971684,0.00082628103,0.00019720502,0.00054567587,0.004795934],"genre_scores_gemma":[0.99529433,0.0024627638,0.001851041,0.000009825005,0.00029027162,0.000009923039,0.000008739045,0.00003003924,0.00004306001],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977744,0.000039539784,0.0012566058,0.0002514687,0.00033346203,0.00034451438],"domain_scores_gemma":[0.99877524,0.00035426414,0.0002551556,0.00021182935,0.00028828322,0.000115240946],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00063188147,0.00033944348,0.0005955662,0.0010416358,0.000092558024,0.000067691544,0.0006459517,0.00041113957,0.000022659806],"category_scores_gemma":[0.00007182991,0.00038717163,0.00018316749,0.00043166074,0.00007965015,0.00033973882,0.0001279398,0.0010202475,0.0000071564355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005345217,0.00032639157,0.030831376,0.0012868867,0.0016442289,0.00046506128,0.0017920693,0.87397784,0.009425874,0.06766144,0.000044777647,0.012009502],"study_design_scores_gemma":[0.005130915,0.00014384043,0.10001869,0.006783609,0.0003160305,0.00024571308,0.0012741801,0.86223084,0.009241107,0.00077453634,0.012825795,0.0010147734],"about_ca_topic_score_codex":0.000033764965,"about_ca_topic_score_gemma":0.000004829657,"teacher_disagreement_score":0.2370053,"about_ca_system_score_codex":0.00089716504,"about_ca_system_score_gemma":0.0001569084,"threshold_uncertainty_score":0.999858},"labels":[],"label_agreement":null},{"id":"W7116679597","doi":"10.1109/icpeev67897.2025.11291305","title":"Optimized Lightweight AI Algorithms for Real-Time Data Processing and Decision-Making in Edge Computing for Autonomous Vehicles","year":2025,"lang":"","type":"article","venue":"","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Scalability; Pruning; Artificial neural network; Inference; SAFER; Edge computing; Enhanced Data Rates for GSM Evolution; Reinforcement learning; Energy consumption","score_opus":0.0164547900183147,"score_gpt":0.2996310716819951,"score_spread":0.2831762816636804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116679597","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025116256,0.0039415527,0.96389055,0.0012229932,0.000470804,0.0024738072,0.00017192226,0.00095887406,0.0017532232],"genre_scores_gemma":[0.40775082,0.00030849958,0.5911428,0.00016901449,0.00012643279,0.000086804204,0.00007365546,0.00009329751,0.00024872256],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99565846,0.000042749387,0.001556364,0.0014574599,0.0001446634,0.0011403259],"domain_scores_gemma":[0.99580926,0.0025718685,0.00023216555,0.0011039501,0.00017756042,0.00010519325],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017154473,0.00066988554,0.0012308127,0.0007166641,0.0007786703,0.00027929287,0.0012176821,0.00093702006,0.000029966017],"category_scores_gemma":[0.00040621753,0.00070648565,0.00013766668,0.00072946976,0.0002321253,0.00070787646,0.001113884,0.00061747537,0.000006846404],"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":[0.00042368443,0.00014727216,0.0003025374,0.00091712613,0.00020125236,0.000012012373,0.0003982048,0.020088492,0.0004391054,0.0046287086,0.0012091442,0.9712325],"study_design_scores_gemma":[0.0039871917,0.00009222254,0.0007057669,0.0017662282,0.00018510212,0.000013280289,0.00021542182,0.97576433,0.0007894691,0.013129094,0.0026458078,0.00070609566],"about_ca_topic_score_codex":0.000029374132,"about_ca_topic_score_gemma":0.000030419118,"teacher_disagreement_score":0.97052634,"about_ca_system_score_codex":0.00037189867,"about_ca_system_score_gemma":0.00045858815,"threshold_uncertainty_score":0.9995386},"labels":[],"label_agreement":null},{"id":"W7116718105","doi":"10.1109/tase.2025.3646982","title":"Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","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":false,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Traverse; Intersection (aeronautics); Abstraction; Process (computing); Focus (optics); Scheme (mathematics)","score_opus":0.012760140901341113,"score_gpt":0.25239601721918764,"score_spread":0.23963587631784652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116718105","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":[0.014383723,0.022638457,0.9600566,0.00059906207,0.0007048157,0.0005127831,0.000004956439,0.00061620603,0.00048340257],"genre_scores_gemma":[0.9609404,0.036566194,0.0022395095,0.00008647696,0.000007773113,0.00008875636,8.398415e-7,0.000022298074,0.00004776052],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977205,0.000038716604,0.0008400943,0.00051981246,0.0003984561,0.0004824336],"domain_scores_gemma":[0.99860346,0.00052159245,0.00015958486,0.00037137937,0.00023419637,0.00010977721],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001424578,0.00034546506,0.0004884964,0.0014065137,0.0008716328,0.000054329605,0.00024012133,0.00021436838,0.00007340223],"category_scores_gemma":[0.0002302255,0.00038933932,0.00011201249,0.0013271627,0.00042120815,0.0004994078,0.000019947569,0.0005293437,0.000007311397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013742194,0.0000334291,0.0000032822438,0.0025403714,0.00006988827,0.0000017398053,0.00046622637,0.8191858,0.002839931,0.0008090191,0.000009487221,0.17402709],"study_design_scores_gemma":[0.000434432,0.00011938391,0.0006875777,0.017836966,0.00013811924,0.000022834955,0.00018264457,0.974956,0.004612698,0.00006178518,0.00064070296,0.00030685912],"about_ca_topic_score_codex":0.000005540277,"about_ca_topic_score_gemma":0.00001710347,"teacher_disagreement_score":0.9578171,"about_ca_system_score_codex":0.0006951328,"about_ca_system_score_gemma":0.00025754454,"threshold_uncertainty_score":0.9998559},"labels":[],"label_agreement":null},{"id":"W7124285423","doi":"10.65109/rxzd6423","title":"Revealed Multi-Objective Utility Aggregation in Human Driving","year":2023,"lang":"","type":"article","venue":"","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":"University of Waterloo; University of Toronto","funders":"","keywords":"Unobservable; Aggregate (composite); Satisficing; Aggregation problem; Observability; Data aggregator; Bounded rationality; Consistency (knowledge bases); Estimation","score_opus":0.019977081041510344,"score_gpt":0.26548911857355617,"score_spread":0.2455120375320458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124285423","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":[0.97899044,0.0001787103,0.008581533,0.00026913633,0.00033458162,0.00060116715,0.000007120462,0.0023977223,0.008639567],"genre_scores_gemma":[0.9966434,0.00018604791,0.0007995841,0.00001800351,0.00004208782,0.000049811737,0.000018945262,0.0000494282,0.0021926663],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977746,0.00011655061,0.00070438057,0.0005564499,0.00014226716,0.0007057249],"domain_scores_gemma":[0.99910414,0.00014878543,0.00008634265,0.00053508073,0.00004808514,0.00007756932],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009398531,0.00032434394,0.00043055209,0.0005524789,0.00027145256,0.000024935733,0.0002898743,0.0006620892,0.00047611524],"category_scores_gemma":[0.00018697105,0.00038536685,0.0001145219,0.0015095262,0.00015724727,0.00027033832,0.00019701071,0.0009050715,0.0005655707],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003822856,0.0004137314,0.707394,0.0004122881,0.0002776733,0.00023805597,0.0071724057,0.015351189,0.013310501,0.0139156245,0.0005786688,0.24089766],"study_design_scores_gemma":[0.000792005,0.00003086004,0.61786044,0.00011556116,0.000013112489,0.0000025172983,0.0006226416,0.37300393,0.0051758494,0.0019031677,0.00016901414,0.00031089605],"about_ca_topic_score_codex":0.0002399825,"about_ca_topic_score_gemma":0.0019957675,"teacher_disagreement_score":0.35765275,"about_ca_system_score_codex":0.00034935935,"about_ca_system_score_gemma":0.00004670253,"threshold_uncertainty_score":0.9998598},"labels":[],"label_agreement":null},{"id":"W7125498623","doi":"10.1109/iceamst67459.2025.11335831","title":"Navigational Efficiency Enhancement through Instruction-based Learning in Autonomous Cars","year":2025,"lang":"","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Noise (video); Field (mathematics); Automation; Work (physics)","score_opus":0.005613874323856443,"score_gpt":0.23306816963081403,"score_spread":0.2274542953069576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125498623","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":[0.6405778,0.0011263988,0.2974567,0.0016188682,0.0012997919,0.00051982654,0.0000045952,0.00085731526,0.05653873],"genre_scores_gemma":[0.99110895,0.000099502635,0.0063670636,0.00021831023,0.000030606832,0.0000812109,0.000024219624,0.000026525206,0.002043605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976195,0.00006759765,0.00088427326,0.0005780764,0.00020379749,0.00064674724],"domain_scores_gemma":[0.9992851,0.00014773195,0.00009908789,0.0003564627,0.000068035755,0.000043598677],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00033625902,0.0003854477,0.00042141142,0.0003579199,0.0003535245,0.000039241815,0.00034105757,0.00055458734,0.0009372069],"category_scores_gemma":[0.0000639902,0.00045867782,0.00012653311,0.001226714,0.00025306252,0.00020257565,0.0000972482,0.0012785576,0.00013615139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003387549,0.00022289823,0.016584685,0.00015693769,0.00009339392,0.000014502439,0.00055322686,0.8182459,0.0012011159,0.08295435,0.000068062815,0.07987104],"study_design_scores_gemma":[0.003069859,0.0001735054,0.018155312,0.00041787824,0.00005595133,0.000005371895,0.00057083555,0.9087047,0.04715859,0.0043664845,0.01663471,0.000686808],"about_ca_topic_score_codex":0.00023442367,"about_ca_topic_score_gemma":0.00008426765,"teacher_disagreement_score":0.3505312,"about_ca_system_score_codex":0.0010119037,"about_ca_system_score_gemma":0.0006576921,"threshold_uncertainty_score":0.9999761},"labels":[],"label_agreement":null},{"id":"W7125919536","doi":"10.1109/smc58881.2025.11343727","title":"Learning Implicit Map Representations from Trajectories: An Enhanced Map-Free Framework for Motion Forecasting","year":2025,"lang":"","type":"article","venue":"","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":"Ministry of Education and Child Care","funders":"","keywords":"Robustness (evolution); Generalizability theory; Encoder; Task (project management); Trajectory; Motion (physics); Raw data","score_opus":0.016607087794571474,"score_gpt":0.27310962666335603,"score_spread":0.25650253886878455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125919536","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":[0.14194874,0.000345736,0.8496646,0.0012103085,0.0015707178,0.00086725794,0.00009612795,0.00150732,0.0027891672],"genre_scores_gemma":[0.82409716,0.000036199028,0.17361929,0.0000733961,0.00025914656,0.00024838615,0.00016161798,0.000072259434,0.0014325402],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971784,0.00008546943,0.00090767676,0.0008900088,0.00014953942,0.00078895554],"domain_scores_gemma":[0.99703544,0.0013367222,0.00018738488,0.0011603194,0.00016453952,0.000115583396],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034836875,0.0004712171,0.00056699855,0.0002199851,0.0011361532,0.00017251815,0.0006906252,0.0011275368,0.0005444759],"category_scores_gemma":[0.0010614303,0.0005689784,0.00023785602,0.00055951765,0.00016073676,0.000493734,0.00021429801,0.0012838297,0.000037316182],"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":[0.00033013162,0.00027755494,0.005487617,0.00070403214,0.0012153212,0.000008058711,0.00917716,0.26738042,0.013508162,0.3406928,0.0008955695,0.3603232],"study_design_scores_gemma":[0.0012563474,0.00024943546,0.004388297,0.00036600605,0.0002476058,0.0000012356863,0.005676441,0.6028077,0.032405812,0.35037163,0.0015805069,0.00064897945],"about_ca_topic_score_codex":0.00037097483,"about_ca_topic_score_gemma":0.0002881419,"teacher_disagreement_score":0.68214846,"about_ca_system_score_codex":0.00026813927,"about_ca_system_score_gemma":0.00010861238,"threshold_uncertainty_score":0.99967617},"labels":[],"label_agreement":null},{"id":"W7125926945","doi":"10.1109/ase63991.2025.00143","title":"ADPerf: Investigating and Testing Performance in Autonomous Driving Systems","year":2025,"lang":"","type":"article","venue":"","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":"University of Waterloo; Concordia University","funders":"","keywords":"Obstacle; Latency (audio); Bottleneck; Reliability (semiconductor); Obstacle avoidance; Point cloud; Autonomous system (mathematics); Lidar","score_opus":0.011463343226227777,"score_gpt":0.20855146120799187,"score_spread":0.1970881179817641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125926945","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":[0.9375104,0.003924823,0.0020070772,0.00026552475,0.0005787782,0.00045472346,0.0000014671587,0.0008987307,0.054358482],"genre_scores_gemma":[0.99458605,0.00022554345,0.0038829646,0.00006674109,0.000045918492,0.00005176802,0.0000012273472,0.00004023237,0.0010995696],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99747604,0.00006421458,0.0009914908,0.0005713687,0.00010685266,0.0007900241],"domain_scores_gemma":[0.9988648,0.00044961806,0.00010930812,0.00041735778,0.000053559685,0.00010533068],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00079160475,0.0004349712,0.00058868894,0.000534349,0.00036206245,0.00012219675,0.00031057963,0.00057595066,0.00001617897],"category_scores_gemma":[0.00039096657,0.00049646595,0.000034991448,0.001163126,0.00027791245,0.0003766046,0.00033384183,0.0011421598,0.00002388363],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026606456,0.000025824505,0.8008143,0.0012073095,0.000062603336,0.000014879822,0.00050633843,0.09261439,0.001992026,0.01509816,0.000020081705,0.08764144],"study_design_scores_gemma":[0.00041103197,0.000042888663,0.22661352,0.0016304388,0.000025271005,0.000026524094,0.0004959917,0.7687962,0.0010418181,0.00018864324,0.0003613999,0.00036629042],"about_ca_topic_score_codex":0.00023575113,"about_ca_topic_score_gemma":0.00008307898,"teacher_disagreement_score":0.6761818,"about_ca_system_score_codex":0.00035903486,"about_ca_system_score_gemma":0.00021843535,"threshold_uncertainty_score":0.9997487},"labels":[],"label_agreement":null},{"id":"W7125945293","doi":"10.1109/ase63991.2025.00213","title":"On the Robustness Evaluation of 3D Obstacle Detection Against Specifications in Autonomous Driving","year":2025,"lang":"","type":"article","venue":"","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":"Concordia University","funders":"","keywords":"Obstacle; Robustness (evolution); Lidar; Obstacle avoidance; Point cloud; Object detection","score_opus":0.029683775757022572,"score_gpt":0.2504044317873429,"score_spread":0.22072065603032032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125945293","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":[0.86521983,0.00030777944,0.08801732,0.0010681984,0.0005684235,0.00092855823,0.0000036456977,0.00023388458,0.043652385],"genre_scores_gemma":[0.9990057,0.000116335046,0.00033564094,0.000041790332,0.000019966246,0.00013849745,0.0000040312543,0.000022174174,0.00031588893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800646,0.00024071129,0.0007795853,0.00036598352,0.00027593607,0.0003313184],"domain_scores_gemma":[0.99830085,0.00054337084,0.00013969683,0.0007975593,0.000193688,0.000024848736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019001542,0.00024779662,0.00029363856,0.00051265565,0.0002505828,0.000026533395,0.00037488277,0.00040342318,0.0003489769],"category_scores_gemma":[0.0003629449,0.0002260076,0.00008694344,0.0012597176,0.0001638905,0.0001368452,0.00007486147,0.00067039946,0.000033192464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010523112,0.00012208444,0.00048674547,0.000028383187,0.000062865794,4.782185e-7,0.00019823937,0.72153825,0.0034303854,0.04188916,0.000026707161,0.23220618],"study_design_scores_gemma":[0.0005300927,0.000027917558,0.05540678,0.00015787195,0.00007933588,7.265065e-7,0.0006233223,0.9104364,0.029168079,0.003282778,0.00010615399,0.00018052809],"about_ca_topic_score_codex":0.0000311848,"about_ca_topic_score_gemma":0.00065216265,"teacher_disagreement_score":0.23202565,"about_ca_system_score_codex":0.00092164386,"about_ca_system_score_gemma":0.00022769358,"threshold_uncertainty_score":0.9216323},"labels":[],"label_agreement":null},{"id":"W7126428564","doi":"10.21428/594757db.0bd8648b","title":"DRNet: A Decision-Making Method for Autonomous Lane Changing with Deep Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"","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":"Université de Sherbrooke","funders":"","keywords":"Reinforcement learning; Leverage (statistics); Task (project management); Baseline (sea); Autonomous agent; State (computer science); Representation (politics)","score_opus":0.0059720956613611,"score_gpt":0.25154658126337964,"score_spread":0.24557448560201856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126428564","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":[0.0030967917,0.0006245696,0.9807494,0.000084989355,0.00015173492,0.0002690734,6.490733e-7,0.0029240656,0.01209871],"genre_scores_gemma":[0.80905473,0.000018391549,0.18965259,0.000048488586,0.000057500998,0.000083710016,0.000005713135,0.00005386699,0.0010249765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902886,0.000009038837,0.00021345432,0.0002404891,0.00009151108,0.00041664075],"domain_scores_gemma":[0.9993928,0.00036389977,0.000017662322,0.00016904055,0.000019169813,0.000037396385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003740482,0.00018120493,0.0001929326,0.00028647317,0.00014503906,0.000052645795,0.0001324922,0.00013907612,0.00018437048],"category_scores_gemma":[0.0000256621,0.00014806564,0.00006358953,0.00030093125,0.000018697636,0.00011389534,0.000046453082,0.00029669586,0.000057151276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018398167,0.0000027398216,0.000023193435,0.000085991174,0.00010297995,0.00002432441,0.0005806401,0.58939546,0.00012832208,0.021295791,0.00010229594,0.38823986],"study_design_scores_gemma":[0.00017169831,0.000100854246,0.00002092066,0.00015376345,0.00003248809,0.0000519978,0.00025425316,0.9721244,0.0008228583,0.001499529,0.02454479,0.00022242374],"about_ca_topic_score_codex":0.0000034485968,"about_ca_topic_score_gemma":0.000022248563,"teacher_disagreement_score":0.805958,"about_ca_system_score_codex":0.00012140014,"about_ca_system_score_gemma":0.000023889874,"threshold_uncertainty_score":0.6037941},"labels":[],"label_agreement":null},{"id":"W7127127490","doi":"10.18280/ijsse.151101","title":"Proximal Policy Optimization–Based Deep Reinforcement Learning for Intelligent Test Case Generation in Autonomous Vehicles","year":2025,"lang":"","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reinforcement learning; Test (biology); Intelligent transportation system; Reinforcement; Scenario testing; Poison control","score_opus":0.007271170258599803,"score_gpt":0.23986585143805764,"score_spread":0.23259468117945784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127127490","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":[0.017561678,0.0019768514,0.9765228,0.0023650697,0.0008805673,0.00041928823,0.000022237085,0.00007344463,0.00017805626],"genre_scores_gemma":[0.9900861,0.0016549856,0.0075954366,0.000093072915,0.00043451745,0.000018231001,0.000046171564,0.00003356047,0.000037959548],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997672,0.000033946304,0.0014423478,0.00024942242,0.00023391483,0.00036837146],"domain_scores_gemma":[0.99862105,0.0004031968,0.00031512143,0.00012482492,0.0004308999,0.00010491747],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00075823866,0.00033647948,0.0004609086,0.0013067058,0.00015606999,0.000114520146,0.00031818502,0.00034665392,0.00002598391],"category_scores_gemma":[0.0008148219,0.00039302584,0.0001740614,0.00036530185,0.00006572603,0.00039284403,0.00010211469,0.0008772921,7.168184e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021423637,0.00010986543,0.0005370899,0.0001881953,0.0002758083,0.00029699042,0.00094255427,0.9763898,0.00038572977,0.0068300734,0.000007746599,0.013821856],"study_design_scores_gemma":[0.0019682816,0.00020771805,0.0001466267,0.00039776135,0.00006427209,0.0005066344,0.00022509349,0.9911962,0.0021566288,0.0001434005,0.0026946615,0.00029268925],"about_ca_topic_score_codex":0.000035927213,"about_ca_topic_score_gemma":0.000037761383,"teacher_disagreement_score":0.9725244,"about_ca_system_score_codex":0.0010099225,"about_ca_system_score_gemma":0.00030829452,"threshold_uncertainty_score":0.9998522},"labels":[],"label_agreement":null},{"id":"W7127429228","doi":"10.1109/ccece64018.2025.11364403","title":"Uncertainty-Aware Lane Change Prediction for Autonomous Driving Using LSTM Networks","year":2025,"lang":"","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"SAFER; Sensitivity (control systems); Acceleration; Ranging; Noise (video); Standard deviation; Gaussian; Advanced driver assistance systems","score_opus":0.020328192259909762,"score_gpt":0.252660218736448,"score_spread":0.23233202647653825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127429228","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":[0.030650958,0.0022629863,0.95582783,0.0008643702,0.0033839622,0.0017453904,0.00007777773,0.0022197138,0.0029670175],"genre_scores_gemma":[0.99314475,0.0003991658,0.0036255328,0.00028264266,0.00064195687,0.00023501659,0.00008104115,0.00008068006,0.0015092291],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973784,0.000047253823,0.0007823851,0.0006579667,0.00010908013,0.001024885],"domain_scores_gemma":[0.99879825,0.00023284295,0.00012605441,0.00061774265,0.000114450275,0.000110687164],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046240335,0.00052834756,0.00062151795,0.00038950433,0.0006454483,0.0000734829,0.00039473546,0.0012306465,0.00019352452],"category_scores_gemma":[0.00004428777,0.0006003067,0.00024316319,0.0006361702,0.00014110883,0.00031216172,0.0002085932,0.000772537,0.000012756139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000708022,0.000105196574,0.007827746,0.0004679465,0.0006083574,0.000010782776,0.00034673925,0.8025234,0.000257612,0.020534119,0.0010464689,0.16620086],"study_design_scores_gemma":[0.00088727113,0.00009223857,0.004073489,0.00037851522,0.00027269812,0.000011652512,0.00024389049,0.9806092,0.00024423745,0.0009778849,0.011753605,0.0004553623],"about_ca_topic_score_codex":0.00021637909,"about_ca_topic_score_gemma":0.00025631968,"teacher_disagreement_score":0.9624938,"about_ca_system_score_codex":0.00081908447,"about_ca_system_score_gemma":0.00014053253,"threshold_uncertainty_score":0.9996448},"labels":[],"label_agreement":null},{"id":"W7128609739","doi":"10.1109/icves65691.2025.11376371","title":"Virtual Testing Environments for LiDARs: A Study of Scene Composition Effects","year":2025,"lang":"","type":"article","venue":"","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":"Transport Canada; Western University; National Research Council Canada","funders":"","keywords":"Chamfer (geometry); Representation (politics); Point (geometry); Replicate; Variety (cybernetics); Digital mapping; Test (biology)","score_opus":0.008509710216373255,"score_gpt":0.22977821249006783,"score_spread":0.22126850227369457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7128609739","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":[0.82372725,0.00019851916,0.17238228,0.0000423391,0.0002962084,0.0017631744,0.000007690108,0.00020957534,0.001372935],"genre_scores_gemma":[0.9945908,0.0000111819245,0.0048634363,0.000033259494,0.000022159646,0.00012750148,0.000005114321,0.000026981395,0.00031953448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856,0.000055386412,0.0005778888,0.00035199625,0.00011132157,0.00034340972],"domain_scores_gemma":[0.9987858,0.0006758153,0.000099868026,0.00037653043,0.00002329665,0.00003867264],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024790532,0.00027363043,0.00046808933,0.0002145828,0.00019467853,0.000012910548,0.00023979443,0.00029324202,0.000016530592],"category_scores_gemma":[0.000082160965,0.00030693144,0.00007727824,0.0003323281,0.0000908331,0.000094855735,0.00015357141,0.0002693059,0.000013172241],"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":[0.00045706978,0.0067829173,0.037601955,0.0014905344,0.002170733,0.000021975224,0.0020407299,0.07263197,0.30762452,0.009488955,0.00014301542,0.55954564],"study_design_scores_gemma":[0.0111909,0.0073498148,0.11515054,0.0009108264,0.0009357828,0.000004642971,0.0021555824,0.5249155,0.33517796,0.0012092664,0.00022061513,0.00077855855],"about_ca_topic_score_codex":0.000022668668,"about_ca_topic_score_gemma":0.000005436815,"teacher_disagreement_score":0.5587671,"about_ca_system_score_codex":0.00014937292,"about_ca_system_score_gemma":0.000028660126,"threshold_uncertainty_score":0.99993825},"labels":[],"label_agreement":null},{"id":"W7131077999","doi":"10.1109/iccvw69036.2025.00091","title":"SADWA: Fine-Grained Weather Awareness with Vision-Language Models for Seamless Autonomous Driving in Real Time","year":2025,"lang":"","type":"article","venue":"","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Visibility; Software deployment; Focus (optics); Scalability; Adverse weather; Categorization; Inference; Granularity","score_opus":0.006012780253468677,"score_gpt":0.2458887809635749,"score_spread":0.2398760007101062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131077999","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":[0.55770713,0.0006822857,0.3948121,0.0013960861,0.0002991496,0.0019654124,0.000069960835,0.0019380586,0.041129794],"genre_scores_gemma":[0.97300774,0.000079753256,0.0093627805,0.000073222574,0.000055303124,0.0002715985,0.000042824526,0.00013204417,0.01697473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99677247,0.00006835281,0.0009010803,0.0009514622,0.00018138567,0.0011252472],"domain_scores_gemma":[0.9982446,0.0004383012,0.00011725971,0.00097516127,0.000102990285,0.00012173034],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00059045263,0.00072580966,0.0010252335,0.00067059614,0.00030010898,0.00008287659,0.0006908636,0.0009165949,0.000298368],"category_scores_gemma":[0.000039381517,0.0006834612,0.00019430158,0.00093274214,0.00020565405,0.0004203508,0.00025439626,0.00065872463,0.00004931432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075367774,0.000766917,0.0059554437,0.0009631075,0.0009500551,0.00020311656,0.0046375412,0.79382676,0.008847308,0.07328124,0.0010984004,0.10871644],"study_design_scores_gemma":[0.003169442,0.00022695855,0.0026969346,0.0005970098,0.000120796474,0.000010248191,0.00062185625,0.9837372,0.0037586698,0.0038647412,0.00036060283,0.00083555107],"about_ca_topic_score_codex":0.00048188027,"about_ca_topic_score_gemma":0.0019068784,"teacher_disagreement_score":0.4153006,"about_ca_system_score_codex":0.00053975644,"about_ca_system_score_gemma":0.00050598016,"threshold_uncertainty_score":0.99956167},"labels":[],"label_agreement":null},{"id":"W7131084870","doi":"10.1109/iccvw69036.2025.00503","title":"HiSS: Human-Inspired Semantic Segmentation for Vehicle Interior Scene Understanding","year":2025,"lang":"","type":"article","venue":"","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Segmentation; Intersection (aeronautics); Process (computing); Focus (optics); Component (thermodynamics); Image segmentation; Key (lock); Pixel; Semantics (computer science)","score_opus":0.031064837575295993,"score_gpt":0.28286843238436643,"score_spread":0.25180359480907044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131084870","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":[0.16235086,0.0007851887,0.81963664,0.0021525335,0.0011613338,0.0013393535,0.00001995507,0.0012778756,0.011276236],"genre_scores_gemma":[0.9916496,0.000106930725,0.0037915085,0.0002486074,0.00005839372,0.00010563538,0.000026230415,0.000056879297,0.0039562164],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997937,0.000035127097,0.0007706378,0.0005081993,0.00011190989,0.0006371283],"domain_scores_gemma":[0.99918604,0.00014299917,0.00010591095,0.0004398116,0.00005524378,0.00006998559],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034328332,0.0003878719,0.00048085416,0.0004250134,0.0006626955,0.00009309574,0.0003395068,0.00050225825,0.0003397395],"category_scores_gemma":[0.00003733525,0.00044983014,0.00020276646,0.00046810572,0.00017884547,0.00027187823,0.00013322204,0.00034771266,0.00005121031],"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":[0.00026675028,0.00023877433,0.004355356,0.002683977,0.0015170655,0.000017683471,0.0016279953,0.0028350835,0.41310495,0.504694,0.0042011915,0.064457186],"study_design_scores_gemma":[0.0068433313,0.00054339896,0.0030117242,0.0011826804,0.0007050142,0.000006929161,0.007743771,0.5964033,0.32112002,0.058657255,0.0024337084,0.0013488611],"about_ca_topic_score_codex":0.00003844022,"about_ca_topic_score_gemma":0.00013988114,"teacher_disagreement_score":0.82929873,"about_ca_system_score_codex":0.0013834893,"about_ca_system_score_gemma":0.00008176856,"threshold_uncertainty_score":0.9997953},"labels":[],"label_agreement":null},{"id":"W7131658085","doi":"10.5220/0014376400004918","title":"Evaluating Vision-Based Object Recognition Algorithms for Autonomous Vehicles with Varying Environmental Conditions: A Benchmark Study","year":2025,"lang":"","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Benchmark (surveying); Object (grammar); Cognitive neuroscience of visual object recognition; Noise (video); Pattern recognition (psychology); Feature (linguistics)","score_opus":0.024497520334138805,"score_gpt":0.30814567711239227,"score_spread":0.28364815677825345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131658085","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":[0.8663615,0.0007959795,0.119810246,0.00053833507,0.00056599424,0.006745163,0.00047707508,0.0013032993,0.0034024203],"genre_scores_gemma":[0.9630865,0.000029268786,0.034325857,0.00025221123,0.000065592634,0.0014444602,0.00036715274,0.00008814843,0.00034081496],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965166,0.00019568879,0.0010622013,0.0010593047,0.0003488046,0.0008173939],"domain_scores_gemma":[0.9977747,0.0011345746,0.0002225419,0.0006741915,0.00008059111,0.0001134005],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011642758,0.0006903514,0.00070529897,0.0005775451,0.0012228674,0.0001266469,0.00037306006,0.0004996334,0.0008052446],"category_scores_gemma":[0.00010108586,0.00070128206,0.00021967477,0.0005452591,0.00029289487,0.0003727534,0.00011711372,0.0006966503,0.000073285475],"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":[0.0011042048,0.0044455435,0.009615635,0.00063431676,0.0024913617,0.000066507484,0.0014209778,0.16507894,0.012063434,0.00032227335,0.00030948356,0.8024473],"study_design_scores_gemma":[0.008697788,0.004161131,0.014018923,0.0005374899,0.0010409338,0.000012036579,0.0027934841,0.9535191,0.011747581,0.0023089256,0.00014279305,0.0010198199],"about_ca_topic_score_codex":0.000051966592,"about_ca_topic_score_gemma":0.000030764204,"teacher_disagreement_score":0.8014275,"about_ca_system_score_codex":0.00076845585,"about_ca_system_score_gemma":0.00038259517,"threshold_uncertainty_score":0.99954385},"labels":[],"label_agreement":null},{"id":"W7132915324","doi":"","title":"Vehicle Motion Prediction Using Locally Conditioned Trajectory Sets","year":2023,"lang":"","type":"dissertation","venue":"TSpace","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":"University of Toronto","funders":"","keywords":"Trajectory; Motion (physics); Generalization; Set (abstract data type); Frame (networking); Vehicle dynamics; Control theory (sociology); Training (meteorology)","score_opus":0.020759992204988618,"score_gpt":0.2901251167015796,"score_spread":0.26936512449659095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132915324","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":[0.9753181,0.00057566504,0.014830839,0.00017253406,0.0032456724,0.0008342417,0.000107571126,0.0027360646,0.0021793281],"genre_scores_gemma":[0.99189264,0.00033718706,0.00047876488,0.000020169486,0.00025443188,0.00007652578,0.0019498838,0.00026661024,0.0047237994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99720657,0.00011287803,0.00079292315,0.000759546,0.0003888246,0.00073927676],"domain_scores_gemma":[0.99865717,0.00013595249,0.00032196083,0.0005777829,0.00014914651,0.00015801351],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00035488914,0.0006819296,0.0006432305,0.00058178045,0.0005963598,0.00005714865,0.00030483506,0.0020194869,0.00067999715],"category_scores_gemma":[0.00008116152,0.0009056935,0.00025367466,0.0008059269,0.00013969425,0.0002917348,0.00004284435,0.0014119493,0.00087489956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050557475,0.00037158444,0.0028473085,0.002907995,0.0015916886,0.00018166125,0.027190331,0.66593695,0.2580625,0.0019662043,0.0015654255,0.036872763],"study_design_scores_gemma":[0.0015369724,0.00021676713,0.07603573,0.00081627123,0.00058380596,0.00003408705,0.00791758,0.88786536,0.022343887,0.0012613573,0.00020298494,0.0011852054],"about_ca_topic_score_codex":0.00028731403,"about_ca_topic_score_gemma":0.00022939757,"teacher_disagreement_score":0.23571861,"about_ca_system_score_codex":0.0007538298,"about_ca_system_score_gemma":0.00021083382,"threshold_uncertainty_score":0.999903},"labels":[],"label_agreement":null},{"id":"W7132998882","doi":"","title":"Design and Development of the Artemis Autonomous Vehicle for the SAE/GM AutoDrive Challenge II Year 2","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Competition (biology); Perception; Autonomy; Series (stratigraphy); General motors","score_opus":0.02141313955417786,"score_gpt":0.2695833846344545,"score_spread":0.24817024508027666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132998882","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":[0.80200744,0.059232533,0.09271593,0.019717278,0.00543868,0.011651232,0.00007367652,0.0015868505,0.0075763906],"genre_scores_gemma":[0.9823937,0.00086317194,0.0068718675,0.00001971783,0.00007863406,0.00040502282,0.000020405658,0.00013046712,0.009217028],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981355,0.000053599695,0.00060043175,0.0005143927,0.00020782533,0.0004882293],"domain_scores_gemma":[0.9985083,0.00047729845,0.00023780987,0.0006285271,0.000086355394,0.00006172776],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057297555,0.0005076457,0.0005115281,0.00010559349,0.0008455528,0.00003364412,0.00060240366,0.00079306116,0.00010594464],"category_scores_gemma":[0.000058847298,0.00036081625,0.00016287858,0.00022433391,0.00020328598,0.00005206089,0.00021319788,0.0008745095,0.000045562785],"study_design_candidate":"qualitative","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":[0.00047944588,0.00033688903,0.000064499545,0.0052307285,0.004278621,0.000011060954,0.5953255,0.0347901,0.027422238,0.03604203,0.0027123417,0.2933065],"study_design_scores_gemma":[0.0040920964,0.001140157,0.017240837,0.003238297,0.0023582669,0.000037967024,0.07361319,0.54798335,0.1877351,0.0103251645,0.14876036,0.0034752353],"about_ca_topic_score_codex":0.00002637116,"about_ca_topic_score_gemma":0.00017453707,"teacher_disagreement_score":0.52171236,"about_ca_system_score_codex":0.0002451262,"about_ca_system_score_gemma":0.0005343672,"threshold_uncertainty_score":0.99988437},"labels":[],"label_agreement":null},{"id":"W7132999674","doi":"","title":"On the Design and Validation of an Autonomous Vehicle Perception System for the SAE/GM AutoDrive Challenge II","year":2022,"lang":"","type":"dissertation","venue":"TSpace","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Software; Perception; Competition (biology); Autonomy; Series (stratigraphy); Autonomous system (mathematics); Tracking (education)","score_opus":0.022033771489582844,"score_gpt":0.2776975084777832,"score_spread":0.25566373698820033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132999674","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":[0.9721828,0.0011159261,0.017743671,0.0026883022,0.0007870389,0.0039558182,0.00004759406,0.00046585748,0.0010130063],"genre_scores_gemma":[0.99652624,0.00036729075,0.0006156239,0.00002056457,0.000084769905,0.000974505,0.00018180437,0.000113270864,0.0011159439],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977698,0.00036597406,0.0005755361,0.0005768134,0.00029420183,0.0004176747],"domain_scores_gemma":[0.99715126,0.0013801317,0.00046098247,0.0008437818,0.00010335742,0.00006047203],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0014282957,0.00050446176,0.0005266755,0.00017504876,0.0018359052,0.00004343591,0.00059229997,0.0006380618,0.000384388],"category_scores_gemma":[0.00009546337,0.00038453913,0.00017446991,0.00023854259,0.00015225771,0.00014245394,0.00009391744,0.0009009327,0.000012130847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015051067,0.00050505705,0.000016259555,0.0023228263,0.0011428463,0.000007655394,0.20512097,0.46996447,0.038963277,0.196051,0.00032521843,0.0840753],"study_design_scores_gemma":[0.001115967,0.002606995,0.003146766,0.00029071886,0.00068244,0.000016513606,0.09600217,0.87896556,0.0139279375,0.0017364778,0.0008144777,0.0006939796],"about_ca_topic_score_codex":0.00017341648,"about_ca_topic_score_gemma":0.00005932635,"teacher_disagreement_score":0.40900108,"about_ca_system_score_codex":0.00047629673,"about_ca_system_score_gemma":0.0001282029,"threshold_uncertainty_score":0.99986064},"labels":[],"label_agreement":null},{"id":"W7134979062","doi":"10.1109/aiot66900.2025.00028","title":"Compressing Multi-Task Model for Autonomous Driving via Pruning and Knowledge Distillation","year":2025,"lang":"","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Pruning; Compression (physics); Distillation; Knowledge-based systems; Feature (linguistics); Matching (statistics)","score_opus":0.015766357239902034,"score_gpt":0.2678684720140777,"score_spread":0.25210211477417566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134979062","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":[0.042046014,0.0036309492,0.94656503,0.00021831802,0.00041048366,0.00078840903,0.000014196061,0.0010096518,0.005316962],"genre_scores_gemma":[0.9265839,0.00009517295,0.0686747,0.000027437181,0.000038778016,0.00006193386,0.000013663479,0.00005708861,0.0044473666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798363,0.000035111196,0.00070440397,0.00059955,0.000057849364,0.00061945285],"domain_scores_gemma":[0.99889934,0.00040018096,0.00011136159,0.00039727223,0.00009574957,0.00009609011],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034570476,0.000443076,0.00055271684,0.00031735183,0.000774627,0.00010573933,0.00023552781,0.00061812473,0.000012740333],"category_scores_gemma":[0.00009655379,0.0005083517,0.00011802826,0.00026427433,0.00021573303,0.00030622727,0.00028961225,0.00047938112,0.000008121495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031832373,0.00013162133,0.0037979134,0.00075322733,0.00027210722,0.0000014589443,0.0021506625,0.57904935,0.009678008,0.025461836,0.00010986931,0.3785621],"study_design_scores_gemma":[0.0012289697,0.000024661047,0.004535586,0.0003097662,0.00015463363,0.0000031416362,0.00011623958,0.9880666,0.0014895304,0.0024637815,0.0011298294,0.0004772369],"about_ca_topic_score_codex":0.000019788498,"about_ca_topic_score_gemma":0.00016537086,"teacher_disagreement_score":0.8845379,"about_ca_system_score_codex":0.00027935015,"about_ca_system_score_gemma":0.00015194167,"threshold_uncertainty_score":0.9997368},"labels":[],"label_agreement":null},{"id":"W7136550126","doi":"10.1109/itsc60802.2025.11423765","title":"Efficacy of Early and Late Spatio-Temporal Fusion in LSTM Models for Vehicle Trajectory Prediction","year":2025,"lang":"","type":"article","venue":"","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":"Conestoga College; Wilfrid Laurier University","funders":"","keywords":"Trajectory; Sensor fusion; Fusion; Pattern recognition (psychology); Artificial neural network","score_opus":0.010603561823965723,"score_gpt":0.22158951174209518,"score_spread":0.21098594991812947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7136550126","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":[0.9305462,0.00079843483,0.06529987,0.00017848077,0.00021899534,0.00082202477,0.000035309666,0.00023604394,0.0018646233],"genre_scores_gemma":[0.9975966,0.00025717026,0.0014821741,0.000017270631,0.000017533219,0.00003876477,0.000015896761,0.000022537828,0.00055201765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986171,0.000032087766,0.00063948455,0.00032936383,0.00008009054,0.0003018492],"domain_scores_gemma":[0.9994352,0.00015013378,0.00007329951,0.000253364,0.00004651621,0.00004152715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032268086,0.00021292173,0.00037476746,0.00035544098,0.00008793661,0.000011657079,0.00012970368,0.0005182926,0.000028977112],"category_scores_gemma":[0.000024034189,0.00023698456,0.000082368686,0.0003409279,0.000118064294,0.0002513969,0.00007146639,0.0003154259,0.0000016686432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002924321,0.0007538714,0.14182077,0.0016439005,0.0004072286,0.0000067291703,0.003933204,0.5480321,0.019037236,0.053066954,0.00017473708,0.22819893],"study_design_scores_gemma":[0.0026542817,0.00021081937,0.21981952,0.00015723232,0.000045538094,3.8326885e-7,0.000050540184,0.76705086,0.004603214,0.0051334733,0.00012946675,0.00014465513],"about_ca_topic_score_codex":0.00024741067,"about_ca_topic_score_gemma":0.00023391175,"teacher_disagreement_score":0.22805427,"about_ca_system_score_codex":0.00009750124,"about_ca_system_score_gemma":0.00007613383,"threshold_uncertainty_score":0.966395},"labels":[],"label_agreement":null},{"id":"W7143755852","doi":"10.71465/ajainn671","title":"AI-Driven Decision Support for Autonomous Vehicles","year":2025,"lang":"","type":"article","venue":"American Journal of Artificial Intelligence and Neural Networks","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Decision support system; Key (lock); Intelligent decision support system; Reinforcement learning; Control (management)","score_opus":0.016325527604279907,"score_gpt":0.28318913179382366,"score_spread":0.26686360418954375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7143755852","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":[0.19147746,0.002154156,0.8015621,0.0025376254,0.0018214147,0.00030465913,0.0000105616045,0.000069786045,0.000062250954],"genre_scores_gemma":[0.9907238,0.0027108556,0.004961422,0.0010693552,0.00043983283,0.000008408162,0.0000023719747,0.000039907518,0.00004403757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968935,0.00008080908,0.0017467755,0.00038449626,0.00017794096,0.0007164388],"domain_scores_gemma":[0.9976509,0.00092866505,0.00059578975,0.00030363665,0.00030186534,0.0002191293],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00063835527,0.00042701943,0.0009895063,0.0004500644,0.0003658987,0.0001311739,0.00057955045,0.0003128117,0.00004599557],"category_scores_gemma":[0.00012031595,0.00041036372,0.00037898775,0.00078254496,0.0010249582,0.0003268435,0.00013223155,0.0012021058,0.0000049374376],"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":[0.00068689213,0.00006729383,0.000380601,0.000019559184,0.00016934758,0.000033799643,0.00017390013,0.28833872,0.00015099475,0.0043752273,0.00038321354,0.70522046],"study_design_scores_gemma":[0.00012914526,0.0023973947,0.00040822863,0.00021163153,0.00024506333,0.00011797817,0.0010010056,0.978359,0.002584685,0.011667077,0.0025016183,0.00037720473],"about_ca_topic_score_codex":0.00001673529,"about_ca_topic_score_gemma":0.000027937695,"teacher_disagreement_score":0.7992464,"about_ca_system_score_codex":0.000113210604,"about_ca_system_score_gemma":0.00015696042,"threshold_uncertainty_score":0.99983484},"labels":[],"label_agreement":null},{"id":"W7160150019","doi":"10.1109/iccv51701.2025.02337","title":"SynAD: Enhancing Real-World End-to-End Autonomous Driving Models through Synthetic Data Integration","year":2025,"lang":"","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Data integration; System integration; Synthetic data; Work (physics); Automation; Data modeling","score_opus":0.02351124654552,"score_gpt":0.27399202600527167,"score_spread":0.25048077945975167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160150019","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":[0.010428995,0.0005566229,0.74830925,0.002489976,0.0013467439,0.00084308186,0.00007879671,0.0020743294,0.23387219],"genre_scores_gemma":[0.9556208,0.00083135045,0.03223133,0.00039851948,0.0001247289,0.000080990045,0.000121231475,0.00012067478,0.01047035],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99464464,0.00014758696,0.0017129887,0.0018076927,0.0003416347,0.0013454502],"domain_scores_gemma":[0.9948652,0.000634553,0.00019113529,0.0039755963,0.00013723609,0.00019631926],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012157264,0.00091184565,0.0010821497,0.00087573996,0.0006797352,0.0002513395,0.0023250915,0.0007854414,0.0014832148],"category_scores_gemma":[0.00028847487,0.000993519,0.00018261808,0.0017869492,0.00025389798,0.0020136083,0.0018432433,0.0015538898,0.0003698473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045830664,0.00020733709,0.00015905853,0.0003250639,0.00073262287,0.000051392064,0.0022289793,0.11964238,0.008748375,0.5839008,0.002670607,0.28128752],"study_design_scores_gemma":[0.00036088112,0.00006157691,0.00042769796,0.0010415073,0.00034093286,0.000012889161,0.00084476103,0.9304257,0.037720393,0.021692974,0.0061011673,0.0009694945],"about_ca_topic_score_codex":0.0026485221,"about_ca_topic_score_gemma":0.020987054,"teacher_disagreement_score":0.9451918,"about_ca_system_score_codex":0.0012191506,"about_ca_system_score_gemma":0.0005660604,"threshold_uncertainty_score":0.9994296},"labels":[],"label_agreement":null},{"id":"W7161730581","doi":"10.1109/imtic68267.2025.11520525","title":"A SAT-Guard-V-Satellite-First Edge-AI Framework for Real-Time Vehicle Theft Detection &amp; Resilient Alerting","year":2025,"lang":"","type":"article","venue":"","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":"Concordia University","funders":"","keywords":"Identification (biology); Key (lock); Noise (video); Event (particle physics); Component (thermodynamics)","score_opus":0.009410951108423743,"score_gpt":0.254167929677559,"score_spread":0.24475697856913528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7161730581","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":[0.29336408,0.0021536206,0.6704183,0.005742288,0.0019077609,0.0022364531,0.000036820966,0.0036927974,0.020447876],"genre_scores_gemma":[0.9665408,0.001504191,0.023126444,0.00037935236,0.00027748456,0.00025618522,0.00002274235,0.00014435868,0.0077484325],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956649,0.000106440886,0.0014172317,0.0011349149,0.00024941008,0.0014271297],"domain_scores_gemma":[0.99660105,0.0013156383,0.00023091947,0.0014583356,0.00021053787,0.00018349825],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010255761,0.0007713221,0.00088425836,0.0005181571,0.0011810946,0.000139069,0.00068664545,0.0019407436,0.00051299704],"category_scores_gemma":[0.00069148495,0.0008375665,0.0005005732,0.0011454544,0.0002804153,0.00028066072,0.0003085767,0.0014986747,0.0010216698],"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":[0.0018710438,0.0008707356,0.012499522,0.002831373,0.0023534275,0.000025222722,0.004758142,0.040777992,0.09720599,0.13553081,0.0034450374,0.6978307],"study_design_scores_gemma":[0.0036007275,0.0006664798,0.033661824,0.00223842,0.00095889193,0.000029646111,0.0010701308,0.3642012,0.23065588,0.10471307,0.2550101,0.0031936464],"about_ca_topic_score_codex":0.00015566425,"about_ca_topic_score_gemma":0.0005521231,"teacher_disagreement_score":0.69463706,"about_ca_system_score_codex":0.0006997577,"about_ca_system_score_gemma":0.00015281065,"threshold_uncertainty_score":0.99975616},"labels":[],"label_agreement":null},{"id":"W756839746","doi":"10.1007/978-3-319-21407-8_15","title":"GPS Data Interpolation: Bezier Vs. Biarcs for Tracing Vehicle Trajectory","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Global Positioning System; Bézier curve; Interpolation (computer graphics); Trajectory; Tracing; Ray tracing (physics); Real-time computing; Computer graphics (images); Geodesy; Computer vision; Telecommunications; Geography; Mathematics; Motion (physics); Operating system; Geometry","score_opus":0.03824621766090867,"score_gpt":0.2584862657457419,"score_spread":0.22024004808483325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W756839746","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":[0.00025976164,0.0009765263,0.9946381,0.00022412934,0.0011355134,0.00032092695,0.000058064135,0.00043239974,0.0019546102],"genre_scores_gemma":[0.83259684,0.000038741073,0.16596366,0.00030455747,0.00070632546,0.000012889292,0.00008882046,0.00009321684,0.00019491655],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828887,0.000006374179,0.0003484445,0.00069705193,0.00025767533,0.00040159628],"domain_scores_gemma":[0.99845874,0.00022686507,0.000073445604,0.0010738862,0.00008477464,0.000082268365],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006592718,0.00031689278,0.00036226347,0.00036222857,0.00012465207,0.00006469234,0.0015862389,0.00046787158,0.000027169794],"category_scores_gemma":[0.00005936007,0.00031849823,0.000054747128,0.00017476347,0.0004110383,0.00033140834,0.00039921518,0.00073792244,0.000019267647],"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":[0.000021563152,0.000013267973,0.000065150634,0.00010778908,0.000036368987,0.000019711122,0.00061362475,0.14822802,0.0002907578,0.0027640897,0.0004981641,0.8473415],"study_design_scores_gemma":[0.00023477808,0.00007352134,0.00006598599,0.00015198295,0.000015045272,0.00002066472,3.205494e-7,0.95818496,0.00044584542,0.031204592,0.0092035355,0.00039875635],"about_ca_topic_score_codex":0.0000041240028,"about_ca_topic_score_gemma":0.00010094921,"teacher_disagreement_score":0.8469427,"about_ca_system_score_codex":0.00021803346,"about_ca_system_score_gemma":0.00019000526,"threshold_uncertainty_score":0.9999267},"labels":[],"label_agreement":null},{"id":"W9523852","doi":"10.1016/s0022-2143(98)90100-7","title":"RoadLab: An In-Vehicle Laboratory for Developing On-Board i-ADAS.","year":2010,"lang":"en","type":"article","venue":"Computer Applications in Industry and Engineering","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":"Université Laval; Western University","funders":"","keywords":"Context (archaeology); Advanced driver assistance systems; Scalability; Computer science; Intelligent transportation system; Instrumentation (computer programming); Road traffic; On board; Vehicle safety; Engineering; Computer security; Systems engineering; Transport engineering; Aeronautics; Artificial intelligence; Automotive engineering","score_opus":0.008076695276608549,"score_gpt":0.22210946743150442,"score_spread":0.21403277215489588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W9523852","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":[0.87333566,0.000029559109,0.12572436,0.000083032966,0.00013812147,0.00023985219,0.000009439503,0.00036125406,0.000078709956],"genre_scores_gemma":[0.9690245,0.0000074523086,0.030490223,0.000041602776,0.00012515005,0.00027466068,0.000009372376,0.000024122886,0.000002958364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938214,0.0000035591863,0.00018167682,0.00019085535,0.000031834723,0.0002099192],"domain_scores_gemma":[0.99963963,0.00006674347,0.000012097995,0.00022067408,0.000015106564,0.00004572446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000120055214,0.00013002689,0.00012881357,0.0001645282,0.00004828832,0.000018182767,0.00015793911,0.00040660534,0.0000021576795],"category_scores_gemma":[0.0000054425054,0.00015247731,0.000011827283,0.00027891516,0.00002465431,0.00010756843,0.000027499382,0.00082988176,0.000002335844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001209582,0.00015550904,0.04784757,0.0002749539,0.00004431392,0.000009243793,0.0008261254,0.6494398,0.027012954,0.13279101,0.0002411936,0.14134519],"study_design_scores_gemma":[0.0005808989,0.00003213424,0.120515786,0.000053548865,0.0000040578843,0.0000047356157,0.000053894437,0.8471102,0.009253872,0.00074318226,0.021169841,0.00047786426],"about_ca_topic_score_codex":0.0000022232211,"about_ca_topic_score_gemma":0.000016320633,"teacher_disagreement_score":0.19767034,"about_ca_system_score_codex":0.000042218304,"about_ca_system_score_gemma":0.000018085404,"threshold_uncertainty_score":0.62178445},"labels":[],"label_agreement":null}]}