{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":3322,"total_is_capped":false,"direct_labels_cover":1,"predictions_cover":3322,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"ba5788671a86","filters":{"venue":"Journal of Advanced Transportation"}},"results":[{"id":"W2801188116","doi":"10.1155/2018/5382192","title":"Acceptance of Driverless Vehicles: Results from a Large Cross-National Questionnaire Study","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":332,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Pleasure; Per capita; Technology acceptance model; Psychology; Applied psychology; Computer-assisted web interviewing; Computer science; Usability; Marketing; Medicine; Business; Environmental health; Human–computer interaction","retraction":null,"screen_n_in":null,"score":{"opus":0.02194797941102168,"gpt":0.3935481741947734,"spread":0.3716001947837517,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004587624,0.0001130297,0.0002332167,0.0001712224,0.0001027231,0.0000182281,0.0001622477,0.00007238497,0.0007430057],"category_scores_gemma":[0.0001093692,0.0001072881,0.0001138047,0.0001986886,0.00007566489,0.0006318164,0.000001950048,0.0001966014,0.000044861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006647316,"about_ca_system_score_gemma":0.00007114644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002954388,"about_ca_topic_score_gemma":0.0003103047,"domain_scores_codex":[0.997939,0.0001024665,0.001157844,0.0001851356,0.0004907282,0.0001248261],"domain_scores_gemma":[0.9969291,0.0001146382,0.001189573,0.0001472826,0.001556253,0.00006314333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01960905,0.0069621,0.5462075,0.00005347123,0.001142044,0.0001463728,0.355255,0.01247037,0.009917349,0.0142233,0.003625664,0.03038782],"study_design_scores_gemma":[0.005495427,0.0004703923,0.9848962,0.00009542765,0.00003412612,0.000004042329,0.00566434,0.00004052174,0.0004822131,0.0008224317,0.001899814,0.00009500329],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909228,0.00004592206,0.005913545,0.0001211596,0.001699085,0.0001603429,0.000205407,0.00002538114,0.0009063311],"genre_scores_gemma":[0.9980589,0.000008186711,0.00112376,0.00008622064,0.0003963297,0.000005956852,0.00005049635,0.00001310036,0.0002570715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4386888,"threshold_uncertainty_score":0.8135391,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2912346386","doi":"10.1155/2019/4125865","title":"Driver Distraction Identification with an Ensemble of Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":300,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Technische Universität München; Deutsche Forschungsgemeinschaft","keywords":"Distraction; Convolutional neural network; Computer science; Identification (biology); Artificial intelligence; Machine learning; Segmentation; Ensemble learning; Phone; Deep learning; Distracted driving; Artificial neural network; Face (sociological concept); Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.007240715379631352,"gpt":0.2370285528321021,"spread":0.2297878374524708,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001358814,0.00006960361,0.0001385407,0.0001020756,0.00002989674,0.00001431441,0.0001923286,0.00004519125,0.00000432126],"category_scores_gemma":[0.000004482441,0.00005862028,0.0000462699,0.0001951591,0.00003561342,0.001185735,0.000001368006,0.0001520753,0.00000105366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002554141,"about_ca_system_score_gemma":0.00003095197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002913862,"about_ca_topic_score_gemma":0.00001630944,"domain_scores_codex":[0.9991807,0.00002170541,0.0003542,0.0001272161,0.0002254001,0.00009081831],"domain_scores_gemma":[0.9988161,0.00003191251,0.0006328059,0.0001440456,0.0003415966,0.00003358478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003222282,0.0002283681,0.05132218,0.00002879926,0.00004351689,0.00001749329,0.0004395386,0.7837108,0.1050016,0.01922795,0.000007692875,0.03964979],"study_design_scores_gemma":[0.0006735686,0.0005556302,0.9715878,0.0000327847,0.00001940427,0.00003034165,0.000108797,0.02061454,0.005247265,0.001021034,0.00003332781,0.00007551347],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.572542,0.00002068988,0.4271132,0.00006894031,0.0001935867,0.00003982931,0.000001030885,0.00001447331,0.000006281522],"genre_scores_gemma":[0.9821438,0.0000104002,0.0177774,0.00000994088,0.0000256225,0.000001095446,0.00001404361,0.000004641158,0.00001312241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9202656,"threshold_uncertainty_score":0.2390466,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2800946744","doi":"10.1155/2018/6135183","title":"Studying the Safety Impact of Autonomous Vehicles Using Simulation-Based Surrogate Safety Measures","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":285,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"VisSim; Roundabout; Traffic simulation; Computer science; Intersection (aeronautics); Poison control; Penetration rate; Advanced driver assistance systems; Transport engineering; Simulation; Engineering; Medicine; Artificial intelligence; Emergency medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.01647231292204726,"gpt":0.2653503693449701,"spread":0.2488780564229229,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003108956,0.0001111564,0.0002075895,0.00009553821,0.00008046893,0.00001032305,0.00009682056,0.00002737224,0.00001346438],"category_scores_gemma":[0.00002102953,0.00007126566,0.0001696095,0.0001595388,0.00003570498,0.0002212238,9.358821e-7,0.0001017302,4.631755e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001191282,"about_ca_system_score_gemma":0.00005469521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000152681,"about_ca_topic_score_gemma":0.0001048866,"domain_scores_codex":[0.9989842,0.00002522561,0.0005663621,0.00006324225,0.0002340977,0.0001268768],"domain_scores_gemma":[0.9992142,0.0001539111,0.000247108,0.00009905213,0.000247245,0.00003844065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003243378,0.0000228397,0.001026564,0.00002273548,0.0001368683,0.000002792732,0.0009484224,0.951387,0.007288093,0.00001602868,0.000001575769,0.03882277],"study_design_scores_gemma":[0.002238682,0.0002542399,0.6576065,0.0001179198,0.0001890991,9.099697e-7,0.000335329,0.3375083,0.000856528,0.00006638204,0.0006929136,0.0001331845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8534084,0.0001869286,0.1458714,0.00003412349,0.0002721252,0.0001539661,0.0000130807,0.00003307085,0.00002683903],"genre_scores_gemma":[0.9979197,0.00002984865,0.001916337,0.000008353441,0.0001025502,7.654068e-7,0.000003944235,0.00001618147,0.000002265786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6565799,"threshold_uncertainty_score":0.2906129,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2916752133","doi":"10.1155/2019/4145353","title":"Spatiotemporal Traffic Flow Prediction with KNN and LSTM","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":281,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Autoregressive integrated moving average; Computer science; Traffic flow (computer networking); Support vector machine; Data mining; Autoregressive model; Weighting; Artificial intelligence; Intelligent transportation system; Artificial neural network; Time series; Machine learning; Engineering; Statistics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.002756394652977247,"gpt":0.1781804182304164,"spread":0.1754240235774391,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006752091,0.00008093484,0.0001172402,0.0001173117,0.00001649393,0.00001188364,0.00003431665,0.00003578061,0.000008934711],"category_scores_gemma":[0.000001025936,0.00006976575,0.00002847874,0.00008987937,0.00001124203,0.000552331,4.090542e-7,0.0001154591,0.000001221204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002306371,"about_ca_system_score_gemma":0.000007817664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.050848e-7,"about_ca_topic_score_gemma":0.00001552965,"domain_scores_codex":[0.9994645,0.000004997395,0.0002405082,0.00006432113,0.0001525653,0.00007309501],"domain_scores_gemma":[0.9997685,0.000007344211,0.00008002258,0.00005237024,0.00004960036,0.00004214625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00009488899,0.0000223655,0.00195979,0.0001026985,0.00005273973,0.000009064352,0.0006743908,0.9004397,0.001800952,0.00009064661,0.0004518932,0.09430084],"study_design_scores_gemma":[0.006220314,0.002054517,0.7841593,0.0005960516,0.0002505858,0.00007409061,0.001193779,0.1680447,0.004123458,0.0001159012,0.03266764,0.0004996638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9175384,0.00009868701,0.08125503,0.00005077289,0.0003158364,0.0001507124,0.000008243587,0.000426458,0.0001558334],"genre_scores_gemma":[0.9876398,0.0003303131,0.01191709,0.0000156457,0.00004252432,0.000002932161,0.00001887707,0.00001515671,0.00001768756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7821996,"threshold_uncertainty_score":0.2844965,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3126787811","doi":"10.1155/2021/8878011","title":"A Review of Traffic Congestion Prediction Using Artificial Intelligence","year":2021,"lang":"en","type":"review","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":280,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"RMIT University; Australian Government","keywords":"Traffic congestion; Computer science; Big data; Traffic congestion reconstruction with Kerner's three-phase theory; Artificial intelligence; Focus (optics); Strengths and weaknesses; Machine learning; Transport engineering; Data mining; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.04605669262826885,"gpt":0.3245952508220783,"spread":0.2785385581938095,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003010773,0.0002170371,0.001052564,0.0003040892,0.00001855271,0.000008764112,0.0001183376,0.0001422606,0.00001488886],"category_scores_gemma":[0.0000287773,0.0002072,0.0004566653,0.0004535571,0.00002379556,0.0002917371,0.000001506553,0.0003360969,5.9551e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001189516,"about_ca_system_score_gemma":0.00007765384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.300244e-7,"about_ca_topic_score_gemma":0.00000222813,"domain_scores_codex":[0.9977907,0.00005043017,0.001675395,0.000119676,0.0002583296,0.0001054809],"domain_scores_gemma":[0.9987637,0.00003845774,0.0007791224,0.000126219,0.0002420772,0.00005047268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002461927,0.00002760231,2.700878e-8,0.05978258,0.0001064603,0.00000975375,0.00003507155,0.1072455,0.00000674172,0.0001715324,0.0001556403,0.8324566],"study_design_scores_gemma":[0.0000883401,0.0001583465,0.000007465996,0.438866,0.003128267,0.00008459839,0.0001013202,0.001761829,0.00004858569,0.00004338288,0.5554092,0.000302611],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002241817,0.8920749,0.1064329,0.000004317294,0.0008522981,0.0003448282,0.00004527012,0.0002100328,0.00001298684],"genre_scores_gemma":[0.0006305152,0.9921402,0.006893481,0.000008285206,0.0001245415,0.00001125214,0.0001554198,0.00003552355,8.233184e-7],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.832154,"threshold_uncertainty_score":0.844937,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2747302402","doi":"10.1155/2017/2750452","title":"DSRC versus 4G-LTE for Connected Vehicle Applications: A Study on Field Experiments of Vehicular Communication Performance","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":276,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Higher Education Discipline Innovation Project; Ministry of Transport of the People's Republic of China; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Dedicated short-range communications; Computer science; Wireless; Computer network; Vehicle-to-vehicle; The Internet; Transmission (telecommunications); Vehicular communication systems; Software; Collision avoidance; Vehicular ad hoc network; Real-time computing; Collision; Telecommunications; Operating system; Wireless ad hoc network; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.02049045024582744,"gpt":0.2925169877946091,"spread":0.2720265375487816,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002005761,0.0001246899,0.0002418276,0.00007456206,0.0001580602,0.000021836,0.0003192103,0.00006129993,0.000007438187],"category_scores_gemma":[0.00003496805,0.0001266207,0.00009369581,0.00006529715,0.0000240458,0.0004371191,0.000003194287,0.0001812393,0.000001608288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005299405,"about_ca_system_score_gemma":0.00001734756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000310759,"about_ca_topic_score_gemma":0.00002713859,"domain_scores_codex":[0.9990113,0.00001845692,0.0005053202,0.00009841775,0.0002309218,0.0001355319],"domain_scores_gemma":[0.998663,0.0001374761,0.0004578543,0.0004495608,0.0002403926,0.00005168521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000890277,0.0002096711,0.001663431,0.00007656363,0.000166229,0.000002603205,0.00120986,0.9686809,0.01517596,0.0001051202,0.00003550862,0.01178394],"study_design_scores_gemma":[0.03137262,0.006914516,0.606847,0.0009091011,0.0006823112,0.000006199034,0.004262117,0.1102,0.2325053,0.0003672118,0.00506516,0.0008684882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905127,0.0003676577,0.008040629,0.00005079409,0.0002329313,0.0006861386,0.000008934451,0.00002577195,0.00007443783],"genre_scores_gemma":[0.995996,0.000228845,0.003545628,0.00001084775,0.00006330296,0.0001013276,0.00002346806,0.00002540872,0.000005177864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8584808,"threshold_uncertainty_score":0.5163441,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4214948608","doi":"10.1155/2022/3815306","title":"A Practical and Economical Ultra-wideband Base Station Placement Approach for Indoor Autonomous Driving Systems","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":268,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality; Shanghai Municipal Education Commission","keywords":"Multilateration; Base station; Software deployment; Computer science; Real-time computing; Ultra-wideband; Dilution of precision; Base (topology); Real-time locating system; Wideband; Simulation; Engineering; Electronic engineering; Telecommunications; Global Positioning System","retraction":null,"screen_n_in":null,"score":{"opus":0.01195070939090359,"gpt":0.2381296605792647,"spread":0.2261789511883611,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002407414,0.00009531437,0.0001831695,0.0001401551,0.0001004306,0.0000263248,0.00005106487,0.00004039853,0.000007660537],"category_scores_gemma":[0.00003395402,0.00009749835,0.00004773492,0.00007976339,0.00001755546,0.0003356003,0.000001471601,0.0001846171,1.16105e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001521051,"about_ca_system_score_gemma":0.00004617262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001246872,"about_ca_topic_score_gemma":0.00000302523,"domain_scores_codex":[0.9991584,0.00001915589,0.0004642566,0.00009772673,0.0001354951,0.0001249208],"domain_scores_gemma":[0.9995457,0.0001030692,0.0001880088,0.00005440871,0.00006462483,0.00004419727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001133483,0.00003580637,0.0006436588,0.0001031325,0.00003807202,0.000005170004,0.0006759044,0.9915992,0.003856497,0.001589808,0.0001409647,0.001198417],"study_design_scores_gemma":[0.01689404,0.002880448,0.01588779,0.0001536429,0.000590153,0.0004587458,0.05609833,0.831198,0.05480504,0.00211165,0.01750301,0.001419182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2913406,0.0001431816,0.7077949,0.00006591928,0.0002519522,0.0002837963,0.00003641715,0.00005791477,0.00002536476],"genre_scores_gemma":[0.9715144,0.00006234977,0.02819535,0.00001386991,0.00003920068,0.00007136174,0.0000756016,0.00001979,0.00000806898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6801739,"threshold_uncertainty_score":0.3975867,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2943956374","doi":"10.1155/2019/5075671","title":"A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":259,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"European Regional Development Fund; Hrvatska Zaklada za Znanost","keywords":"Vehicle routing problem; Heuristics; Computer science; Algorithm; Electric vehicle; Greenhouse gas; Operations research; Routing (electronic design automation); Mathematics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.02666724517741675,"gpt":0.2407231984742481,"spread":0.2140559532968313,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001006325,0.0000953113,0.0001485046,0.00008495156,0.00005126449,0.0000223794,0.00007095695,0.00004784003,0.000006568617],"category_scores_gemma":[0.00005536663,0.0000734696,0.00003558941,0.0002894092,0.000008252396,0.0002603761,0.000001013654,0.0002320809,0.000002169328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004130756,"about_ca_system_score_gemma":0.00001836054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004138627,"about_ca_topic_score_gemma":0.00001059646,"domain_scores_codex":[0.9991423,0.0001004559,0.0003508953,0.00008684141,0.0001819993,0.0001374412],"domain_scores_gemma":[0.9993813,0.0002364584,0.0001825242,0.00007926505,0.00008529051,0.00003516674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0000532329,0.00001668715,0.01215809,0.00002787013,0.00002929737,0.000001132569,0.0007136365,0.9574275,0.01795312,0.0006248874,0.000008851539,0.01098569],"study_design_scores_gemma":[0.0006126541,0.0001239265,0.8084256,0.00007313159,0.00002178147,0.000003947808,0.00009990575,0.1882793,0.001938566,0.0003004163,0.0000139546,0.0001067761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.934568,0.0001194819,0.06467986,0.00009087804,0.0001432601,0.0001783929,0.000003167782,0.00003260508,0.0001843617],"genre_scores_gemma":[0.983245,0.00006577694,0.01659451,0.00001862086,0.00003072755,0.000002185147,0.000006800691,0.00002131433,0.00001504255],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7962675,"threshold_uncertainty_score":0.2996003,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.006843731743575368,"gpt":0.2064994454312283,"spread":0.1996557136876529,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006553753,0.00007734884,0.0002154182,0.00005138044,0.00001774103,0.000001496026,0.0001034415,0.00007181368,0.00002013114],"category_scores_gemma":[0.00001017596,0.0000755919,0.00008371487,0.0001356765,0.00003156279,0.0002074824,8.667044e-7,0.0002018803,0.00000237541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002241455,"about_ca_system_score_gemma":0.00002423128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.774831e-7,"about_ca_topic_score_gemma":0.000002588785,"domain_scores_codex":[0.9992409,0.000006164122,0.000516429,0.00005227204,0.00009750598,0.00008673265],"domain_scores_gemma":[0.9996306,0.00002420655,0.0001708644,0.0000536575,0.0000688717,0.00005183535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002516248,0.00002271541,0.001858325,0.0001024479,0.00008599447,0.0000300258,0.002261344,0.8548429,0.07771474,0.001335686,0.00004454721,0.06144964],"study_design_scores_gemma":[0.00510785,0.001113697,0.6565304,0.0001984412,0.0002394445,0.00003577364,0.001885386,0.009909783,0.3013487,0.003247008,0.0198314,0.0005521568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9665922,0.0005259986,0.03192242,0.0004481595,0.0001403333,0.00005966051,0.00001408806,0.00009920911,0.000197927],"genre_scores_gemma":[0.9922479,0.0002654337,0.007388438,0.00003805117,0.00003898534,6.09738e-7,0.000004712475,0.00001331308,0.000002597303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8449332,"threshold_uncertainty_score":0.3082548,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2160777395","doi":"10.1002/atr.5670430206","title":"The multi‐actor, multi‐criteria analysis methodology (MAMCA) for the evaluation of transport projects: Theory and practice","year":2009,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":213,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Process (computing); Management science; Computer science; Process management; Order (exchange); Evaluation methods; Inclusion (mineral); Risk analysis (engineering); Operations research; Engineering; Business; Sociology","retraction":null,"screen_n_in":null,"score":{"opus":0.4136676087511625,"gpt":0.5639888913116063,"spread":0.1503212825604439,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.05275189,0.0001958058,0.0006103129,0.0005091829,0.0003119039,0.00009797093,0.000605772,0.0001041889,0.00003868344],"category_scores_gemma":[0.02511506,0.0001026936,0.0004431156,0.001160166,0.0001651755,0.001191975,0.000003675724,0.0002299926,5.018788e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004779357,"about_ca_system_score_gemma":0.0001710755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000704314,"about_ca_topic_score_gemma":0.0001954776,"domain_scores_codex":[0.9925798,0.002504054,0.002212404,0.0003630287,0.002106666,0.0002340724],"domain_scores_gemma":[0.9648303,0.02777016,0.002607805,0.0004646785,0.004249011,0.00007806457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01015958,0.0003556635,0.00205626,0.00001562376,0.000897068,0.0000107045,0.0232618,0.1031721,0.06434281,0.002583044,0.00004633035,0.793099],"study_design_scores_gemma":[0.005771586,0.0005504355,0.9009522,0.0000569919,0.004823904,0.00003746836,0.02720038,0.02883436,0.002755623,0.02500893,0.003763693,0.0002443976],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.286967,0.001839919,0.7087039,0.001203027,0.0005179665,0.0007174502,0.00003290619,0.000006734344,0.0000110173],"genre_scores_gemma":[0.7657304,0.000259947,0.2337653,0.0001250981,0.00005867322,0.00001610132,0.000006348353,0.000009881393,0.00002819227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.898896,"threshold_uncertainty_score":0.9830968,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2811198925","doi":"10.1155/2018/1096123","title":"A Review of the Self-Adaptive Traffic Signal Control System Based on Future Traffic Environment","year":2018,"lang":"en","type":"review","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":201,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Adaptive control; Floating car data; SIGNAL (programming language); Traffic flow (computer networking); Reinforcement learning; The Internet; Control (management); Guidance system; Real-time computing; Traffic congestion; Control engineering; Engineering; Transport engineering; Artificial intelligence; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.007062774539046484,"gpt":0.2181452006883122,"spread":0.2110824261492657,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000375013,0.0003626462,0.001189994,0.000201139,0.0000399745,0.000006857897,0.0003074389,0.000170953,0.00001325282],"category_scores_gemma":[0.000002901718,0.0002417607,0.0008736541,0.0002340163,0.00003138353,0.0001186394,0.000001232272,0.0004574163,0.000003285517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002982327,"about_ca_system_score_gemma":0.00009205422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.419301e-8,"about_ca_topic_score_gemma":0.000001017672,"domain_scores_codex":[0.9977541,0.0001243145,0.00131694,0.0001670352,0.0004811936,0.0001564348],"domain_scores_gemma":[0.9984783,0.00006243336,0.001058489,0.0002500972,0.00008190392,0.00006878826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003179802,0.00009814255,4.430479e-8,0.09526949,0.000367514,0.00001200201,0.00007502914,0.2332397,9.786369e-7,0.00008309005,0.001982599,0.6688396],"study_design_scores_gemma":[0.0007255274,0.0003919215,0.00001968416,0.148784,0.002545713,0.00001307996,0.00005976662,0.00418451,0.000004109496,5.771544e-7,0.8430303,0.0002407325],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001017238,0.9828365,0.01451171,0.00002587489,0.0007722519,0.001285561,0.00009681533,0.0003825964,0.00007849209],"genre_scores_gemma":[0.009426893,0.9882095,0.001885552,0.00005637475,0.0002752852,0.00006041428,0.00002787617,0.00005613787,0.000001965236],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8410478,"threshold_uncertainty_score":0.9858717,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2756577398","doi":"10.1155/2017/3082781","title":"Will Automated Vehicles Negatively Impact Traffic Flow?","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":197,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Bottleneck; Traffic flow (computer networking); Cripple; Truck; Transport engineering; Computer science; Traffic simulation; Automation; Traffic wave; Simulation; Engineering; Traffic congestion reconstruction with Kerner's three-phase theory; Traffic congestion; Automotive engineering; Microsimulation; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.006438747598037913,"gpt":0.2430655203890703,"spread":0.2366267727910324,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009166695,0.0001192838,0.0002017336,0.00008424948,0.00009909426,0.00004685178,0.0001591639,0.00003556751,0.00001568884],"category_scores_gemma":[0.0000125003,0.00009989851,0.0001396501,0.00004099243,0.00002167472,0.001149194,9.441858e-7,0.0001229951,0.000002827187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005090093,"about_ca_system_score_gemma":0.00001748163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002278581,"about_ca_topic_score_gemma":0.00004883653,"domain_scores_codex":[0.9992743,0.000006585793,0.0003384069,0.00006594471,0.0001716938,0.0001430749],"domain_scores_gemma":[0.9994774,0.00001784639,0.0002190908,0.0001257045,0.00008599505,0.00007388886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00006740943,0.00002020816,0.00008261678,0.0000258547,0.00009727376,0.00003710462,0.0005907241,0.8965949,0.003802533,0.00002429321,0.0001744028,0.09848267],"study_design_scores_gemma":[0.00227127,0.0001644487,0.9272225,0.00009752509,0.00009068629,0.000006168224,0.0001609718,0.06733624,0.0002750967,0.0001117357,0.002101273,0.0001620776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957029,0.0002481067,0.002965791,0.0001344988,0.0005150802,0.00009796449,0.0000140678,0.0002124793,0.0001091659],"genre_scores_gemma":[0.9963868,0.0001844795,0.003283739,0.000007525995,0.00009473499,0.000002344157,0.00000672897,0.00001870701,0.00001488947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9271399,"threshold_uncertainty_score":0.4073743,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3049652883","doi":"10.1155/2020/8867316","title":"COVID-19 Outbreak in Colombia: An Analysis of Its Impacts on Transport Systems","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":191,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Subsidy; TRIPS architecture; Business; Externality; Government (linguistics); Public transport; Local government; Pandemic; Coronavirus disease 2019 (COVID-19); Transport engineering; Economics; Geography; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1524409671983659,"gpt":0.41825691247346,"spread":0.2658159452750941,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009366805,0.0002017166,0.001237274,0.0003459693,0.00004171364,0.000005310453,0.0001978258,0.0001157647,0.00003724825],"category_scores_gemma":[0.002532592,0.0001608263,0.0003526751,0.001083958,0.00004175969,0.0003075692,0.000001834793,0.0002796701,7.616802e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002340967,"about_ca_system_score_gemma":0.0001624677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008349642,"about_ca_topic_score_gemma":0.001146232,"domain_scores_codex":[0.9972023,0.0001626003,0.001687633,0.0002428878,0.0004870064,0.000217615],"domain_scores_gemma":[0.9966341,0.001240429,0.001305956,0.0001385451,0.0002530991,0.00042792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001606729,0.0004225917,0.1278594,0.0008522669,0.0006995331,0.0001818187,0.008823403,0.8532215,0.00320048,0.002853488,0.00006511712,0.0002136575],"study_design_scores_gemma":[0.002422499,0.001667453,0.9869545,0.0001716424,0.001499231,0.000002120768,0.00288161,0.001852732,0.000352499,0.001344092,0.0006078151,0.0002438206],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892567,0.0002962981,0.00826462,0.001594979,0.00009273335,0.0003388798,0.00009993866,0.00002805166,0.00002775988],"genre_scores_gemma":[0.9973939,0.000327412,0.001279612,0.0008932839,0.00004765272,0.000008149848,0.00003032203,0.00001568786,0.000003951222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8590951,"threshold_uncertainty_score":0.6558307,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2752010668","doi":"10.1155/2017/2823617","title":"Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":178,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; National Science Foundation","keywords":"Computer science; Frame (networking); Truck; Detector; Real-time computing; Computer vision; Frame rate; Task (project management); Artificial intelligence; Rotation (mathematics); Simulation; Automotive engineering; Engineering; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.00923285716901435,"gpt":0.2472432198298996,"spread":0.2380103626608852,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001132795,0.0001294727,0.000166618,0.00004955837,0.0004332206,0.0001363542,0.0007911922,0.00003594326,0.000003604848],"category_scores_gemma":[0.00001377354,0.00008469501,0.00008356927,0.0001178355,0.00008129473,0.002155429,0.000009045778,0.0002668509,0.000005949014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000370511,"about_ca_system_score_gemma":0.00003850736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000101263,"about_ca_topic_score_gemma":0.0002231749,"domain_scores_codex":[0.9989593,0.00002392123,0.0003276518,0.0002014664,0.0003278078,0.0001598646],"domain_scores_gemma":[0.9980884,0.00009760399,0.0009081897,0.0005876325,0.0002455124,0.00007262345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0005519756,0.0001769907,0.003448897,0.0000292656,0.0001647147,0.0001955304,0.004325918,0.3637153,0.2772131,0.001565838,0.0001390988,0.3484734],"study_design_scores_gemma":[0.002449111,0.0003814495,0.8941701,0.000178215,0.0001156379,0.00005017322,0.0003247363,0.00294492,0.0861772,0.007716948,0.005068311,0.0004232038],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4402087,0.00006612452,0.5580995,0.001225997,0.000235011,0.0001123761,0.000002902314,0.00002033136,0.0000289851],"genre_scores_gemma":[0.952172,0.00006690908,0.04733732,0.0001519865,0.0002129557,0.00001239823,0.000002943749,0.00001254665,0.00003089617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8907212,"threshold_uncertainty_score":0.3453762,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2743198946","doi":"10.1155/2017/6575947","title":"Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":172,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Deep learning; Leverage (statistics); Computer science; Artificial intelligence; Machine learning; Traffic flow (computer networking); Deep belief network; Predictive modelling; Flow (mathematics); Data mining; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.009046214792051374,"gpt":0.2731775485421342,"spread":0.2641313337500828,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002190087,0.0001251994,0.0001819755,0.0001614257,0.0001627898,0.00005738531,0.0001137309,0.00005208185,0.000007763824],"category_scores_gemma":[0.00001252873,0.0001049143,0.0001004158,0.0000647108,0.00001919846,0.00108554,0.000001056492,0.0002555791,3.62695e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008251965,"about_ca_system_score_gemma":0.00001413769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000355697,"about_ca_topic_score_gemma":0.00003774344,"domain_scores_codex":[0.9992485,0.00001646269,0.0003148901,0.00008222517,0.0002049878,0.0001329123],"domain_scores_gemma":[0.9994254,0.00001476427,0.0002776215,0.0001152448,0.00009720789,0.0000697926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000745464,0.00001302034,0.000482751,0.00002826206,0.00008592379,0.00001884279,0.000659406,0.8671961,0.002860828,0.000008892341,0.00003103521,0.1285404],"study_design_scores_gemma":[0.001810486,0.0004806905,0.1707529,0.000217912,0.0002135723,0.00005826822,0.000407724,0.8229926,0.0009056463,0.00003306118,0.001935384,0.0001917596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3866571,0.00006762795,0.6125389,0.00001236662,0.0001947941,0.00008456547,0.000003596689,0.0003512182,0.00008985923],"genre_scores_gemma":[0.8640388,0.0001974159,0.1356317,0.000004115114,0.00008510053,0.00000263018,0.00001104248,0.0000242787,0.000004917548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4773817,"threshold_uncertainty_score":0.427828,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2789990415","doi":"10.1155/2018/6392697","title":"Global and Local Path Planning Study in a ROS-Based Research Platform for Autonomous Vehicles","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":167,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Motion planning; Trajectory; Point (geometry); Work (physics); Robotics; Software; Computer science; Path (computing); Robot; Collision avoidance; Collision; Simulation; Real-time computing; Artificial intelligence; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.06841923027206204,"gpt":0.3728782159994038,"spread":0.3044589857273418,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00113726,0.00009738332,0.0002018744,0.0002076283,0.0001150537,0.00005342886,0.0002795183,0.00004829129,3.990257e-7],"category_scores_gemma":[0.00005128361,0.0000884423,0.00003498987,0.0004346612,0.00007594393,0.0006450591,0.000005554686,0.0001764125,3.870777e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001330444,"about_ca_system_score_gemma":0.000202194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001950077,"about_ca_topic_score_gemma":0.00004691813,"domain_scores_codex":[0.9986016,0.00004071794,0.0004651815,0.0002091408,0.0004212869,0.0002620839],"domain_scores_gemma":[0.9990249,0.0001989181,0.000206376,0.0001261257,0.0003535356,0.00009011147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0009505865,0.0005409654,0.1481806,0.00005557025,0.00003808425,0.0005422476,0.01691648,0.6897174,0.0004716368,0.0008200528,0.00006179068,0.1417046],"study_design_scores_gemma":[0.003770125,0.003400908,0.8521309,0.000206322,0.0000108498,0.00002035317,0.00201663,0.1347243,0.0002553134,0.003250894,0.00008085572,0.0001325745],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5184189,0.00006467424,0.4811147,0.00006375429,0.0001404208,0.0001761885,0.000003205669,0.00001162311,0.000006480818],"genre_scores_gemma":[0.7965456,0.000001390867,0.2033544,0.00002189438,0.00005997679,0.000007967018,0.000001939257,0.000004921654,0.000001915413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7039503,"threshold_uncertainty_score":0.3606572,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2081945780","doi":"10.1002/atr.129","title":"Investigating effects of asphalt pavement conditions on traffic accidents in Tennessee based on the pavement management system (PMS)","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":153,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"International Roughness Index; Pavement management; Serviceability (structure); Transport engineering; Asphalt; Predictive modelling; Environmental science; Negative binomial distribution; Engineering; Civil engineering; Statistics; Mathematics; Geography; Surface finish","retraction":null,"screen_n_in":null,"score":{"opus":0.004874585276560605,"gpt":0.2122989856581981,"spread":0.2074244003816375,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002736267,0.0001473637,0.0002103475,0.0001880159,0.00005442262,0.000008378332,0.0001340885,0.00005003317,0.00001020544],"category_scores_gemma":[0.0000102505,0.0001080099,0.00009394273,0.0002036948,0.00002291059,0.0001185797,0.000001067276,0.0003177943,0.000002195795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008315842,"about_ca_system_score_gemma":0.00001717284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001303031,"about_ca_topic_score_gemma":0.00005405926,"domain_scores_codex":[0.9987022,0.00003070811,0.0006261935,0.0000997543,0.0003876243,0.0001535079],"domain_scores_gemma":[0.9993129,0.0001773156,0.0002507183,0.0001400887,0.00005670382,0.00006227756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00002166988,0.0001178051,0.0007803956,0.0005222596,0.00004956463,0.00003449108,0.0005582182,0.9821817,0.01094413,0.001820082,0.00001768941,0.002951951],"study_design_scores_gemma":[0.004431786,0.0004828611,0.9351133,0.003155881,0.0001548696,0.00000293488,0.002243411,0.01954609,0.03433866,0.0001100826,0.0001524952,0.0002676378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965436,0.00002488097,0.00192099,0.0001060675,0.000828562,0.0004367704,0.000008598717,0.00003679411,0.00009370752],"genre_scores_gemma":[0.9984424,0.00002620658,0.001361616,0.00005403685,0.00004144998,0.00003383678,0.0000165804,0.00002052909,0.000003325973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9626356,"threshold_uncertainty_score":0.4404516,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2997385668","doi":"10.1155/2020/2604012","title":"Fuel Economy in Truck Platooning: A Literature Overview and Directions for Future Research","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":149,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Platoon; Truck; Fuel efficiency; Transport engineering; Automotive engineering; Engineering; Computer science; Control (management)","retraction":null,"screen_n_in":null,"score":{"opus":0.01915191924535933,"gpt":0.2710545416742176,"spread":0.2519026224288582,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001152969,0.00006062061,0.0001344814,0.0001093008,0.00002169296,0.00002191568,0.00003910581,0.00003307925,0.000003184605],"category_scores_gemma":[0.000007503313,0.00005666456,0.00004620461,0.0001972068,0.000005953567,0.0002999364,6.845738e-7,0.0002005564,2.041984e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002295232,"about_ca_system_score_gemma":0.000009926582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.368228e-7,"about_ca_topic_score_gemma":0.00006672287,"domain_scores_codex":[0.9995193,0.00000836155,0.0002374992,0.00006901303,0.00006704094,0.00009875018],"domain_scores_gemma":[0.999764,0.0000383465,0.00003883234,0.00002924744,0.00007238864,0.00005713963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006538151,0.00006372219,0.001058571,0.002858707,0.000167455,0.00007856684,0.03082053,0.7384394,0.001949002,0.009198459,0.000660552,0.2140512],"study_design_scores_gemma":[0.005824941,0.000456434,0.3368737,0.0005690231,0.00006926373,0.000009790057,0.003702953,0.003477741,0.0001211982,0.004216936,0.6444208,0.0002572402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9105583,0.07655468,0.004870539,0.005589025,0.0008112923,0.0008896457,0.00004411502,0.00009480527,0.0005876312],"genre_scores_gemma":[0.9916332,0.005355459,0.0026737,0.00005437272,0.0002381162,0.00001615673,0.000009355091,0.00001128564,0.000008361661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7349617,"threshold_uncertainty_score":0.2310714,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1932811023","doi":"10.1002/atr.1283","title":"An agent‐based simulation model to assess the impacts of introducing a shared‐taxi system: an application to Lisbon (Portugal)","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":146,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Science Foundation","keywords":"Taxis; TRIPS architecture; Revenue; Operations research; Function (biology); Computer science; Set (abstract data type); Transport engineering; Sharing economy; Mode (computer interface); Matching (statistics); Engineering; Business","retraction":null,"screen_n_in":null,"score":{"opus":0.02014116571891006,"gpt":0.2947942593456919,"spread":0.2746530936267819,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005529579,0.0001544493,0.0002487299,0.0002938577,0.00006814164,0.00002816339,0.000191351,0.00006385187,0.000004421569],"category_scores_gemma":[0.00003716016,0.000135848,0.00007481084,0.0005455242,0.0000115149,0.0007935661,6.080184e-7,0.0001502886,0.000001442278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001070797,"about_ca_system_score_gemma":0.0000571276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001571454,"about_ca_topic_score_gemma":0.000343563,"domain_scores_codex":[0.9983362,0.0000338598,0.0009272367,0.00016946,0.0003772758,0.0001559422],"domain_scores_gemma":[0.9984294,0.00006004462,0.000328299,0.0003348215,0.0006682636,0.0001791835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007790593,0.00005730477,0.0007205756,0.0001180956,0.00001408648,4.363654e-7,0.002642398,0.8607439,0.1302762,0.0008036445,0.000006083371,0.004539348],"study_design_scores_gemma":[0.0005840216,0.0002916017,0.1822838,0.0001315609,0.00008538249,0.000001009515,0.0008349639,0.8057749,0.00951998,0.0000836955,0.000239386,0.0001697257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4873159,0.000005155368,0.5121427,0.00007678659,0.0001017409,0.0002686012,0.00002998035,0.00004936392,0.000009815129],"genre_scores_gemma":[0.9677613,0.00000219603,0.03176944,0.0001119041,0.0001047893,0.00003350298,0.0001826831,0.00003274699,0.000001420006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4804454,"threshold_uncertainty_score":0.5539719,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1577646917","doi":"10.1002/atr.192","title":"Distributions of travel time variability on urban roads","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":144,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Skew; Measure (data warehouse); Travel time; Computer science; Transport engineering; Bimodality; Distribution (mathematics); Econometrics; Mode (computer interface); Trip distribution; Statistics; Engineering; Data mining; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0160490750260619,"gpt":0.2672571627155221,"spread":0.2512080876894602,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005921311,0.00007586322,0.0001828295,0.0000965744,0.0001186046,0.000005279202,0.0001093729,0.0000731679,0.0002011002],"category_scores_gemma":[0.00009933976,0.00007260308,0.0001119153,0.0002720453,0.0001002998,0.0003765573,2.350515e-7,0.0001218035,0.000003137192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004386033,"about_ca_system_score_gemma":0.000138409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005329282,"about_ca_topic_score_gemma":0.0000630468,"domain_scores_codex":[0.9988157,0.00008527395,0.0005488573,0.00009283595,0.0003378581,0.000119473],"domain_scores_gemma":[0.9988211,0.00009529157,0.0005095751,0.00007679243,0.0004088294,0.00008843128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.003366065,0.002889754,0.1620459,0.0001403633,0.0002951613,0.0000569631,0.4403376,0.1152534,0.01251025,0.2442167,0.0008470215,0.01804088],"study_design_scores_gemma":[0.0007520075,0.0003028123,0.9889689,0.00008165986,0.00009188189,5.867355e-7,0.002464054,0.00002267685,0.00298885,0.003378936,0.0008313222,0.0001163237],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9216682,0.00002939104,0.07184111,0.0001635514,0.000395659,0.000181013,0.0001163965,0.00002818449,0.005576538],"genre_scores_gemma":[0.9895911,0.00004358506,0.01011951,0.00001373756,0.00005668988,0.000001781279,0.00006707136,0.000006359726,0.0001002283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.826923,"threshold_uncertainty_score":0.2960667,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2148714761","doi":"10.1002/atr.193","title":"Railway passenger train delay prediction via neural network model","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":140,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Overfitting; Computer science; Artificial neural network; Test set; Artificial intelligence; Machine learning; Set (abstract data type); Train; Data mining; Test data; Data set; Time delay neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.00773148274247261,"gpt":0.2049145313019972,"spread":0.1971830485595246,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000257516,0.0001342657,0.0001984605,0.00007727791,0.00004996094,0.000009483304,0.00007068355,0.00007792925,0.00001184655],"category_scores_gemma":[0.000003904219,0.0001194871,0.0001180697,0.0001670301,0.00001171256,0.0008680632,4.916564e-7,0.0001942911,0.000001897658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005009324,"about_ca_system_score_gemma":0.0000111154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001467605,"about_ca_topic_score_gemma":0.00001161667,"domain_scores_codex":[0.998835,0.00001575619,0.000562947,0.00006548053,0.0002379494,0.0002828185],"domain_scores_gemma":[0.9995376,0.00002104744,0.0001526542,0.00007895138,0.00007835688,0.0001313928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002589315,0.00002384223,0.0008602747,0.00002426344,0.00002637518,0.000004074222,0.001178435,0.9789929,0.01052312,0.0001211693,0.0001918829,0.00802781],"study_design_scores_gemma":[0.001357206,0.0001783512,0.1298737,0.0001187453,0.000120538,0.00008982274,0.0003007913,0.8616595,0.001192022,0.0003607359,0.004395214,0.0003533272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7361521,0.001023562,0.2607796,0.00001994594,0.001698657,0.0000616455,0.000006292909,0.00006075533,0.0001974536],"genre_scores_gemma":[0.9922375,0.0001062774,0.006783903,0.00001677982,0.0007744167,0.000005106332,0.00001525372,0.00003114253,0.00002961037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2560854,"threshold_uncertainty_score":0.4872544,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2745151765","doi":"10.1155/2017/8204353","title":"Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":138,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Universidad de Cuenca; CHIST-ERA; Secretaría de Educación Superior, Ciencia, Tecnología e Innovación; Agence Nationale de la Recherche","keywords":"Crowdsensing; Computer science; Particle swarm optimization; Pollution; Air pollution; Swarm behaviour; Scheme (mathematics); Real-time computing; Environmental science; Operations research; Engineering; Machine learning; Artificial intelligence; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.01388315411610441,"gpt":0.2746674659826479,"spread":0.2607843118665434,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001182447,0.00008831505,0.0001512676,0.0001353506,0.00008341476,0.00003911396,0.0001135138,0.00004177519,0.000001979534],"category_scores_gemma":[0.000009952842,0.00007803129,0.00003007149,0.0001277618,0.00001322542,0.0006532097,6.668147e-7,0.00009197277,5.055721e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000140215,"about_ca_system_score_gemma":0.00003019359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003851177,"about_ca_topic_score_gemma":0.0001742853,"domain_scores_codex":[0.9992991,0.000008260891,0.0003500998,0.00008537489,0.0001567033,0.000100472],"domain_scores_gemma":[0.9993712,0.00000876509,0.0002366653,0.0001588127,0.0001629135,0.00006168034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00009786881,0.00002486,0.01208455,0.00004374906,0.000007293452,0.00000445689,0.00009873302,0.9806065,0.005729254,0.00002144929,0.000002066744,0.001279201],"study_design_scores_gemma":[0.001001498,0.00006598116,0.9327665,0.0002262985,0.00002859763,0.000002818267,0.0001563718,0.06017767,0.005339419,0.00002905952,0.00008643005,0.0001193557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.708725,0.00003889037,0.2907841,0.00008344249,0.0001520262,0.000174086,0.000008012877,0.00001531969,0.00001911483],"genre_scores_gemma":[0.9717867,0.000009608661,0.02809021,0.00001324434,0.00006310663,0.00001063344,0.00000815876,0.00001580747,0.000002545649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.920682,"threshold_uncertainty_score":0.3182023,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3000110218","doi":"10.1155/2020/6412562","title":"Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":137,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Pixel; Deep learning; Noise (video); Process (computing); Pattern recognition (psychology); Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.007317009610048299,"gpt":0.2297082347808916,"spread":0.2223912251708433,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000444439,0.0001093284,0.0001484618,0.00006024951,0.0000515814,0.00001340547,0.0000562619,0.00005264455,0.00001075821],"category_scores_gemma":[0.00001072919,0.0001041139,0.00007447966,0.0001294552,0.00001402168,0.0003888857,9.595798e-7,0.0002034811,0.00000106443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008976213,"about_ca_system_score_gemma":0.00001409563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002261503,"about_ca_topic_score_gemma":0.00001615424,"domain_scores_codex":[0.9992459,0.000008560077,0.0003435319,0.00007315695,0.0001930759,0.0001357398],"domain_scores_gemma":[0.9995983,0.00001247869,0.0001360391,0.00003844947,0.0001372894,0.00007745536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00003949111,0.000004344614,0.0004886842,0.00003956231,0.00002811917,0.00001913947,0.0007950856,0.727276,0.2690107,0.00001009771,0.00001236652,0.002276389],"study_design_scores_gemma":[0.001893627,0.0001854239,0.5958535,0.0001394816,0.0001072755,0.00003112086,0.001173238,0.2295026,0.1698522,0.0001364262,0.0008455901,0.0002794434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9052124,0.0001712825,0.0935184,0.00003298276,0.0008425312,0.00007562508,0.000006764923,0.0001267793,0.00001324041],"genre_scores_gemma":[0.9798058,0.00003739,0.01974688,0.00003761511,0.0003394445,0.000001331607,0.00001057389,0.00001943427,0.000001540167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5953648,"threshold_uncertainty_score":0.4245644,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2897709835","doi":"10.1155/2018/9841498","title":"The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":137,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Lanzhou Jiaotong University; National Natural Science Foundation of China","keywords":"Injury prevention; Poison control; Human factors and ergonomics; Logistic regression; Occupational safety and health; Aggressive driving; Transport engineering; Medicine; Engineering; Environmental health","retraction":null,"screen_n_in":null,"score":{"opus":0.006849218787160279,"gpt":0.2735149696864164,"spread":0.2666657508992561,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001163644,0.0001543304,0.0002202957,0.00007322057,0.0002088004,0.00001952527,0.0001622015,0.00007206013,0.00002194978],"category_scores_gemma":[0.00002119755,0.0001049949,0.0002277639,0.0001362934,0.0001112547,0.0003729827,0.000002612404,0.0002199807,0.000003880588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001811441,"about_ca_system_score_gemma":0.00003772857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007103954,"about_ca_topic_score_gemma":0.00007654659,"domain_scores_codex":[0.9988754,0.0000169665,0.0005146628,0.00009217407,0.000303741,0.0001971034],"domain_scores_gemma":[0.9988647,0.00006225887,0.0006609287,0.0001293359,0.0001951116,0.00008765302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001971034,0.0003811612,0.2908547,0.00007917676,0.0005214541,0.0001744128,0.0128811,0.3537409,0.220106,0.00007209842,0.001737265,0.1174807],"study_design_scores_gemma":[0.0006979838,0.0003597283,0.9799923,0.000166978,0.00005704894,0.00001367661,0.0001012582,0.00014804,0.01807208,0.0000487614,0.0002298998,0.0001122069],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984006,0.0001284064,0.0005245462,0.00001977267,0.0007217469,0.0001057508,0.00002504514,0.000037819,0.00003633402],"genre_scores_gemma":[0.9989045,0.0002778902,0.000597413,0.00000422028,0.0001470507,0.000002470539,0.000007461689,0.00002472133,0.00003422084],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6891376,"threshold_uncertainty_score":0.4281569,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2470641485","doi":"10.1002/atr.1392","title":"Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":133,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Categorical variable; Traffic flow (computer networking); Computer science; Traffic generation model; Spatial correlation; Data mining; Intelligent transportation system; Gaussian process; Bayesian probability; Gaussian; Bayesian network; Term (time); Time series; Flow (mathematics); Artificial intelligence; Machine learning; Engineering; Real-time computing; Mathematics; Transport engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.00465174743202079,"gpt":0.2044209678535449,"spread":0.1997692204215241,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001006837,0.0001317509,0.0001586626,0.000124111,0.00004874353,0.00001040378,0.00007545081,0.00005940305,0.00002686316],"category_scores_gemma":[0.000002196065,0.00009202208,0.0000736353,0.0001366134,0.0000315683,0.0006787034,6.122808e-7,0.0001256652,0.000001653246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006137937,"about_ca_system_score_gemma":0.00001496383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.435185e-8,"about_ca_topic_score_gemma":0.00001886512,"domain_scores_codex":[0.9990644,0.00001076897,0.0004104045,0.00009678177,0.0002582138,0.0001593726],"domain_scores_gemma":[0.9996327,0.00001978449,0.00009244717,0.00008079104,0.00008215467,0.00009214797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001096399,0.00002995942,0.0009519971,0.00003023763,0.00008953775,0.00003106587,0.0001420618,0.8982515,0.001268171,0.000210917,0.002092258,0.0967927],"study_design_scores_gemma":[0.005358854,0.001453569,0.9161358,0.001371521,0.0004183648,0.000140053,0.0002475297,0.04427686,0.002824904,0.0006187428,0.02645347,0.0007002897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1403045,0.00006964395,0.8579689,0.0001336158,0.000454532,0.0001477799,0.00004845981,0.0007344648,0.0001380969],"genre_scores_gemma":[0.978043,0.0003604154,0.02111556,0.0000195005,0.0003447135,0.00001107113,0.00006265851,0.00002756562,0.00001548561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9151838,"threshold_uncertainty_score":0.3752552,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3142674662","doi":"10.1155/2021/5589075","title":"Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":132,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Swinburne University of Technology","keywords":"Computer science; Term (time); Artificial neural network; Artificial intelligence; Set (abstract data type); Predictive modelling; Traffic flow (computer networking); Data set; Machine learning; Data mining; Deep learning; Time series; Field (mathematics); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01106887866969341,"gpt":0.2291040074259991,"spread":0.2180351287563057,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009633396,0.00009075128,0.0001305287,0.0001334553,0.00005077912,0.00001608815,0.00003091679,0.00005414194,0.000005369422],"category_scores_gemma":[0.000005210245,0.00009782553,0.00008258177,0.0001206987,0.00001496157,0.0005337803,7.793855e-7,0.0001065417,1.106656e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000471475,"about_ca_system_score_gemma":0.00002373562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.279965e-7,"about_ca_topic_score_gemma":0.00001408846,"domain_scores_codex":[0.9993079,0.000007903112,0.0003372795,0.00009561866,0.0001612377,0.00009006906],"domain_scores_gemma":[0.9996354,0.00002824673,0.00005240134,0.0000444348,0.000184369,0.00005514982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005263295,0.00004583849,0.0001348038,0.0000833943,0.00009190648,0.000007650803,0.0002459831,0.9233232,0.007697821,0.001017924,0.001434725,0.06586417],"study_design_scores_gemma":[0.0068535,0.001052158,0.2942789,0.0007056353,0.0009006796,0.0005083253,0.001740018,0.5717186,0.04424878,0.006782275,0.07015014,0.00106101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4410489,0.0008588312,0.5557203,0.00008642771,0.001207816,0.000160872,0.00007080525,0.0006262345,0.0002197256],"genre_scores_gemma":[0.9824028,0.001680651,0.01559652,0.00001526235,0.0001555258,0.00001848933,0.00008457123,0.00001822912,0.00002793733],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5413539,"threshold_uncertainty_score":0.3989209,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2995723352","doi":"10.1155/2019/4603548","title":"Fully Autonomous Buses: A Literature Review and Future Research Directions","year":2019,"lang":"en","type":"review","venue":"Journal of Advanced Transportation","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":123,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Office of Research and Engagement, University of Tennessee, Knoxville; University of Tennessee, Knoxville","keywords":"Software deployment; Autonomy; Business; Risk analysis (engineering); Transport engineering; Computer security; Engineering; Computer science; Political science","retraction":null,"screen_n_in":null,"score":{"opus":0.03358686952318059,"gpt":0.3531966268559354,"spread":0.3196097573327548,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004609433,0.0003147059,0.001247716,0.0006629931,0.00008126403,0.00004642941,0.0001715313,0.0002786403,0.00004190179],"category_scores_gemma":[0.00001992237,0.0002662,0.0003505631,0.001563275,0.00004050539,0.0005285566,0.000001200248,0.001356345,0.00000855496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001271439,"about_ca_system_score_gemma":0.0002233149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.012479e-7,"about_ca_topic_score_gemma":0.00003152064,"domain_scores_codex":[0.9978194,0.00006917323,0.001330015,0.0002101781,0.0003466458,0.0002245985],"domain_scores_gemma":[0.9983693,0.0001141121,0.0003730672,0.000243927,0.0007901823,0.0001094098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005935181,0.00003937667,0.0000020929,0.09840886,0.0002670071,0.00003384742,0.0006958318,0.0003593244,0.000003563772,0.0008443843,0.0005702581,0.8987695],"study_design_scores_gemma":[0.0002105484,0.00006784977,0.0003305184,0.03315445,0.001210996,0.00008843208,0.00008963166,7.179231e-7,5.230678e-7,0.00004284587,0.9645886,0.0002149211],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001529338,0.9973975,0.0001500961,0.0001392341,0.0009949224,0.0008479321,0.0002266872,0.00007183412,0.0001565158],"genre_scores_gemma":[0.00000852234,0.997192,0.001728361,0.00003809106,0.0003367017,0.00006158089,0.000446762,0.00006445988,0.0001235017],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9640183,"threshold_uncertainty_score":0.999979,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2030339471","doi":"10.1002/atr.5670410303","title":"Factors contributing to the severity of intersection crashes","year":2007,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":123,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Alberta Motor Association Foundation for Traffic Safety","keywords":"Intersection (aeronautics); Crash; Ordered probit; Probit; Poison control; Transport engineering; Injury prevention; Probit model; Human factors and ergonomics; Road traffic; Occupational safety and health; Environmental health; Geography; Engineering; Computer science; Statistics; Medicine; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.00790048441329116,"gpt":0.2306821171824702,"spread":0.222781632769179,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002874942,0.00006093347,0.0001198685,0.00005849181,0.00003474367,0.000002701836,0.00005597662,0.00003007922,0.000005197952],"category_scores_gemma":[0.00001800968,0.00004077777,0.00007272244,0.0001298961,0.000009323686,0.0001487194,5.973089e-7,0.0001253324,3.854012e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004025762,"about_ca_system_score_gemma":0.000007216708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003366275,"about_ca_topic_score_gemma":0.0002007495,"domain_scores_codex":[0.999348,0.000005479828,0.0003720056,0.00003383261,0.0001263709,0.0001142927],"domain_scores_gemma":[0.9996433,0.00005939355,0.0001150654,0.00004047262,0.0001023673,0.00003937009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001321475,0.00001898997,0.02989674,0.0000343906,0.00005185899,0.000003959368,0.006071891,0.9195296,0.009350274,0.00007252455,0.00002706114,0.03481054],"study_design_scores_gemma":[0.0002502014,0.00006103238,0.9741609,0.00004469259,0.00002057132,0.000002513359,0.002441921,0.00007635883,0.02203973,0.00002602736,0.0008296236,0.00004640966],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8070959,0.00005421823,0.1921371,0.00003401857,0.0005703419,0.00004961899,0.000005775856,0.00001680357,0.00003627239],"genre_scores_gemma":[0.9990444,0.0000166947,0.0008473582,0.00000857922,0.00006885907,2.309939e-7,0.000003648916,0.000007029805,0.000003153382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9442642,"threshold_uncertainty_score":0.1662869,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3164841514","doi":"10.1155/2021/4037533","title":"Intelligent Traffic Management System Based on the Internet of Vehicles (IoV)","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":121,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Plan for Science, Technology and Innovation; King Abdulaziz City for Science and Technology","keywords":"Computer science; Intelligent transportation system; Advanced Traffic Management System; Intersection (aeronautics); Traffic flow (computer networking); Vehicular ad hoc network; Smart city; Traffic optimization; Traffic congestion; Traffic engineering; The Internet; Management system; Floating car data; Computer network; Wireless ad hoc network; Transport engineering; Computer security; Internet of Things; Telecommunications; Engineering; Wireless","retraction":null,"screen_n_in":null,"score":{"opus":0.007177806661803432,"gpt":0.2021677282204014,"spread":0.1949899215585979,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002066866,0.0001127441,0.0001993948,0.00008214934,0.00001601485,0.00001140041,0.0001246614,0.00003733215,0.00002321902],"category_scores_gemma":[0.000004733896,0.00008714462,0.0001507071,0.0001930098,0.00001503519,0.00008935371,0.000001193863,0.0001875506,0.000002940234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009004591,"about_ca_system_score_gemma":0.00001580133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.604617e-7,"about_ca_topic_score_gemma":0.00001281103,"domain_scores_codex":[0.998925,0.00003507966,0.0005268439,0.00008109079,0.0003077305,0.0001242785],"domain_scores_gemma":[0.9994531,0.0000721083,0.0001670406,0.00014374,0.0001185577,0.00004539948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005887993,0.0000378894,0.00004220788,0.0002481064,0.0001009837,0.0001443546,0.0003366529,0.98219,0.001163729,0.0006984202,0.00009960045,0.01487919],"study_design_scores_gemma":[0.001642691,0.0002932937,0.02219774,0.002707242,0.0003190472,0.00003975872,0.003696757,0.8973457,0.06571613,0.00008758262,0.005645953,0.0003081188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9274817,0.0006249138,0.07084664,0.0001024086,0.0005158393,0.0001404337,0.000004794266,0.00003761219,0.0002457318],"genre_scores_gemma":[0.9955649,0.000149187,0.004147636,0.00003091735,0.00004712561,0.000004584368,0.00001350026,0.0000225067,0.00001967718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0848443,"threshold_uncertainty_score":0.3553655,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1914666677","doi":"10.1002/atr.1261","title":"Design and analysis of demand‐adapted railway timetables","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":119,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Scheduling (production processes); Process (computing); Quality (philosophy); Integer programming; Service quality; Mathematical model; Public transport; Order (exchange)","retraction":null,"screen_n_in":null,"score":{"opus":0.006452185978115887,"gpt":0.2005948712036713,"spread":0.1941426852255554,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002950873,0.0000752376,0.0003058491,0.0002680815,0.00001775992,0.000005668849,0.00004725147,0.00003472436,0.000009962391],"category_scores_gemma":[0.00001055396,0.00006331562,0.00008144046,0.0003691317,0.00001343657,0.000188982,3.229208e-7,0.00005229128,2.022733e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008523697,"about_ca_system_score_gemma":0.000006296603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003187134,"about_ca_topic_score_gemma":0.00001182526,"domain_scores_codex":[0.9992616,0.00002269549,0.0004381174,0.00005420773,0.0001440877,0.00007927218],"domain_scores_gemma":[0.9995568,0.00006877631,0.0001740521,0.00006030582,0.00009535578,0.00004465314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002222603,0.000008977481,0.0005206974,0.00002798082,0.0002414611,0.000001261571,0.0004719961,0.9599304,0.03266803,0.0001831719,0.000005278748,0.005918549],"study_design_scores_gemma":[0.001776708,0.0004058402,0.4252923,0.0001650219,0.002149598,0.000008241955,0.000337051,0.5354144,0.03171876,0.0003746092,0.002030597,0.00032683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5058663,0.0004707706,0.4934903,0.000004412966,0.00007796346,0.00002283349,0.000001361008,0.000009343102,0.00005670357],"genre_scores_gemma":[0.9787837,0.0001943249,0.02097284,0.000002997271,0.0000198316,8.86424e-7,0.000005352044,0.000009023101,0.00001107077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4729173,"threshold_uncertainty_score":0.2581936,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2786157499","doi":"10.1155/2018/2702360","title":"Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":119,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Colorado State University; National Natural Science Foundation of China; Tongji University; Colorado Department of Transportation; U.S. Department of Transportation","keywords":"Transport engineering; Logistic regression; Logit; Traffic accident; Statistics; Accident (philosophy); Road surface; Traffic volume; Mixed logit; Poison control; Probability model; Environmental science; Computer science; Mathematics; Engineering; Environmental health; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.03421323561220973,"gpt":0.2436147772847223,"spread":0.2094015416725125,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001541692,0.00008097806,0.0001531755,0.00003158296,0.00006416803,0.000007865295,0.00007155551,0.00003660772,0.000001849009],"category_scores_gemma":[0.00002384244,0.00005521338,0.00004302668,0.00009548508,0.0001333819,0.000280251,0.00000194632,0.0001105718,9.725732e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002605641,"about_ca_system_score_gemma":0.00002025618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005175986,"about_ca_topic_score_gemma":0.000102566,"domain_scores_codex":[0.9992692,0.00001819585,0.0003991768,0.00006696973,0.0001519404,0.00009450502],"domain_scores_gemma":[0.9994965,0.00005515213,0.0001888694,0.00006110659,0.000155824,0.0000425169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00002831102,0.00002685326,0.02893833,0.00004520791,0.00002341645,5.470578e-7,0.004541826,0.8005484,0.1562971,0.00007586739,0.00000123847,0.009472913],"study_design_scores_gemma":[0.0005136495,0.00011719,0.7778124,0.0001226368,0.00004390878,0.000003115839,0.0007893345,0.1894001,0.0291957,0.00191674,0.000003156349,0.00008208957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9642977,0.0001392784,0.0352901,0.00004002051,0.0001154038,0.00008619303,0.000003588741,0.00001760245,0.00001018884],"genre_scores_gemma":[0.9725111,0.0000268963,0.02740139,0.000006823362,0.0000428327,7.482612e-7,7.596413e-7,0.000008441692,0.000001007691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7488741,"threshold_uncertainty_score":0.2251536,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2037634695","doi":"10.1002/atr.5670410107","title":"Accident severity analysis using ordered probit model","year":2007,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":118,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Ordered probit; Crash; Probit; Pedestrian; Probit model; Poison control; Vehicle type; Estimation; Injury prevention; Accident (philosophy); Transport engineering; Accident analysis; Statistics; Computer science; Engineering; Environmental health; Medicine; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.00982743776368159,"gpt":0.2486859778708103,"spread":0.2388585401071287,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001963484,0.00009554455,0.0002141503,0.000214649,0.00003608931,0.000006686343,0.00006662875,0.00005429203,0.000009631074],"category_scores_gemma":[0.000003364627,0.00008877005,0.0001680461,0.0004306872,0.000009325111,0.0003625941,5.456638e-7,0.0001562762,4.991064e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007944457,"about_ca_system_score_gemma":0.00002430241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002025946,"about_ca_topic_score_gemma":0.0001854428,"domain_scores_codex":[0.9990773,0.000004380626,0.0005032148,0.00006634773,0.0002036404,0.0001451246],"domain_scores_gemma":[0.9995738,0.00001239917,0.0001465937,0.00006803328,0.0001255611,0.00007357927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00006787816,0.00002120503,0.00353397,0.00001697038,0.0002003273,0.00001824988,0.0009890781,0.9856825,0.004925687,0.00002801099,0.000004953276,0.004511156],"study_design_scores_gemma":[0.0009761376,0.00003707287,0.7193392,0.00004244281,0.0008213321,0.00001197845,0.0005645649,0.2739088,0.003683971,0.0002548497,0.0001338839,0.0002257824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.553877,0.00009057834,0.4458181,0.000005558836,0.0001194331,0.00003393126,0.000002439101,0.00002434533,0.00002861942],"genre_scores_gemma":[0.9375513,0.00008026467,0.06228168,0.000008545414,0.00004501018,2.965467e-7,0.000009719429,0.00001335838,0.000009786462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7158052,"threshold_uncertainty_score":0.3619938,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2616717520","doi":"10.1155/2017/5202150","title":"Surrogate Safety Analysis of Pedestrian-Vehicle Conflict at Intersections Using Unmanned Aerial Vehicle Videos","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":118,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Intersection (aeronautics); Pedestrian; Schema crosswalk; Computer science; Warning system; Transport engineering; Trajectory; Real-time computing; Artificial intelligence; Computer security; Engineering; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.01765234857349429,"gpt":0.2667794703091317,"spread":0.2491271217356374,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001781745,0.0001279542,0.0004008331,0.0002230181,0.0002118173,0.00001810378,0.0001588338,0.00007547728,0.00005629252],"category_scores_gemma":[0.00002023674,0.0001236083,0.0003140546,0.000209694,0.00005888403,0.0005355898,0.000003235834,0.0001561317,0.000001157523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001184418,"about_ca_system_score_gemma":0.00003081105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006449479,"about_ca_topic_score_gemma":0.001388894,"domain_scores_codex":[0.9988032,0.00001895662,0.0007043623,0.0001005416,0.0002057002,0.0001672659],"domain_scores_gemma":[0.9989663,0.00004521038,0.0005258615,0.0001997216,0.0001703031,0.00009255426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000486998,0.00002918735,0.01384827,0.00003304001,0.0006412683,0.00001429087,0.001350635,0.9166268,0.06493647,0.0000230719,0.000006360706,0.002003578],"study_design_scores_gemma":[0.002595682,0.0001054024,0.9475662,0.00009342277,0.001340903,0.000005329566,0.0004927979,0.03400102,0.01269116,0.00002122128,0.0009056179,0.0001812284],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894602,0.0001188002,0.009314066,0.00004084608,0.0007832939,0.0000806372,0.00008148958,0.00003102212,0.00008969309],"genre_scores_gemma":[0.9984224,0.0002635959,0.001145718,0.000004347306,0.00009935837,7.648996e-7,0.00002727107,0.00001893594,0.00001756577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9337179,"threshold_uncertainty_score":0.50406,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2804442660","doi":"10.1155/2018/8969353","title":"Shared Autonomous Vehicles Effect on Vehicle-Km Traveled and Average Trip Duration","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":117,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Seventh Framework Programme; Technische Universität München; Deutsche Forschungsgemeinschaft; European Commission","keywords":"Metropolitan area; TRIPS architecture; Duration (music); Transport engineering; Occupancy; Public transport; Vehicle miles of travel; Population; Computer science; Geography; Engineering; Civil engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.006788451526456153,"gpt":0.230344952786855,"spread":0.2235565012603988,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001948906,0.0001491148,0.000217562,0.0001965452,0.00007943956,0.00002598754,0.00006156345,0.00007641209,0.0000350632],"category_scores_gemma":[0.000020536,0.0001405963,0.00007180285,0.0002374879,0.00004350235,0.0004808897,3.214917e-7,0.0001917861,0.000005829755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005008784,"about_ca_system_score_gemma":0.00002456311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001778917,"about_ca_topic_score_gemma":0.00005487592,"domain_scores_codex":[0.9988994,0.00001923151,0.0006283642,0.0001188424,0.0002049264,0.0001292401],"domain_scores_gemma":[0.9993894,0.00006984197,0.0001685134,0.0000933299,0.0002032548,0.00007564857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001578065,0.000205153,0.007253644,0.0003484031,0.0002808208,0.0000641595,0.00984372,0.1973067,0.6397771,0.003893048,0.0002251614,0.1392241],"study_design_scores_gemma":[0.002943119,0.00118145,0.9374796,0.0001111549,0.00007587762,0.000008711925,0.000144119,0.001731419,0.05435287,0.0003691054,0.001397048,0.0002055893],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930776,0.00006043316,0.005843334,0.0001147232,0.0004224769,0.0002094958,0.00003987204,0.00008756376,0.0001445005],"genre_scores_gemma":[0.9979655,0.00004060187,0.001672172,0.00006424791,0.0001331768,0.000009201356,0.00007483728,0.00002331525,0.00001695027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9302259,"threshold_uncertainty_score":0.5733349,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1798589081","doi":"10.1002/atr.179","title":"Relationships among service quality, corporate image, customer satisfaction, and behavioral intention for the elderly in high speed rail services","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Customer Service Quality and Loyalty","field":"Business, Management and Accounting","cited_by":116,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Science Council","keywords":"Service quality; Structural equation modeling; Service (business); Quality (philosophy); Business; Customer satisfaction; Marketing; Empirical research; Empirical evidence; Quality of service; Human factors and ergonomics; Transport engineering; Poison control; Engineering; Computer science; Medicine; Environmental health; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.07791081678750995,"gpt":0.2951740166122142,"spread":0.2172631998247043,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009673365,0.00014347,0.0002268442,0.000244266,0.0001874447,0.00009014072,0.0001203517,0.00008105228,0.00004789947],"category_scores_gemma":[0.00001715719,0.0001165111,0.00007870264,0.0004850157,0.00004290107,0.00345193,0.000005512147,0.0002755193,0.00000747729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002441605,"about_ca_system_score_gemma":0.00001587272,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.00779053,"about_ca_topic_score_gemma":0.06488664,"domain_scores_codex":[0.9986455,0.00003410447,0.0007831041,0.0001517896,0.0002346478,0.000150879],"domain_scores_gemma":[0.9976984,0.0000847004,0.001349702,0.0001123727,0.0007350927,0.00001977431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008103794,0.0001349512,0.9808043,0.0005882314,0.00004145342,0.000006567887,0.003708554,0.001387957,0.001193593,0.0044593,0.00001585941,0.006848905],"study_design_scores_gemma":[0.001606931,0.0000408852,0.9829569,0.0001186991,0.0001897816,0.000001647373,0.007407394,0.0001552296,0.00004782034,0.007148598,0.0001843391,0.0001417636],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975442,0.00006018393,0.001039833,0.000539335,0.0004068166,0.0003331543,0.000008443288,0.00002177056,0.00004622425],"genre_scores_gemma":[0.9979602,0.00003423778,0.001504566,0.0002477831,0.0001494842,0.00001075162,0.00005811371,0.00001871078,0.00001619664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05709612,"threshold_uncertainty_score":0.9988167,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3000657974","doi":"10.1155/2020/7194342","title":"Video-Based Detection Infrastructure Enhancement for Automated Ship Recognition and Behavior Analysis","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":114,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Computer science; Intersection (aeronautics); Convolutional neural network; Real-time computing; Bounding overwatch; Object detection; Bridge (graph theory); Minimum bounding box; Artificial intelligence; Data mining; Engineering; Pattern recognition (psychology); Transport engineering; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01123889528674413,"gpt":0.2404728558386858,"spread":0.2292339605519417,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007134941,0.00008420179,0.0001673169,0.0001147987,0.00003253086,0.00001321106,0.00002732701,0.00005437697,0.00004519362],"category_scores_gemma":[0.00001592618,0.00008580471,0.00009817581,0.0002934195,0.000007947458,0.0002245548,2.718558e-7,0.0001010834,5.302759e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003465548,"about_ca_system_score_gemma":0.00001120945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.741404e-7,"about_ca_topic_score_gemma":0.0000165368,"domain_scores_codex":[0.9993355,0.00001013029,0.0003793701,0.00007652672,0.0001254878,0.00007300118],"domain_scores_gemma":[0.9995658,0.00002957424,0.00013814,0.00002990441,0.0001589142,0.00007769465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003097855,0.00002367541,0.001236977,0.000198035,0.0001687106,0.000004692249,0.0007772811,0.7411135,0.14774,0.000002622971,0.00002191963,0.1084029],"study_design_scores_gemma":[0.003793721,0.0005530423,0.5554371,0.00006670078,0.002218786,0.000003057409,0.0002918498,0.2810802,0.1552966,0.000213886,0.0007391536,0.0003059547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6902415,0.00004105977,0.3092947,0.00006438901,0.00009228582,0.0001445946,0.00004149478,0.00007518948,0.000004816776],"genre_scores_gemma":[0.9808875,0.00003639644,0.01872767,0.00007228195,0.00004728657,0.00001700416,0.0002001176,0.00001098659,7.365122e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5542002,"threshold_uncertainty_score":0.3499015,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2904042868","doi":"10.1155/2018/3869106","title":"An Improved Deep Learning Model for Traffic Crash Prediction","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":113,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Natural Science Foundation of China; Tennessee Department of Transportation; Research and Innovative Technology Administration; U.S. Department of Transportation","keywords":"Computer science; Artificial intelligence; Machine learning; Feature (linguistics); Deep learning; Crash; Autoencoder; Feature learning; Data mining; Supervised learning; Curse of dimensionality; Random forest; Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.00612544029823836,"gpt":0.2260695478117455,"spread":0.2199441075135071,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001185521,0.00009542645,0.0001374967,0.0000775589,0.00007784324,0.000009275454,0.00006504409,0.00006791414,0.00000565449],"category_scores_gemma":[0.000006170357,0.00008993585,0.00008338906,0.00007575558,0.00001908815,0.0005444686,2.077199e-7,0.0001548762,6.888612e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003677255,"about_ca_system_score_gemma":0.0000177295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.342051e-7,"about_ca_topic_score_gemma":0.00003918502,"domain_scores_codex":[0.9992948,0.000006466406,0.0003740467,0.00008022923,0.0001081282,0.0001363652],"domain_scores_gemma":[0.9995549,0.00001592311,0.0001145669,0.00005490234,0.0001860163,0.00007374761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001288461,0.00002255689,0.0001091201,0.00002864142,0.00002240785,6.754564e-7,0.002458591,0.9169644,0.01651961,0.00001833512,0.000009630226,0.0637172],"study_design_scores_gemma":[0.001119412,0.0004993413,0.02937686,0.0000259386,0.00005396597,0.000003631627,0.0003694643,0.9671513,0.0008873021,0.00009205416,0.0003271019,0.00009355744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5171393,0.00004958538,0.4823218,0.000007338874,0.0003137101,0.00007198705,0.000007007797,0.00007707696,0.00001214827],"genre_scores_gemma":[0.9631528,0.0001004579,0.03633852,0.000007268573,0.0003170173,0.000004973952,0.00003459949,0.00002874532,0.00001557683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4460135,"threshold_uncertainty_score":0.3667478,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2084583462","doi":"10.1002/atr.188","title":"Random regret minimization or random utility maximization: an exploratory analysis in the context of automobile fuel choice","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":112,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Australian Research Council","keywords":"Regret; Context (archaeology); Minification; Utility maximization; Maximization; Computer science; Exploratory analysis; Mathematical optimization; Operations research; Econometrics; Economics; Engineering; Mathematics; Mathematical economics; Machine learning; Data science","retraction":null,"screen_n_in":null,"score":{"opus":0.08020964197159035,"gpt":0.2427933876032794,"spread":0.162583745631689,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001071043,0.0001088405,0.0004735868,0.0002959211,0.0000366949,0.00001068104,0.0001666573,0.0000672659,0.0005411856],"category_scores_gemma":[0.00006730652,0.00009058614,0.0002036931,0.0004217353,0.00005364935,0.001059209,0.000001616356,0.0001059617,0.000004077669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005616014,"about_ca_system_score_gemma":0.00002324965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005299209,"about_ca_topic_score_gemma":0.0008242989,"domain_scores_codex":[0.9982779,0.00007589216,0.001301609,0.0001718547,0.00007156334,0.0001011763],"domain_scores_gemma":[0.9982633,0.0001021124,0.001345361,0.0001896576,0.00005920335,0.00004039578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002562399,0.0005568697,0.8791469,0.00005430888,0.0003051869,0.000006595943,0.0419515,0.07092863,0.00004073361,0.001375421,0.0000207764,0.003050666],"study_design_scores_gemma":[0.006156862,0.0002100937,0.9827854,0.00001485499,0.0001558126,0.000001338725,0.005485341,0.002976433,0.0001160951,0.001737714,0.0002485717,0.0001114957],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9632726,0.0005490982,0.03540519,0.00004853067,0.0001632985,0.0002598383,0.00005708473,0.000005094803,0.0002392441],"genre_scores_gemma":[0.9964114,0.0005718232,0.002760297,0.00007848363,0.00003173442,0.00001583393,0.0001034354,0.00001009166,0.00001692306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1036385,"threshold_uncertainty_score":0.5925603,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2912665531","doi":"10.1155/2019/9085238","title":"Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":111,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Support vector machine; Collision; Computer science; Trajectory; Artificial intelligence; Collision avoidance; Feature extraction; Pattern recognition (psychology); Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.01431423077681252,"gpt":0.2348436977236492,"spread":0.2205294669468367,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002373254,0.00009068667,0.0001830735,0.0001235,0.00003070867,0.000009948943,0.00006447837,0.00004672518,0.00001638005],"category_scores_gemma":[0.00001047055,0.00009184001,0.00004024919,0.0001227974,0.000006409037,0.0007853274,0.000002098011,0.000290148,0.00000105649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004245227,"about_ca_system_score_gemma":0.00001550811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007579334,"about_ca_topic_score_gemma":0.00009868391,"domain_scores_codex":[0.9992675,0.00002240434,0.0003521425,0.0001036043,0.0001342725,0.00012008],"domain_scores_gemma":[0.9997215,0.00004647625,0.0001003739,0.00006755391,0.00002310499,0.00004098061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00006442388,0.000021248,0.1006674,0.0001133449,0.00004034416,0.00003954488,0.001945972,0.725219,0.1071769,0.000001866394,9.847197e-7,0.06470907],"study_design_scores_gemma":[0.003858926,0.0001515529,0.8222438,0.0007114336,0.0001177968,0.00002584899,0.002435033,0.166807,0.002583147,0.0000264645,0.0006853809,0.0003536311],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958124,0.001492396,0.002180391,0.000007438707,0.0003638636,0.00007623711,0.00000814361,0.0000310243,0.00002810439],"genre_scores_gemma":[0.9923756,0.0006289402,0.006867314,0.00000371588,0.00004293317,3.353446e-7,0.00005488965,0.00002172417,0.000004579547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7215764,"threshold_uncertainty_score":0.3745127,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2785435926","doi":"10.1155/2018/8061514","title":"Vehicle Remote Health Monitoring and Prognostic Maintenance System","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":111,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Automotive industry; Support vector machine; Computer science; Random forest; Fault tree analysis; Decision tree; Fault (geology); Machine learning; Fault detection and isolation; Artificial intelligence; Algorithm; Reliability engineering; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.006348595682481415,"gpt":0.2331274597367833,"spread":0.2267788640543018,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001433339,0.00007046069,0.0001597346,0.00006136882,0.0000494841,0.00001296747,0.00003553584,0.00002626379,8.128053e-7],"category_scores_gemma":[0.00000820319,0.00006368273,0.00003250302,0.0000964414,0.0000139887,0.0001965249,3.330323e-7,0.00009663778,0.000002098619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007569898,"about_ca_system_score_gemma":0.00001362641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007486828,"about_ca_topic_score_gemma":0.00001843985,"domain_scores_codex":[0.9993138,0.00001319515,0.0003681687,0.00005838253,0.0001270223,0.0001194145],"domain_scores_gemma":[0.9996061,0.00001633834,0.0001438519,0.00004844731,0.000108545,0.0000767297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006441906,0.00004516845,0.01495237,0.002551036,0.0002783734,0.0001297718,0.01202944,0.1897069,0.2008766,0.0005247721,0.0001853423,0.578076],"study_design_scores_gemma":[0.006555122,0.001977618,0.8769588,0.004974709,0.0000995369,0.0003777228,0.01061928,0.0686681,0.01897843,0.0002153666,0.009991604,0.0005836931],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9760978,0.0009233419,0.02128474,0.00006342957,0.001366603,0.0001163698,0.000002155158,0.00007833096,0.00006721234],"genre_scores_gemma":[0.9973515,0.0001179597,0.002184374,0.000006631376,0.0003121767,0.000001255817,4.631659e-7,0.00001393867,0.00001170902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8620064,"threshold_uncertainty_score":0.2596906,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4214700196","doi":"10.1155/2022/3825532","title":"Traffic Sign Detection via Improved Sparse R-CNN for Autonomous Vehicles","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Beijing Union University; National Natural Science Foundation of China","keywords":"Traffic sign; Computer science; Robustness (evolution); Artificial intelligence; Block (permutation group theory); Pyramid (geometry); Focus (optics); Traffic sign recognition; Sign (mathematics); Feature extraction; Computer vision; Data mining; Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.01172939146582379,"gpt":0.243861178121797,"spread":0.2321317866559732,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002381837,0.0001321989,0.0001998098,0.0001557014,0.0003372658,0.00002420849,0.000438474,0.00003088571,0.000004656868],"category_scores_gemma":[0.00001132546,0.000139611,0.0001651153,0.0004710329,0.00002074086,0.0008502715,0.000007831547,0.0002597044,9.779781e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001465219,"about_ca_system_score_gemma":0.00007170998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001052854,"about_ca_topic_score_gemma":0.00001773571,"domain_scores_codex":[0.9986566,0.00003861346,0.0005881973,0.0002411142,0.0002620819,0.0002134184],"domain_scores_gemma":[0.9986811,0.0001404182,0.0006902639,0.0002080668,0.0001938159,0.00008632258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001250615,0.00007304587,0.000007551746,0.000007899027,0.00001260441,0.00000524917,0.0004461834,0.6133109,0.08014461,0.0004684133,0.00001223339,0.3053862],"study_design_scores_gemma":[0.01260424,0.008122321,0.04402818,0.00006094102,0.0003150844,0.0004441141,0.001386494,0.6580105,0.1418871,0.05894713,0.07246715,0.001726722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2358803,0.0001301723,0.7626677,0.0003823439,0.0004944156,0.0003642979,0.000008191667,0.00006885309,0.000003768279],"genre_scores_gemma":[0.8816993,0.00002165918,0.1178795,0.0001126926,0.0001057406,0.0001262479,0.00001082441,0.00001789766,0.00002605504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.645819,"threshold_uncertainty_score":0.5693172,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3082854818","doi":"10.1155/2020/7843743","title":"Road Markings and Their Impact on Driver Behaviour and Road Safety: A Systematic Review of Current Findings","year":2020,"lang":"en","type":"review","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":110,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Transport engineering; Road surface; Road traffic; Plan (archaeology); Poison control; Engineering; Geography; Civil engineering; Environmental health; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.01257301855721051,"gpt":0.2809094699969738,"spread":0.2683364514397633,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002551588,0.000430587,0.002662726,0.0001745833,0.00002978904,0.00001062535,0.0001244845,0.0001164177,0.000008099188],"category_scores_gemma":[0.00002599128,0.0002618294,0.0005069393,0.0002114712,0.00002661874,0.0001933895,0.000002740459,0.000520143,9.86084e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001077255,"about_ca_system_score_gemma":0.00006817355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.83946e-7,"about_ca_topic_score_gemma":0.000001376765,"domain_scores_codex":[0.9977742,0.00005937517,0.001606837,0.0001631389,0.0002485912,0.0001478409],"domain_scores_gemma":[0.9986776,0.00008840733,0.0008847434,0.0001212728,0.00008888732,0.0001390834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00002054215,0.00001635064,0.00001076668,0.6368217,0.0002284947,0.00001431263,0.000445842,0.0003117631,0.000001625932,0.000009022416,0.00001433007,0.3621053],"study_design_scores_gemma":[0.0004882283,0.0002428399,0.004422459,0.9774071,0.003143729,0.0001052622,0.00006092687,0.00002721043,0.000001895485,0.000006382786,0.01376955,0.0003244306],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001299947,0.9971353,0.0003443833,0.00001049031,0.0002426001,0.0007971785,0.0001382485,0.00002380265,0.000007982156],"genre_scores_gemma":[0.009060269,0.9906328,0.0001329005,0.000007315698,0.00004687627,0.00001115056,0.00005730441,0.00004934279,0.000002008831],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.3617808,"threshold_uncertainty_score":0.9999834,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2046533071","doi":"10.1002/atr.5670360106","title":"Multiobjective bilevel optimization for transportation planning and management problems","year":2002,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":109,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Bilevel optimization; Mathematical optimization; Multi-objective optimization; Pareto principle; Pareto optimal; Computer science; Genetic algorithm; Optimization problem; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02592375696837062,"gpt":0.288531866100729,"spread":0.2626081091323583,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003215487,0.0001224642,0.000186313,0.0002289123,0.000308414,0.00003823653,0.00007455768,0.00008640438,0.00003655031],"category_scores_gemma":[0.00002563042,0.0001271316,0.00008034329,0.0002782142,0.00006204849,0.0009225318,2.293546e-7,0.00009483656,4.409279e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005211353,"about_ca_system_score_gemma":0.00002159318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001407529,"about_ca_topic_score_gemma":0.0001340041,"domain_scores_codex":[0.9987136,0.00003626501,0.0005613752,0.0001696179,0.0003363619,0.00018284],"domain_scores_gemma":[0.9988388,0.00009452005,0.0005129252,0.0000491955,0.0004066028,0.00009792023],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001183229,0.00006742373,0.002999966,0.00009917946,0.00004512248,0.000006931113,0.04373552,0.9398991,0.00005524436,0.001997766,0.00006713672,0.01090826],"study_design_scores_gemma":[0.02118923,0.001672515,0.7897793,0.002178598,0.001473192,0.00001005856,0.06863354,0.06125533,0.0005714122,0.005339973,0.04618178,0.001715086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1367847,0.0008673386,0.8595464,0.0005807543,0.0005652359,0.001084773,0.00007873921,0.00008127018,0.0004107547],"genre_scores_gemma":[0.8286498,0.001540044,0.1693066,0.00005087392,0.0001007829,0.00003896677,0.0001454605,0.00002078135,0.0001466284],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8786438,"threshold_uncertainty_score":0.5184278,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1992341494","doi":"10.1002/atr.136","title":"Hybrid model for prediction of bus arrival times at next station","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":108,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Arrival time; Kalman filter; Support vector machine; Time of arrival; Artificial neural network; Baseline (sea); Computer science; Real-time computing; Travel time; Direction of arrival; Data mining; Simulation; Transport engineering; Engineering; Artificial intelligence; Telecommunications","retraction":null,"screen_n_in":null,"score":{"opus":0.00869408554000613,"gpt":0.2214554500788737,"spread":0.2127613645388675,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001099545,0.00007651466,0.0001247624,0.0001322686,0.00002545394,0.000005799032,0.00005743671,0.00003711764,0.000007911275],"category_scores_gemma":[0.000009359911,0.00007734078,0.00008124421,0.0000472045,0.00001514059,0.000532695,7.005925e-7,0.0001085259,3.261062e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002876167,"about_ca_system_score_gemma":0.00001227368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.259597e-7,"about_ca_topic_score_gemma":0.00001696879,"domain_scores_codex":[0.9992918,0.000003002363,0.0004118191,0.00005872003,0.0001554422,0.00007918747],"domain_scores_gemma":[0.9995558,0.00001802173,0.0001777223,0.00006432132,0.0001449371,0.00003918476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001001154,0.00002720879,0.00008518255,0.00009313864,0.00003575457,0.000001038761,0.0004172514,0.7874347,0.1902944,0.0007998009,0.001936432,0.01877496],"study_design_scores_gemma":[0.001980871,0.0002761096,0.02303517,0.00007384982,0.0001577576,0.000009043517,0.0001292707,0.8613366,0.1056956,0.002625542,0.004528778,0.0001514055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4422626,0.00002753226,0.5567735,0.00001968639,0.0004290877,0.0001346528,0.0001049958,0.0001862393,0.00006172409],"genre_scores_gemma":[0.9558943,0.000178071,0.04369787,0.000007711506,0.00006240417,0.00001077745,0.0001027203,0.00001662699,0.0000294968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5136317,"threshold_uncertainty_score":0.3153866,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2061649002","doi":"10.1002/atr.106","title":"Model of personal attitudes towards transit service quality","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":108,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true},"ca_institutions":"University of Calgary; University of Toronto","funders":"","keywords":"Multinomial logistic regression; Transit (satellite); Reliability (semiconductor); Latent variable; Service quality; Transport engineering; Perception; Service (business); Public transport; Quality (philosophy); Business; Computer science; Marketing; Psychology; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.04480772207004855,"gpt":0.3582700714044495,"spread":0.3134623493344009,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008611435,0.0001027183,0.000290299,0.00007268352,0.0001265043,0.00001405107,0.0002348859,0.0001106603,0.0001698078],"category_scores_gemma":[0.00003276612,0.00009362844,0.0002045654,0.0002534084,0.0001469732,0.001035059,6.426716e-7,0.0003306688,6.351618e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002467524,"about_ca_system_score_gemma":0.0004167199,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000522662,"about_ca_topic_score_gemma":0.02073332,"domain_scores_codex":[0.9982968,0.00003997754,0.0007087807,0.0001295069,0.0006571356,0.0001678278],"domain_scores_gemma":[0.9984668,0.00004698322,0.0005170638,0.00009033396,0.0007494581,0.000129383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001047526,0.0007688949,0.5081431,0.0004484307,0.0001138108,0.00001060886,0.1304707,0.02087105,0.3109826,0.005250657,0.00001253567,0.0218801],"study_design_scores_gemma":[0.0007166137,0.00004295153,0.9911116,0.00003840968,0.00006491136,2.595511e-7,0.00226196,0.00008298483,0.003060952,0.002314819,0.000184632,0.0001198593],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938692,0.0000522617,0.004242551,0.0008867455,0.0003475813,0.0001051951,0.00004389373,0.0000152431,0.0004372625],"genre_scores_gemma":[0.9906547,0.00004333539,0.009028522,0.00007516001,0.0001394642,0.000001438499,0.00001072948,0.000008361269,0.0000382946],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4829686,"threshold_uncertainty_score":0.9971358,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1999342954","doi":"10.1002/atr.5670430305","title":"Cellular automata model for heterogeneous traffic","year":2009,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":108,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Cellular automaton; Traffic flow (computer networking); Computer science; Microscopic traffic flow model; Traffic generation model; Simple (philosophy); Traffic model; Field (mathematics); Traffic simulation; Occupancy; Road traffic; Simulation; Distributed computing; Transport engineering; Engineering; Artificial intelligence; Microsimulation; Real-time computing; Mathematics; Computer network; Civil engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.006969614517772311,"gpt":0.2090080056996205,"spread":0.2020383911818482,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005868076,0.00008751937,0.0001483038,0.0000715624,0.00001999253,0.000008620073,0.00007258922,0.00002828806,0.000002200969],"category_scores_gemma":[0.000001969102,0.00008344036,0.0001197588,0.00004658122,0.000003712635,0.0001805454,1.758619e-7,0.00005873318,5.535483e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002605899,"about_ca_system_score_gemma":0.000008691277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.229474e-8,"about_ca_topic_score_gemma":0.0000063254,"domain_scores_codex":[0.9993801,0.000002181881,0.0003222594,0.00005845285,0.0001171334,0.000119839],"domain_scores_gemma":[0.9997525,0.000009716512,0.00007483336,0.00006371031,0.00005147305,0.00004780735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006055016,0.00002873032,2.974031e-7,0.00002683617,0.00002357537,0.00001050086,0.0003370222,0.8777837,0.008786893,0.0001094096,0.00007061985,0.1127619],"study_design_scores_gemma":[0.002519576,0.0002948297,0.002215224,0.00003921742,0.0001154845,0.000004949386,0.00005188294,0.989655,0.001351166,0.0007567283,0.002833976,0.0001620311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6174004,0.0004580115,0.3814754,0.0001178038,0.0002545874,0.0001612418,0.000009103595,0.0000985406,0.00002486163],"genre_scores_gemma":[0.9847972,0.0001215934,0.01493275,0.00003632174,0.00006224703,0.000003747797,0.00001221522,0.00001252093,0.00002142136],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3673968,"threshold_uncertainty_score":0.3402599,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2097223009","doi":"10.1002/atr.172","title":"Stochastic modeling of the equilibrium speed–density relationship","year":2011,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"University of Massachusetts Amherst","keywords":"Probabilistic logic; Representation (politics); Diagram; Traffic flow (computer networking); Computer science; Flow (mathematics); Mathematical model; Stochastic modelling; Variety (cybernetics); Work (physics); Stochastic process; Statistical physics; Mathematical economics; Mathematical optimization; Industrial engineering; Operations research; Mathematics; Engineering; Artificial intelligence; Physics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.02056750609937821,"gpt":0.2084869895956922,"spread":0.187919483496314,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009186783,0.00005800102,0.0001091364,0.00005148856,0.00001571072,0.00000216677,0.00008036679,0.00002207034,0.000007074798],"category_scores_gemma":[0.00001184669,0.00004439545,0.00008581243,0.00008992014,0.00001066711,0.0001946154,0.00000109692,0.000111942,6.139889e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001753754,"about_ca_system_score_gemma":0.00001007317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002732017,"about_ca_topic_score_gemma":0.00002725458,"domain_scores_codex":[0.9994103,0.000006954618,0.0003341168,0.00003788263,0.0001409329,0.00006983027],"domain_scores_gemma":[0.9996848,0.00001742043,0.000114506,0.00007790157,0.0000803677,0.00002504223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00004262009,0.00001359536,0.0003484086,0.00002838942,0.00002330088,0.00000166268,0.001279161,0.994421,0.002275797,0.0006080598,0.000004244894,0.0009537279],"study_design_scores_gemma":[0.00147938,0.0000949509,0.8429136,0.0001838984,0.0002548085,0.000006390604,0.0007356186,0.1476922,0.0009647357,0.005487402,0.00002776853,0.0001593009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8814533,0.0001186178,0.117683,0.00002252005,0.0004321432,0.00007579428,0.000001796632,0.00001896683,0.0001938768],"genre_scores_gemma":[0.9980393,0.000007193472,0.001896325,0.000004844431,0.00003134798,4.834614e-7,9.67045e-7,0.00000853307,0.00001102637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8467289,"threshold_uncertainty_score":0.1810394,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2109648075","doi":"10.1002/atr.104","title":"Severity of urban transit bus crashes in Bangladesh","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Accident Research Centre, Monash University; Alberta Motor Association Foundation for Traffic Safety; Centre for Transportation Engineering and Planning","keywords":"Crash; Transport engineering; Pedestrian; Transit (satellite); Probit model; Ordered probit; Probit; Collision; Poison control; Developing country; Business; Computer science; Geography; Public transport; Engineering; Environmental health; Computer security; Economic growth; Medicine; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.003742251509414496,"gpt":0.1993809292278705,"spread":0.1956386777184561,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001037791,0.00008216301,0.000196609,0.0001008686,0.00001102083,0.000002466505,0.00007450022,0.00007155223,0.00002744385],"category_scores_gemma":[0.000004914446,0.00007654645,0.00008210758,0.000146306,0.00002229707,0.0002822503,2.334316e-7,0.0003153851,6.007713e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001352212,"about_ca_system_score_gemma":0.00001994693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003685548,"about_ca_topic_score_gemma":0.0004362064,"domain_scores_codex":[0.9992464,0.000005986342,0.0004534668,0.0000515429,0.0001423103,0.0001002716],"domain_scores_gemma":[0.9997081,0.00002438687,0.00009503986,0.00006097736,0.00006496652,0.00004655869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000174347,0.00009852708,0.03760285,0.0001690742,0.00004000299,0.00004712128,0.005085189,0.8147828,0.1183701,0.0002402746,0.00005105531,0.02333866],"study_design_scores_gemma":[0.0009589269,0.00004934995,0.9868606,0.00005721141,0.00002227806,0.000008204323,0.0002229718,0.0003126882,0.01012078,0.0001421872,0.001154067,0.00009074188],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947975,0.0001610701,0.004259238,0.00003820704,0.0005267385,0.00005404352,0.00001121553,0.00002235137,0.0001296612],"genre_scores_gemma":[0.9944572,0.0001222845,0.005326419,0.000004975504,0.00006319577,8.168833e-7,0.000006576897,0.00001300101,0.000005572439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9492577,"threshold_uncertainty_score":0.3121473,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2772577757","doi":"10.1155/2017/2525481","title":"A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Causation; Bayesian network; Bayesian probability; Relation (database); Computer science; Bayes' theorem; Transport engineering; Operations research; Engineering; Data mining; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.01271200708215329,"gpt":0.2596745221520529,"spread":0.2469625150698997,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001435774,0.00009184921,0.00029435,0.0001848956,0.00008572608,0.00001768685,0.0001445099,0.000050524,0.000006917978],"category_scores_gemma":[0.00001072256,0.00008714905,0.0001513529,0.0002615326,0.00001423349,0.000353266,0.000001417094,0.00009822099,2.814929e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003902938,"about_ca_system_score_gemma":0.00001604114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001342635,"about_ca_topic_score_gemma":0.00009122908,"domain_scores_codex":[0.9990803,0.000009832514,0.0004919567,0.00007685032,0.0002144197,0.0001266555],"domain_scores_gemma":[0.9992959,0.00001029666,0.0003358203,0.0001646544,0.0001147255,0.00007857364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00005533934,0.00002038416,0.01337986,0.00001783843,0.0003365982,0.000002794887,0.0009338205,0.9734513,0.00139276,0.00004303749,0.000008038857,0.01035826],"study_design_scores_gemma":[0.0003530281,0.00003017487,0.9245558,0.00005007618,0.0005493443,0.000001427216,0.0001350655,0.0739558,0.0001935668,0.0000412146,0.00004997392,0.00008453823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6647783,0.00007694092,0.3346817,0.00001152197,0.0002194458,0.0000666687,0.000004554502,0.00001037862,0.000150532],"genre_scores_gemma":[0.9564272,0.00007669396,0.04336527,0.000005625688,0.00009394684,0.000001034783,0.00001354159,0.00001298133,0.000003720881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9111759,"threshold_uncertainty_score":0.3553835,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2909143934","doi":"10.1155/2019/4109148","title":"A Machine Learning Method for Predicting Driving Range of Battery Electric Vehicles","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Gradient boosting; Decision tree; Boosting (machine learning); Residual; Range (aeronautics); Linear regression; Mean squared prediction error; Regression; Computer science; Machine learning; Regression analysis; Battery (electricity); Electric vehicle; Support vector machine; Artificial intelligence; Predictive modelling; Random forest; Engineering; Statistics; Algorithm; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.009117288104004197,"gpt":0.2744208671625764,"spread":0.2653035790585722,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003488871,0.0001225895,0.0003158903,0.0003369125,0.00003073873,0.000007226512,0.0001613813,0.00007419217,0.00001256537],"category_scores_gemma":[0.0001141642,0.0001179101,0.0001225928,0.0003145596,0.00001079824,0.0004602873,0.000002790762,0.0004299984,8.533265e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006842327,"about_ca_system_score_gemma":0.00001597653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001288795,"about_ca_topic_score_gemma":0.000005097778,"domain_scores_codex":[0.9988256,0.00002291834,0.0005588038,0.0001074455,0.0002552759,0.0002299183],"domain_scores_gemma":[0.9990061,0.0003882714,0.0002853249,0.00009904654,0.0001850652,0.00003621025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008446,0.00001124493,0.03414696,0.0002083555,0.00004592067,0.00000331929,0.0002191933,0.5962126,0.3152067,0.00002180563,0.000002991159,0.05383637],"study_design_scores_gemma":[0.006391891,0.001955642,0.2583406,0.0008859485,0.0001698343,0.00004214695,0.001587126,0.3440717,0.3808191,0.002496009,0.002622814,0.0006171318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6696994,0.0005269772,0.3293328,0.00003280626,0.000117648,0.0001894914,0.000005531314,0.00007054381,0.00002480468],"genre_scores_gemma":[0.8952883,0.0003253393,0.1042723,0.000005066933,0.0000365848,0.000009137184,0.000007977566,0.00003744516,0.00001787764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.252141,"threshold_uncertainty_score":0.4808235,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}