{"meta":{"query_hash":"267700c9c1e6","filters":{"venue":"International Journal of Traffic and Transportation Management"},"cohort_total":26,"direct_labels_cover":0,"predictions_cover":26,"exported":26,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/267700c9c1e6","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Traffic+and+Transportation+Management"},"results":[{"id":"W2952987575","doi":"10.5383/jttm.01.01.002","title":"Dynamic, multi- and intermodal bike sharing in agent-based modelling","year":2019,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Bundesministerium für Verkehr, Innovation und Technologie","keywords":"Bike sharing; TRIPS architecture; Cycling; Transport engineering; Public transport; Parking space; Computer science; Traffic congestion; Multimodal transport; Environmental science; Operations research; Simulation; Engineering; Geography","score_opus":0.01946961776203312,"score_gpt":0.29720149688174574,"score_spread":0.27773187911971264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952987575","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9739438,0.00014891011,0.02472978,0.00045742554,0.0003454396,0.00013996576,0.000005668579,0.000010075104,0.00021893042],"genre_scores_gemma":[0.99649113,0.00024058443,0.0029893373,0.00006408117,0.000019418158,0.0000018630611,0.000011801042,0.000005536072,0.0001762225],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990129,0.000016768607,0.0003754356,0.00015353678,0.00033422167,0.000107151194],"domain_scores_gemma":[0.99964297,0.000021730088,0.00015145862,0.000041997748,0.00008489648,0.0000569774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039614507,0.000075596225,0.00012035693,0.00023053045,0.00003994732,0.000066316796,0.00019432777,0.000034705285,0.000060282255],"category_scores_gemma":[0.0000018144736,0.000073359326,0.000054378997,0.00007509358,0.000054143413,0.000331052,0.000003815422,0.00010292505,0.0000013626326],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036839058,0.0003864992,0.6487996,0.00013610566,0.0002098819,0.00022608372,0.0103584435,0.29142118,0.000024750494,0.005293741,0.0000058442643,0.042769488],"study_design_scores_gemma":[0.0034234927,0.0000474127,0.74586725,0.00029211902,0.00006330079,8.08216e-7,0.0038036024,0.24467295,0.00000704486,0.00040595044,0.0012227657,0.0001932734],"about_ca_topic_score_codex":0.00009613845,"about_ca_topic_score_gemma":0.001201537,"teacher_disagreement_score":0.09706766,"about_ca_system_score_codex":0.00005661729,"about_ca_system_score_gemma":0.000022807,"threshold_uncertainty_score":0.29915065},"labels":[],"label_agreement":null},{"id":"W2955361553","doi":"10.5383/jttm.01.01.004","title":"Modeling framework for supporting taxi policy making","year":2019,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Policy making; Computer science; Process management; Business; Political science; Public administration","score_opus":0.011268370391382027,"score_gpt":0.2926964490448884,"score_spread":0.2814280786535064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955361553","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50240016,0.000042280008,0.4961294,0.00042776504,0.0005568627,0.00017067979,0.000024923254,0.000039871153,0.000208056],"genre_scores_gemma":[0.97276384,0.00011518706,0.026741654,0.0001464861,0.00012595013,0.000009647643,0.00004504773,0.000016851247,0.00003534672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897826,0.0000036633355,0.00055921456,0.00009278137,0.00024762037,0.00011845152],"domain_scores_gemma":[0.99956286,0.000031289357,0.00010965934,0.000055837136,0.00020502761,0.000035342153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017702166,0.00009636429,0.00012618821,0.00032023928,0.000026255091,0.00004089514,0.00013360343,0.000041678737,0.000043298372],"category_scores_gemma":[0.0000071290583,0.00009967027,0.000091188005,0.000116889925,0.00001027972,0.00021742516,0.0000013943414,0.000103195016,0.0000025240317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039593284,0.000025923653,0.00057792,0.00010246773,0.0002634488,0.000007991303,0.0007098922,0.86184007,0.000033785207,0.11013198,0.000026969119,0.02623999],"study_design_scores_gemma":[0.004278983,0.00017212496,0.041210275,0.0007644593,0.00031670774,0.00002718791,0.0047379793,0.9188152,0.00014157103,0.0152447745,0.013661122,0.0006296403],"about_ca_topic_score_codex":0.0000012804048,"about_ca_topic_score_gemma":0.000009156064,"teacher_disagreement_score":0.47036365,"about_ca_system_score_codex":0.00004277801,"about_ca_system_score_gemma":0.000018880188,"threshold_uncertainty_score":0.40644357},"labels":[],"label_agreement":null},{"id":"W2997728300","doi":"10.5383/jttm.01.02.003","title":"Extensive use of Motorcycles in Karachi, Pakistan: Revisiting the Policies that led us here","year":2020,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Public transport; Transport engineering; Business; Traffic congestion; Car ownership; Engineering","score_opus":0.03736529369537057,"score_gpt":0.3173626295085501,"score_spread":0.27999733581317954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997728300","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9907236,0.00032375983,0.0007253595,0.0077699884,0.00012077839,0.00013827988,0.000016040138,0.0000075677217,0.00017462745],"genre_scores_gemma":[0.99823815,0.000766724,0.00031659842,0.00048562436,0.00015011332,0.000001468689,0.0000064775854,0.0000043911805,0.00003047304],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99879974,0.000059779562,0.0004563433,0.000099887475,0.0004913505,0.00009290144],"domain_scores_gemma":[0.99925095,0.00009267492,0.00038083826,0.000040096966,0.00018375646,0.000051658066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032995633,0.00007327912,0.00015756919,0.000079674326,0.00006691671,0.000067514884,0.00022899096,0.00002337224,0.000032882042],"category_scores_gemma":[0.000017391545,0.00005547742,0.00009457384,0.00012533696,0.00012493864,0.00038696025,0.000003972716,0.00010393857,3.8038087e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006189956,0.00010843802,0.8703788,0.00012342221,0.00037136476,0.0003018153,0.03389587,0.021706892,0.00005105694,0.013460615,0.000098337594,0.058884386],"study_design_scores_gemma":[0.0005162,0.000021631744,0.9833091,0.000112399466,0.000057294663,4.1526968e-7,0.009523063,0.00020202811,0.000018314262,0.00013306881,0.0060427273,0.000063757594],"about_ca_topic_score_codex":0.00030592593,"about_ca_topic_score_gemma":0.0004895237,"teacher_disagreement_score":0.1129303,"about_ca_system_score_codex":0.000025297177,"about_ca_system_score_gemma":0.000025881354,"threshold_uncertainty_score":0.22623035},"labels":[],"label_agreement":null},{"id":"W2998096164","doi":"10.5383/jttm.01.02.002","title":"The Effect of Type of Attributes on the Fill-Ability of Accident Reporting Forms (ARF)","year":2020,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Traffic and Road Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Accident (philosophy); Pedestrian; Block (permutation group theory); Computer science; Traffic accident; Database; Transport engineering; Road accident; Statistical analysis; Information retrieval; Statistics; Mathematics; Engineering","score_opus":0.016800101498190594,"score_gpt":0.25417075048698573,"score_spread":0.23737064898879515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998096164","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99688107,0.00020342098,0.0015219933,0.0008300401,0.00023564156,0.00013172891,0.000010403367,0.000009315833,0.00017636233],"genre_scores_gemma":[0.9994719,0.0003741574,0.00009210632,0.0000146619495,0.000028627428,0.0000015147302,0.000006406276,0.0000056899403,0.0000049207215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99860543,0.000019788675,0.000903619,0.00005751772,0.00035464895,0.000059020356],"domain_scores_gemma":[0.9989479,0.00019916032,0.00062539533,0.00006347562,0.00013766748,0.000026389114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005346673,0.00007501451,0.00017645808,0.000038570743,0.000023994091,0.000007071497,0.0001825684,0.000020739004,0.0000148105555],"category_scores_gemma":[0.00005726654,0.000040305276,0.00011976928,0.00008305975,0.000045589502,0.000057597856,0.0000047286835,0.00009216805,3.4614936e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008869901,0.000057921523,0.038134035,0.00037665808,0.0013695656,0.000028399783,0.0015174921,0.8860924,0.00032263104,0.0050605233,0.0005500269,0.065603346],"study_design_scores_gemma":[0.0014897889,0.00080481375,0.97457254,0.0002951354,0.00024946473,0.0000063138377,0.0010152272,0.012677603,0.0069033676,0.00009333101,0.0017748665,0.00011753955],"about_ca_topic_score_codex":0.0000012827742,"about_ca_topic_score_gemma":0.0000035955677,"teacher_disagreement_score":0.9364385,"about_ca_system_score_codex":0.00001332902,"about_ca_system_score_gemma":0.0000070707583,"threshold_uncertainty_score":0.16436015},"labels":[],"label_agreement":null},{"id":"W3113872603","doi":"10.5383/jttm.02.01.001","title":"Modelling Heterogeneous and Undisciplined Traffic Flow using Cell Transmission Model","year":2020,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Traffic control and management","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cell Transmission Model; Traffic flow (computer networking); Microscopic traffic flow model; Computer science; Diagram; Flow (mathematics); Transmission (telecommunications); Road traffic; Transport engineering; Field (mathematics); Traffic generation model; Free flow; Traffic congestion reconstruction with Kerner's three-phase theory; Traffic simulation; Simulation; Real-time computing; Traffic congestion; Computer network; Engineering; Microsimulation; Telecommunications; Mathematics","score_opus":0.015292378719802824,"score_gpt":0.21409458454585595,"score_spread":0.19880220582605312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113872603","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46556908,0.0006303252,0.5330936,0.00034810495,0.00013543581,0.00010686528,0.000010783102,0.000049105296,0.00005672843],"genre_scores_gemma":[0.97453743,0.0015138667,0.023730349,0.000089839035,0.000075325275,0.0000022146642,0.00001327918,0.00002201273,0.000015677591],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893385,0.000008476768,0.00045353457,0.00015246407,0.00032250516,0.00012916437],"domain_scores_gemma":[0.99965966,0.000010976104,0.00008592068,0.00004041502,0.000058195816,0.0001448451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008356968,0.00016007222,0.00018427051,0.0001428357,0.00004473945,0.0000561051,0.00013963746,0.00003780137,0.000012265472],"category_scores_gemma":[3.7749268e-7,0.00015250001,0.000090041365,0.00006281616,0.000020107975,0.00018806243,0.0000056532203,0.00010988808,7.256347e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010065117,0.00003594421,0.0000024797496,0.0001273927,0.00020810572,0.00009093061,0.0013686365,0.9361828,0.00008982868,0.00010513995,0.000036712183,0.061651375],"study_design_scores_gemma":[0.0015669239,0.000047764483,0.00005124445,0.000053472853,0.0001858551,0.000008173501,0.0002146773,0.9967323,0.000027178954,0.0000349403,0.0009332263,0.00014427687],"about_ca_topic_score_codex":5.9956074e-7,"about_ca_topic_score_gemma":0.0000019434076,"teacher_disagreement_score":0.50936323,"about_ca_system_score_codex":0.000027592456,"about_ca_system_score_gemma":0.000008852798,"threshold_uncertainty_score":0.621877},"labels":[],"label_agreement":null},{"id":"W3114851005","doi":"10.5383/jttm.02.02.006","title":"Effect of Distance between Ramp and Upstream Signal on Ramp Meter Operation","year":2020,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Traffic control and management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Upstream (networking); Metering mode; Queue; SIGNAL (programming language); Computer science; Metre; Downstream (manufacturing); Traffic congestion; Control (management); Simulation; Control theory (sociology); Engineering; Transport engineering; Computer network","score_opus":0.005152630042778735,"score_gpt":0.2120631812937908,"score_spread":0.20691055125101204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114851005","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.944183,0.00024088273,0.053987615,0.00076014566,0.00020364173,0.00021571199,0.00003055242,0.000035529094,0.00034293396],"genre_scores_gemma":[0.9991658,0.00029216128,0.00033197264,0.000063500134,0.00009572496,0.0000043208406,0.000022923728,0.000009866766,0.0000137717],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99915785,0.000020443826,0.0003602769,0.0000991423,0.00029202164,0.00007029102],"domain_scores_gemma":[0.99971145,0.00005697051,0.00009317075,0.00003362909,0.0000405148,0.000064268395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015656157,0.000113281094,0.0001884079,0.00009949255,0.000015964215,0.000031112035,0.00010389741,0.000023020242,0.000023629489],"category_scores_gemma":[0.0000024876442,0.00009443684,0.000064009044,0.00004393516,0.000021825312,0.00013041995,0.0000036595106,0.000080187354,0.000001331668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045007007,0.00003446495,0.0008621884,0.00026463834,0.0009077245,0.00005664894,0.0008886769,0.3561929,0.000120398974,0.0015045417,0.00021168495,0.63850605],"study_design_scores_gemma":[0.062243633,0.013554386,0.44165102,0.0022696233,0.0057945666,0.000029973291,0.002251024,0.31682315,0.015179683,0.00035558327,0.1372013,0.0026460416],"about_ca_topic_score_codex":7.6653953e-7,"about_ca_topic_score_gemma":0.000002872109,"teacher_disagreement_score":0.63586,"about_ca_system_score_codex":0.000016064758,"about_ca_system_score_gemma":0.0000026240473,"threshold_uncertainty_score":0.38510224},"labels":[],"label_agreement":null},{"id":"W3115305183","doi":"10.5383/jttm.02.01.002","title":"Design, Implementation and Testing of a New Multi-Sensor Mobile Device as a Tool for Cycling Data Collection in Highly Congested Urban Streets","year":2020,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Traffic and Road Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dashboard; Global Positioning System; Key (lock); Computer science; Mobile device; Data collection; Sample (material); Transport engineering; Real-time computing; Embedded system; Engineering; Computer security; Telecommunications; Data science; World Wide Web","score_opus":0.05781503586959652,"score_gpt":0.30545715188978695,"score_spread":0.24764211602019043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115305183","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82076025,0.00023226268,0.17822914,0.00018361349,0.00013529508,0.00039572033,0.000031611005,0.000024739918,0.0000073581587],"genre_scores_gemma":[0.9471603,0.0002600809,0.05239882,0.00003584548,0.000047626385,0.000007797125,0.00007027593,0.000011552703,0.000007677652],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990746,0.000013188348,0.000526104,0.00012777731,0.00017841719,0.000079964135],"domain_scores_gemma":[0.99955916,0.000086647706,0.00015961523,0.00004173186,0.00010032768,0.000052530315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017245788,0.00009298232,0.00014700725,0.0001332331,0.00002200885,0.000022839911,0.000121889076,0.000028208977,0.000005971596],"category_scores_gemma":[0.000010477929,0.00009425681,0.000023931274,0.00012053708,0.000011731635,0.00023619595,0.0000058015153,0.000065190805,2.2671907e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050075265,0.00008980658,0.008693515,0.00028846192,0.0005431493,0.000051360676,0.004770595,0.7733979,0.00083893235,0.00015295895,0.00042092288,0.21025161],"study_design_scores_gemma":[0.011680823,0.0005469115,0.22393578,0.0003226233,0.00035696494,0.000019474173,0.0068861344,0.75315386,0.0009947683,0.000021305856,0.001766744,0.00031460737],"about_ca_topic_score_codex":0.000014377201,"about_ca_topic_score_gemma":0.00006085425,"teacher_disagreement_score":0.21524227,"about_ca_system_score_codex":0.000025619614,"about_ca_system_score_gemma":0.000027613234,"threshold_uncertainty_score":0.3843681},"labels":[],"label_agreement":null},{"id":"W3116474425","doi":"10.5383/jttm.02.01.003","title":"Environmental Awareness and Inclination towards Walking: A Smartphone Application based study","year":2020,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pedestrian; Applied psychology; Perception; Psychology; Environmental health; Transport engineering; Medicine; Engineering","score_opus":0.017003061589099297,"score_gpt":0.2929161460341506,"score_spread":0.2759130844450513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116474425","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9830787,0.00008626549,0.013709842,0.0025768543,0.00014281426,0.00026478604,0.000014040572,0.000017849929,0.000108802284],"genre_scores_gemma":[0.9990487,0.00009918509,0.00046734614,0.00020649037,0.00012037479,0.000008253406,0.00003019451,0.0000056577264,0.000013785556],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99876475,0.000046543097,0.00037256756,0.0001648088,0.0005736897,0.00007764314],"domain_scores_gemma":[0.9995514,0.000022657854,0.00021570304,0.000036603025,0.00007195524,0.00010168598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036581198,0.00008283156,0.000119924895,0.000092057475,0.00012584768,0.000060682505,0.0001689054,0.000030090363,0.000051540323],"category_scores_gemma":[0.0000060666703,0.00008008247,0.000045825404,0.000087054665,0.000066245404,0.00029965537,0.0000046514715,0.000076909375,0.000001174034],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042763402,0.0008676218,0.71480864,0.000054835193,0.00028678775,0.000104047234,0.027953178,0.0020388567,0.0000526181,0.00095530984,0.000035892157,0.25241455],"study_design_scores_gemma":[0.0015148963,0.00009816395,0.9878813,0.000014171593,0.0001052882,2.2255783e-7,0.006902259,0.0010279383,0.000015886626,0.00008589199,0.00226382,0.000090138456],"about_ca_topic_score_codex":0.000043597593,"about_ca_topic_score_gemma":0.00021241832,"teacher_disagreement_score":0.27307266,"about_ca_system_score_codex":0.000042309974,"about_ca_system_score_gemma":0.000034109344,"threshold_uncertainty_score":0.32656685},"labels":[],"label_agreement":null},{"id":"W3116750145","doi":"10.5383/jttm.02.02.004","title":"Predicting Travel Behavior by Analyzing Mobility Transactions","year":2020,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reachability; Travel behavior; Computer science; Mode (computer interface); Travel time; Mode choice; Travel survey; Transport engineering; Human–computer interaction; Public transport; Engineering; Theoretical computer science","score_opus":0.015972011793855496,"score_gpt":0.28676891954238076,"score_spread":0.2707969077485253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116750145","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8179037,0.0001733843,0.17580682,0.005269608,0.00020343141,0.00019156621,0.000054374545,0.000028825874,0.00036827536],"genre_scores_gemma":[0.9987425,0.00041039757,0.0003900161,0.00020651652,0.00011632237,0.00000839375,0.000027159087,0.0000053803915,0.000093321985],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9986642,0.000063812266,0.00047976372,0.00015038154,0.0005372884,0.00010456984],"domain_scores_gemma":[0.9993163,0.000050207258,0.00020909728,0.0000403444,0.00023247789,0.00015159902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004468561,0.000080370606,0.00013816464,0.0001086321,0.00018810669,0.0000734085,0.0002097265,0.000039204846,0.00033325687],"category_scores_gemma":[0.000014331183,0.000082891514,0.0001401437,0.00017325647,0.000101351456,0.0002796585,0.0000012834724,0.0001329105,0.0000020643263],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000447195,0.0018629773,0.057658084,0.00017335625,0.0024893766,0.00019798547,0.10199802,0.08661865,0.0003060106,0.00605898,0.000856517,0.7413328],"study_design_scores_gemma":[0.01027795,0.0008449096,0.63640004,0.00041485496,0.0063303127,0.000012216264,0.21201065,0.051314704,0.0005960385,0.0005922453,0.07959199,0.0016140768],"about_ca_topic_score_codex":0.00019931562,"about_ca_topic_score_gemma":0.00076474104,"teacher_disagreement_score":0.7397188,"about_ca_system_score_codex":0.000057507452,"about_ca_system_score_gemma":0.00004556862,"threshold_uncertainty_score":0.36489293},"labels":[],"label_agreement":null},{"id":"W3116870837","doi":"10.5383/jttm.02.02.002","title":"Generation of a synthetic population for agent-based transport modelling with small sample travel survey data using statistical raster census data","year":2020,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Raster graphics; Markov chain Monte Carlo; Computer science; Sampling (signal processing); Sample (material); Markov chain; Population; Data mining; Bayesian probability; Stratified sampling; Scale (ratio); Statistics; Geography; Mathematics; Cartography; Artificial intelligence; Machine learning","score_opus":0.2979154819512889,"score_gpt":0.3552241445980471,"score_spread":0.05730866264675816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116870837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1835374,0.000034024142,0.812597,0.0003990452,0.00015490367,0.00023545121,0.0030259881,0.000009857546,0.0000063349275],"genre_scores_gemma":[0.8180612,0.00007018875,0.16961528,0.00006585547,0.000087085944,0.0000017546103,0.012082607,0.000012671843,0.000003368087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983319,0.0000777393,0.00064103707,0.0002724911,0.0005584967,0.00011831354],"domain_scores_gemma":[0.9987892,0.00017781754,0.0004250731,0.00013298725,0.00037544308,0.00009948605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007585869,0.00011251333,0.00020141018,0.00012767277,0.00011592171,0.00004438237,0.00040414982,0.00004663105,0.000015783588],"category_scores_gemma":[0.000030051417,0.00010723882,0.000032971344,0.00013012486,0.0000686769,0.00037245755,0.000003476522,0.00006878971,8.859738e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000705373,0.00010034862,0.011198911,0.00008089018,0.00020088661,0.000012714016,0.0017914152,0.977633,0.000007904348,0.0016618064,0.00003260525,0.006574117],"study_design_scores_gemma":[0.0020198545,0.00010872502,0.05364891,0.000110990586,0.00045573022,9.884029e-7,0.0013063358,0.9415043,0.000009259969,0.000030742256,0.00063883135,0.00016533578],"about_ca_topic_score_codex":0.0005383598,"about_ca_topic_score_gemma":0.0015248272,"teacher_disagreement_score":0.6429817,"about_ca_system_score_codex":0.000025288342,"about_ca_system_score_gemma":0.000089059264,"threshold_uncertainty_score":0.4373072},"labels":[],"label_agreement":null},{"id":"W3117043051","doi":"10.5383/jttm.02.02.005","title":"An iterative k-means clustering approach for identification of bicycle impediments in an urban traffic network","year":2020,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Outlier; Identification (biology); Computer science; Robustness (evolution); Data mining; Transport engineering; Engineering; Machine learning; Artificial intelligence","score_opus":0.024372042973707927,"score_gpt":0.3163114652121677,"score_spread":0.29193942223845976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117043051","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.921153,0.000067143745,0.07779692,0.00035032246,0.00020088954,0.00032746053,0.000030598865,0.00001330724,0.000060322443],"genre_scores_gemma":[0.99448943,0.000053407985,0.0049658264,0.00008008947,0.0002626181,0.000010697509,0.00011646621,0.000008112104,0.000013344707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.998515,0.00005924114,0.0007009878,0.00019049636,0.00040614468,0.0001281491],"domain_scores_gemma":[0.99922156,0.000023238348,0.00038098916,0.00005271575,0.00021244623,0.00010902215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005807361,0.00009062354,0.00016887761,0.00011959066,0.00007552539,0.00007334794,0.0003053045,0.00004229181,0.000015182514],"category_scores_gemma":[0.0000051866514,0.00009080747,0.00007732604,0.00016545801,0.00007031665,0.00079013855,0.0000022319634,0.00007743019,1.4472349e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009808872,0.0008164558,0.10656086,0.00016357962,0.00026438158,0.000023033315,0.07186092,0.76365286,0.00014277892,0.0017371292,0.00007138072,0.05372572],"study_design_scores_gemma":[0.0036253075,0.0004724213,0.7467015,0.00010467779,0.00021752212,4.8384913e-7,0.020013146,0.22764385,0.00007458797,0.00023174597,0.0006110004,0.00030380423],"about_ca_topic_score_codex":0.00001427821,"about_ca_topic_score_gemma":0.0003149349,"teacher_disagreement_score":0.6401406,"about_ca_system_score_codex":0.000036960315,"about_ca_system_score_gemma":0.000031499283,"threshold_uncertainty_score":0.3703021},"labels":[],"label_agreement":null},{"id":"W3136409717","doi":"10.5383/jttm.03.01.002","title":"Predicting Severity of Accidents in Malaysia By Ordinal Logistic Regression Models","year":2021,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universiti Sains Malaysia","keywords":"Logistic regression; Ordered logit; Ordinal regression; Road accident; Accident (philosophy); Regression analysis; Transport engineering; Geography; Statistics; Environmental health; Medicine; Mathematics; Engineering","score_opus":0.011382067238391741,"score_gpt":0.24853254881086195,"score_spread":0.2371504815724702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136409717","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9032785,0.00043194785,0.094773665,0.00015253862,0.00044030749,0.0000869735,0.000025185023,0.00011471142,0.00069611287],"genre_scores_gemma":[0.99569935,0.0021986556,0.0019654536,0.00002526867,0.000022637487,0.0000036455021,0.000038834212,0.000008758473,0.00003740761],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989796,0.000015299524,0.0004909438,0.00009476016,0.0003381927,0.000081196464],"domain_scores_gemma":[0.9996582,0.000014869493,0.00012757698,0.0000524626,0.00010962178,0.000037252226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015375165,0.00009040997,0.00014477222,0.00020179321,0.000011545387,0.00002069526,0.00013499195,0.000036326735,0.000016191438],"category_scores_gemma":[0.0000036916858,0.00008957734,0.00005471581,0.00010525987,0.000019036259,0.00027790348,0.000008081451,0.00010977599,2.7887032e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014152715,0.00029170464,0.016609734,0.0002903925,0.00055932597,0.00053435523,0.0007066895,0.8755825,0.00033967965,0.0034717098,0.005281142,0.0961912],"study_design_scores_gemma":[0.0043625426,0.00012012979,0.2543115,0.0014031898,0.0002745945,0.00007097155,0.0020028113,0.7313782,0.0016576031,0.0010469602,0.0029824954,0.0003889952],"about_ca_topic_score_codex":0.0000038304993,"about_ca_topic_score_gemma":0.00001697872,"teacher_disagreement_score":0.23770177,"about_ca_system_score_codex":0.000043014228,"about_ca_system_score_gemma":0.000007858664,"threshold_uncertainty_score":0.3652858},"labels":[],"label_agreement":null},{"id":"W3137448256","doi":"10.5383/jttm.03.01.003","title":"Machine Learning and statistic predictive modeling for road traffic flow","year":2021,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Computer science; Autoregressive integrated moving average; Traffic flow (computer networking); Artificial neural network; Traffic congestion; Statistic; Mean absolute percentage error; Autoregressive model; Multilayer perceptron; Intelligent transportation system; Machine learning; Artificial intelligence; Transport engineering; Time series; Engineering; Econometrics; Statistics; Geography; Computer security","score_opus":0.007351217630148736,"score_gpt":0.2313326467833026,"score_spread":0.22398142915315386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137448256","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08697889,0.0013059312,0.9101873,0.00020830025,0.00051452324,0.00017266034,0.00007507097,0.00030462723,0.00025269415],"genre_scores_gemma":[0.9770558,0.005486945,0.017142212,0.000036517387,0.000064556734,0.000014115787,0.00011593352,0.000017684622,0.00006626988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914455,0.000013263806,0.00037322473,0.00012562501,0.00024264905,0.00010068206],"domain_scores_gemma":[0.99964553,0.000025705596,0.00007127802,0.000033332304,0.00016361671,0.00006056106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015446213,0.000115143186,0.00014316355,0.00018576255,0.000049776256,0.00005769931,0.000075009346,0.000033110955,0.000012412404],"category_scores_gemma":[0.0000070422166,0.00011914262,0.000060761315,0.000057644615,0.000018446035,0.00018611795,0.000005096073,0.00011975139,3.3894025e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057257934,0.000033104385,0.00002793674,0.00009167793,0.00039865664,0.000064201704,0.00036582787,0.76645005,0.000012898924,0.0010631831,0.00028211164,0.23115307],"study_design_scores_gemma":[0.0012010051,0.00007317534,0.00087043765,0.00009103658,0.00015862714,0.000020887688,0.0005679657,0.99112266,0.000017635477,0.00008677388,0.0056846463,0.00010514907],"about_ca_topic_score_codex":7.2086294e-7,"about_ca_topic_score_gemma":0.000014169273,"teacher_disagreement_score":0.89304507,"about_ca_system_score_codex":0.000032549047,"about_ca_system_score_gemma":0.000008685644,"threshold_uncertainty_score":0.4858495},"labels":[],"label_agreement":null},{"id":"W3137449253","doi":"10.5383/jttm.03.01.001","title":"On the use of active mobile and stationary devices for detailed traffic data collection: A simulation-based evaluation","year":2021,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Trafikverket","keywords":"Data collection; Computer science; Mobile device; Identification (biology); Process (computing); Key (lock); Floating car data; Real-time computing; Data quality; Simulation; Transport engineering; Engineering; Traffic congestion","score_opus":0.047004023668281734,"score_gpt":0.3106063901611015,"score_spread":0.26360236649281976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137449253","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69591177,0.000269978,0.30155918,0.0005962435,0.0004520494,0.00082346203,0.00020566354,0.00012247884,0.000059209673],"genre_scores_gemma":[0.99447465,0.00033383624,0.004672024,0.000120181394,0.000027821634,0.0000509824,0.0002930188,0.000010973344,0.000016514605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989617,0.000037177277,0.00038782632,0.00013386783,0.0004174179,0.000062020736],"domain_scores_gemma":[0.9988196,0.00045318127,0.00016397583,0.00010997984,0.00042445384,0.00002881477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028499897,0.000096114476,0.00011662407,0.00019351483,0.00005296644,0.000051628424,0.00013254391,0.000030110266,0.000029207607],"category_scores_gemma":[0.000033948698,0.000082136,0.000047683643,0.00012119716,0.00003148594,0.0003263902,0.0000058131886,0.00006532831,1.9283763e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014054368,0.00007890305,0.000031897413,0.0000586305,0.0003782179,0.0000054791244,0.0002124544,0.8763032,0.000009750425,0.0009259151,0.0014121659,0.12044282],"study_design_scores_gemma":[0.0012554625,0.000086606306,0.010123405,0.000096862845,0.0002449999,0.0000016287099,0.00049832836,0.98013276,0.000075140015,0.00005391669,0.0073534576,0.00007740785],"about_ca_topic_score_codex":6.144906e-7,"about_ca_topic_score_gemma":0.000045277233,"teacher_disagreement_score":0.2985629,"about_ca_system_score_codex":0.00004065893,"about_ca_system_score_gemma":0.000033079723,"threshold_uncertainty_score":0.33494088},"labels":[],"label_agreement":null},{"id":"W3139013666","doi":"10.5383/jttm.03.01.005","title":"The convergence of Internet of Things, Blockchain and Connected Vehicles: Conceptual Advantages and Disadvantages of a new Cooperative Intelligent Transportation System","year":2021,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Blockchain; Intelligent transportation system; Computer science; The Internet; Sustainability; Internet of Things; Cryptocurrency; Convergence (economics); Computer security; Transport engineering; Engineering; World Wide Web","score_opus":0.008010038348685999,"score_gpt":0.2399359366189161,"score_spread":0.2319258982702301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139013666","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8138836,0.0017833482,0.18343185,0.0006221628,0.00011585663,0.0001229372,0.000018213008,0.000010140251,0.000011911215],"genre_scores_gemma":[0.99244696,0.0022237427,0.0052533275,0.000022277905,0.000007588365,0.0000034606333,0.000008747183,0.0000035958901,0.000030311578],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99879557,0.000037194914,0.00067027967,0.00015686637,0.00027114063,0.00006896132],"domain_scores_gemma":[0.9987647,0.00013998592,0.0005204225,0.00009731468,0.00043384748,0.00004370609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024052129,0.000095580705,0.00021550665,0.00010560539,0.000036471327,0.000019551102,0.0002768825,0.00004179218,0.0000032972964],"category_scores_gemma":[0.000011039357,0.00007572534,0.000047803325,0.00013779158,0.0002563931,0.00013297441,0.0000149028565,0.000093346374,6.214779e-8],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001671021,0.00017462482,0.0049411403,0.00025255248,0.00074165873,0.000061951985,0.020206513,0.0022012459,0.0011418681,0.9016647,0.000043833716,0.06840282],"study_design_scores_gemma":[0.0147800855,0.0024038425,0.3703333,0.0041493555,0.0012452819,0.0004009574,0.209475,0.18765134,0.19266187,0.010948985,0.0046790657,0.0012709186],"about_ca_topic_score_codex":0.000017345677,"about_ca_topic_score_gemma":0.000047575893,"teacher_disagreement_score":0.8907157,"about_ca_system_score_codex":0.000012175462,"about_ca_system_score_gemma":0.000039439896,"threshold_uncertainty_score":0.30879894},"labels":[],"label_agreement":null},{"id":"W3193997552","doi":"10.5383/jttm.03.02.004","title":"A Posture Recognition System to Track Drivers’ Activities While Driving","year":2021,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Qatar National Research Fund; Fonds National de la Recherche Luxembourg; Qatar Foundation","keywords":"Computer science; Virtual reality; Track (disk drive); Simulation; Human–computer interaction; Driving simulator; Test (biology); Warning system; Computer security","score_opus":0.016832726054703866,"score_gpt":0.2981257234579994,"score_spread":0.28129299740329555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193997552","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9793826,0.000056829336,0.0076470696,0.0016034432,0.0028844315,0.00012843516,0.000042143834,0.00005246806,0.008202596],"genre_scores_gemma":[0.99661124,0.000055294007,0.0013020375,0.0003701672,0.00018816642,0.000010299823,0.000071085946,0.000011175469,0.0013805489],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988012,0.00005972995,0.00048443914,0.00016797306,0.00038350956,0.000103192855],"domain_scores_gemma":[0.9991856,0.000048932172,0.0002493169,0.00007139817,0.00035522974,0.0000895073],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017173425,0.000106600666,0.00015077693,0.00028744488,0.00006160422,0.00008254941,0.00011919357,0.000047084803,0.0012021618],"category_scores_gemma":[0.0000052915793,0.000107663174,0.00011210707,0.00010651245,0.00001626384,0.0002686056,0.000005238163,0.0001323211,0.00006008436],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014496311,0.0015120066,0.004640953,0.00031482062,0.0056711063,0.005572845,0.06938312,0.016314367,0.0008151793,0.05844002,0.02400361,0.8118823],"study_design_scores_gemma":[0.0059046214,0.0003510074,0.72893584,0.0011994806,0.00051942014,0.0008905428,0.12505528,0.0006976577,0.00052697706,0.00012424604,0.13518338,0.0006115221],"about_ca_topic_score_codex":0.0000029905946,"about_ca_topic_score_gemma":0.000048251008,"teacher_disagreement_score":0.81127083,"about_ca_system_score_codex":0.00008331487,"about_ca_system_score_gemma":0.000018689112,"threshold_uncertainty_score":0.99971086},"labels":[],"label_agreement":null},{"id":"W3194357836","doi":"10.5383/jttm.03.02.001","title":"Regionalization for urban air mobility application in metropolitan areas: case studies in San Francisco and New York","year":2021,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Metropolitan area; Geography; Identification (biology); Population; Cluster analysis; Cartography; Transport engineering; Computer science; Engineering","score_opus":0.021888416447108216,"score_gpt":0.2702549499012892,"score_spread":0.24836653345418097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194357836","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70806295,0.0037251748,0.28667802,0.0005109339,0.00036771907,0.00044316472,0.000014078216,0.000037526224,0.0001604323],"genre_scores_gemma":[0.99256694,0.0013990733,0.005736569,0.000054144293,0.000073584015,0.000021101197,0.00007497342,0.000011377362,0.000062236395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907583,0.000015640439,0.00048398535,0.00014724681,0.00018594322,0.00009137184],"domain_scores_gemma":[0.9996215,0.00004654701,0.000100624275,0.00005502096,0.0001329006,0.00004336895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019883939,0.00010668475,0.00016269454,0.0002921562,0.000022608654,0.000021048583,0.00006313736,0.00003591776,0.0000058247792],"category_scores_gemma":[0.000010538454,0.000112458816,0.000036836955,0.00018868218,0.000027697584,0.00021332465,0.0000040419245,0.00006441581,1.7937796e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012919787,0.0001493878,0.006452926,0.00040916677,0.00038251554,0.0005698335,0.0027802573,0.9261965,0.0000072771404,0.019414902,0.0013808049,0.042127214],"study_design_scores_gemma":[0.017017316,0.0002792543,0.18395619,0.0013166962,0.0005914637,0.00045677822,0.043875784,0.72837937,0.00016022402,0.006234686,0.016668074,0.001064142],"about_ca_topic_score_codex":0.000011273705,"about_ca_topic_score_gemma":0.000808946,"teacher_disagreement_score":0.284504,"about_ca_system_score_codex":0.00013474222,"about_ca_system_score_gemma":0.000012281976,"threshold_uncertainty_score":0.45859376},"labels":[],"label_agreement":null},{"id":"W3195080669","doi":"10.5383/jttm.03.02.002","title":"Measuring Vehicle Speeds, Compliance Rates, and Braking Reaction Times at Level Crossings Using Fixed and Moving Driving Simulators","year":2012,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Traffic and Road Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Driving simulator; Simulation; Computer science; Automotive engineering; Blueprint; Compliance (psychology); Driving simulation; Engineering; Psychology; Mechanical engineering","score_opus":0.04513831130581906,"score_gpt":0.2656689089889848,"score_spread":0.22053059768316577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195080669","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9885167,0.0010925313,0.009643713,0.00005399705,0.00046164638,0.000067432775,0.000005626736,0.00003676342,0.0001215865],"genre_scores_gemma":[0.99651754,0.00041828846,0.0028560709,0.000019128644,0.00011478362,5.993542e-7,0.0000059960944,0.000017567567,0.00005000138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991348,0.000010989456,0.0003453336,0.00010105689,0.00025798724,0.00014985036],"domain_scores_gemma":[0.9996405,0.00003555786,0.00013439274,0.000036571753,0.00006327417,0.00008965865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022014625,0.00012557764,0.00014198359,0.00014443655,0.00012364736,0.00006895154,0.000062390245,0.00003821452,0.000008672033],"category_scores_gemma":[0.0000034440866,0.00012452058,0.000037684902,0.00005387456,0.00004200856,0.00056564575,0.000009803297,0.00010729978,6.8555363e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017535723,0.00010393842,0.17236581,0.0004028792,0.0009982196,0.00008538611,0.0058537885,0.6454642,0.0064564263,0.0011123323,0.0000949817,0.16688669],"study_design_scores_gemma":[0.0010944047,0.00001397781,0.8852955,0.00038262518,0.0001142627,0.000073832794,0.0005419383,0.11068399,0.00033227957,0.000015903352,0.0012582212,0.00019304927],"about_ca_topic_score_codex":0.000005493852,"about_ca_topic_score_gemma":0.000013559774,"teacher_disagreement_score":0.7129297,"about_ca_system_score_codex":0.000084694504,"about_ca_system_score_gemma":0.000004818704,"threshold_uncertainty_score":0.5077802},"labels":[],"label_agreement":null},{"id":"W3195594901","doi":"10.5383/jttm.03.02.003","title":"An integrated agent-based model of travel demand and package deliveries","year":2021,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fraunhofer-Gesellschaft","keywords":"Computer science; Last mile (transportation); Population; Agent-based model; Operations research; Food delivery; Transport engineering; Engineering; Business; Mile; Geography; Marketing","score_opus":0.016565161365512,"score_gpt":0.21396618041212428,"score_spread":0.19740101904661228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195594901","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5224692,0.00037278864,0.4767345,0.000043724045,0.00013702111,0.00003449681,0.0000848347,0.0000131546985,0.00011030001],"genre_scores_gemma":[0.9878906,0.0011262208,0.01078475,0.00003982501,0.000018162415,0.0000011251423,0.0001040205,0.000010796159,0.00002451566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992407,0.000009000917,0.00037272632,0.000083326886,0.00022566767,0.000068585665],"domain_scores_gemma":[0.9996056,0.000013224294,0.00007469704,0.000050686118,0.00019310389,0.00006268402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008151683,0.000097219025,0.0001484214,0.00012558736,0.000016139744,0.000024229435,0.000086162225,0.000034170946,0.00002494667],"category_scores_gemma":[0.0000014357053,0.00009171488,0.000050272964,0.00005485351,0.000052512954,0.00013971956,0.0000010294373,0.00007647711,1.9299127e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100153855,0.00013808506,0.0013982299,0.00016062825,0.00042593048,0.00029932233,0.00081333716,0.9766983,0.0013122294,0.0053544943,0.000070777096,0.01322855],"study_design_scores_gemma":[0.003277554,0.00015789841,0.08162731,0.00023406037,0.00048796224,0.000023614713,0.0018440488,0.90584755,0.0050742133,0.00039957472,0.00071745133,0.0003087897],"about_ca_topic_score_codex":0.0000013875481,"about_ca_topic_score_gemma":0.000048252918,"teacher_disagreement_score":0.46594974,"about_ca_system_score_codex":0.0000150409505,"about_ca_system_score_gemma":0.000018457105,"threshold_uncertainty_score":0.37400243},"labels":[],"label_agreement":null},{"id":"W4238787331","doi":"10.5383/jttm.02.02.001","title":"Model calibration to simulate driving recommendations for traffic flow optimization in oversaturated city traffic","year":2020,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Traffic control and management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Queue; Traffic flow (computer networking); Calibration; Traffic signal; Computer science; Traffic wave; Traffic conflict; Traffic congestion reconstruction with Kerner's three-phase theory; Traffic bottleneck; Traffic simulation; Traffic optimization; Queueing theory; Simulation; Transport engineering; Real-time computing; Traffic congestion; Floating car data; Microsimulation; Engineering; Computer network; Statistics; Mathematics","score_opus":0.017254020000117675,"score_gpt":0.2393588010339445,"score_spread":0.22210478103382683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238787331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3535386,0.00006244232,0.6387232,0.006410193,0.0004865543,0.0005691559,0.000054897315,0.00011372922,0.000041195628],"genre_scores_gemma":[0.96528834,0.0002707218,0.03374105,0.0003846133,0.000090151436,0.000025776268,0.00015320675,0.000023835077,0.00002232051],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986841,0.000015031102,0.00066578307,0.0001895509,0.00028481678,0.00016073778],"domain_scores_gemma":[0.99953336,0.00003996753,0.00011969546,0.000053870775,0.00012352098,0.00012959419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016673532,0.00016981224,0.00021052206,0.00032522777,0.000042366402,0.000083624334,0.00018255468,0.00004897074,0.000027506483],"category_scores_gemma":[0.000010558663,0.00018034366,0.000098875724,0.00021543326,0.000010788273,0.00040563525,0.000005884913,0.00011740046,8.362354e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001588938,0.000058830425,0.000011685802,0.00005021556,0.00019477,0.0000150394435,0.0016858694,0.9306789,0.000016458158,0.00021653794,0.0009122793,0.0660005],"study_design_scores_gemma":[0.002235584,0.00007474351,0.0008394035,0.000076905875,0.000088697205,0.0000010435876,0.00039172807,0.9940484,0.00000491428,0.000011883923,0.0020582306,0.00016846454],"about_ca_topic_score_codex":6.303039e-7,"about_ca_topic_score_gemma":0.000061768376,"teacher_disagreement_score":0.6117497,"about_ca_system_score_codex":0.00008512724,"about_ca_system_score_gemma":0.00001584375,"threshold_uncertainty_score":0.7354201},"labels":[],"label_agreement":null},{"id":"W4240397898","doi":"10.5383/jttm.01.01.003","title":"Evaluating Reinforcement Learning State Representations for Adaptive Traffic Signal Control","year":2019,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Traffic control and management","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Reinforcement learning; Computer science; Asynchronous communication; Artificial neural network; State (computer science); SIGNAL (programming language); Artificial intelligence; Real-time computing; Machine learning; Control engineering; Engineering; Algorithm; Telecommunications","score_opus":0.013524494973752828,"score_gpt":0.2651455425027036,"score_spread":0.25162104752895076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240397898","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70935357,0.00032828804,0.28668055,0.00035907206,0.0011950677,0.0010973758,0.00002610283,0.00012355091,0.0008363992],"genre_scores_gemma":[0.99649566,0.00023669096,0.0025169663,0.00006117753,0.00008758733,0.00004205474,0.000040094554,0.00002174789,0.00049799535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850684,0.000022588369,0.00063559157,0.0001592398,0.0005006156,0.0001751173],"domain_scores_gemma":[0.9993085,0.00010186082,0.00021541642,0.00006405736,0.00024148682,0.000068646645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039573025,0.00015612823,0.00021303029,0.0002644897,0.000047865546,0.00006268907,0.00017251344,0.000027397817,0.000101489706],"category_scores_gemma":[0.000004445258,0.0001524004,0.00014814644,0.00007334009,0.000018656207,0.00026050766,0.00000432989,0.00013291428,0.0000067102965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030240073,0.000032879765,0.000058242986,0.00006119616,0.0008758589,0.000016863367,0.0006237175,0.85623986,0.000059474325,0.0013695437,0.00013858524,0.14022136],"study_design_scores_gemma":[0.0077488804,0.00050972635,0.009164354,0.00012533837,0.00030914327,0.0000060817842,0.0013330297,0.97490704,0.0000215688,0.00010527337,0.0055411323,0.00022845797],"about_ca_topic_score_codex":0.0000013932388,"about_ca_topic_score_gemma":0.000009749664,"teacher_disagreement_score":0.2871421,"about_ca_system_score_codex":0.00006992807,"about_ca_system_score_gemma":0.000015145349,"threshold_uncertainty_score":0.6214708},"labels":[],"label_agreement":null},{"id":"W4242220737","doi":"10.5383/jttm.01.02.001","title":"Exploring the Transferability of FEATHERS – An Activity Based Travel Demand Model – For Ho Chi Minh City, Vietnam","year":2020,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Bijzonder Onderzoeksfonds UGent; Universiteit Hasselt; Japan International Cooperation Agency","keywords":"Transferability; Ho chi minh; Metropolitan area; Feather; Geography; Mode (computer interface); Mode choice; Descriptive statistics; Destinations; Business; Transport engineering; Computer science; Statistics; Econometrics; Public transport; Cartography; Engineering; Economics; Ecology; Mathematics; Scale (ratio); Biology; Archaeology","score_opus":0.10965099203117419,"score_gpt":0.32227408376315864,"score_spread":0.21262309173198446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242220737","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81420815,0.000025164467,0.18145664,0.0037191117,0.000140935,0.0002913131,0.000056204517,0.000009752188,0.00009275344],"genre_scores_gemma":[0.99811757,0.00011034197,0.0014671359,0.0001525857,0.00009828732,0.000015567812,0.000012894342,0.0000073160663,0.00001828757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9986944,0.000052943076,0.0004169631,0.00017078717,0.0005462254,0.00011867364],"domain_scores_gemma":[0.9993414,0.000060902243,0.00019800017,0.00006379931,0.00022329722,0.000112568225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000712043,0.000103020124,0.00019207213,0.00006413254,0.00013752263,0.000043780998,0.00034812326,0.00002972226,0.000023670298],"category_scores_gemma":[0.000010301025,0.000081138074,0.0001883153,0.00010090014,0.00015135057,0.0006108349,0.0000017673847,0.00010160194,9.535603e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006848298,0.002581103,0.1010159,0.0007961192,0.001677502,0.000050679915,0.16773377,0.44467258,0.00081415044,0.01671709,0.0001236759,0.25696912],"study_design_scores_gemma":[0.0037593541,0.00032372435,0.84781617,0.0000936048,0.0004989055,3.267437e-7,0.0072547044,0.13797367,0.0006326491,0.0007486925,0.0006175206,0.00028069026],"about_ca_topic_score_codex":0.000025187666,"about_ca_topic_score_gemma":0.00054490386,"teacher_disagreement_score":0.74680024,"about_ca_system_score_codex":0.000029178389,"about_ca_system_score_gemma":0.000069883936,"threshold_uncertainty_score":0.33087146},"labels":[],"label_agreement":null},{"id":"W4243033930","doi":"10.5383/jttm.01.01.001","title":"The matching problem of empty vehicle redistribution in autonomous taxi systems","year":2019,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bipartite graph; Queue; Redistribution (election); Computer science; Mathematical optimization; Matching (statistics); Graph; Operations research; Engineering; Mathematics; Computer network; Statistics; Theoretical computer science","score_opus":0.0045647536251294625,"score_gpt":0.21168392067843061,"score_spread":0.20711916705330116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243033930","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98937947,0.00018455564,0.009003221,0.00022821651,0.0005496815,0.00020654853,0.000030671217,0.000020173755,0.00039744168],"genre_scores_gemma":[0.99931437,0.00023036622,0.00033496076,0.000008152182,0.00001924589,0.000007046304,0.00003840361,0.00000631449,0.00004112357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989831,0.0000100129555,0.00061066647,0.00006220205,0.0002580366,0.00007598287],"domain_scores_gemma":[0.9995976,0.000030439596,0.0001539691,0.000051808798,0.00014486097,0.000021327614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026944684,0.00006749761,0.000106170446,0.00013654215,0.000020133812,0.000029140474,0.00012656076,0.000026703708,0.000008114683],"category_scores_gemma":[9.896397e-7,0.000056306337,0.000043089138,0.000115660936,0.0000207026,0.00016622734,0.0000013647269,0.000098722696,0.0000014473796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005692779,0.000078623285,0.003914908,0.00015315079,0.0002462521,0.000016916609,0.0012432012,0.9144907,0.00032985714,0.06476853,0.0000827561,0.014618161],"study_design_scores_gemma":[0.0044532055,0.0001514229,0.9110714,0.00061408966,0.00013216896,0.000019750125,0.007484282,0.05592925,0.00033850688,0.0013364884,0.018142264,0.00032715674],"about_ca_topic_score_codex":0.000008708943,"about_ca_topic_score_gemma":0.000045235283,"teacher_disagreement_score":0.9071565,"about_ca_system_score_codex":0.00005116295,"about_ca_system_score_gemma":0.00001294468,"threshold_uncertainty_score":0.22961058},"labels":[],"label_agreement":null},{"id":"W4252361803","doi":"10.5383/jttm.03.01.004","title":"Modeling framework for supporting taxi policy making","year":2019,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Order (exchange); Business; Econometric model; Mode (computer interface); Fleet management; Supply and demand; Distribution (mathematics); Transport engineering; Operations research; Computer science; Industrial organization; Economics; Finance; Engineering; Microeconomics","score_opus":0.011268370391382027,"score_gpt":0.2926964490448884,"score_spread":0.2814280786535064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252361803","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50240016,0.000042280008,0.4961294,0.00042776504,0.0005568627,0.00017067979,0.000024923254,0.000039871153,0.000208056],"genre_scores_gemma":[0.97276384,0.00011518706,0.026741654,0.0001464861,0.00012595013,0.000009647643,0.00004504773,0.000016851247,0.00003534672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897826,0.0000036633355,0.00055921456,0.00009278137,0.00024762037,0.00011845152],"domain_scores_gemma":[0.99956286,0.000031289357,0.00010965934,0.000055837136,0.00020502761,0.000035342153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017702166,0.00009636429,0.00012618821,0.00032023928,0.000026255091,0.00004089514,0.00013360343,0.000041678737,0.000043298372],"category_scores_gemma":[0.0000071290583,0.00009967027,0.000091188005,0.000116889925,0.00001027972,0.00021742516,0.0000013943414,0.000103195016,0.0000025240317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039593284,0.000025923653,0.00057792,0.00010246773,0.0002634488,0.000007991303,0.0007098922,0.86184007,0.000033785207,0.11013198,0.000026969119,0.02623999],"study_design_scores_gemma":[0.004278983,0.00017212496,0.041210275,0.0007644593,0.00031670774,0.00002718791,0.0047379793,0.9188152,0.00014157103,0.0152447745,0.013661122,0.0006296403],"about_ca_topic_score_codex":0.0000012804048,"about_ca_topic_score_gemma":0.000009156064,"teacher_disagreement_score":0.47036365,"about_ca_system_score_codex":0.00004277801,"about_ca_system_score_gemma":0.000018880188,"threshold_uncertainty_score":0.40644357},"labels":[],"label_agreement":null},{"id":"W4252451967","doi":"10.5383/jttm.02.02.003","title":"The effects of autonomous buses to vehicle scheduling system","year":2020,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Crew; Public transport; Crew scheduling; Scheduling (production processes); Computer science; Population; Transport engineering; Operations research; Business; Engineering; Aeronautics; Operations management","score_opus":0.0057761575401743485,"score_gpt":0.2098955778032294,"score_spread":0.20411942026305505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252451967","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9486917,0.00022344278,0.04893987,0.0011770979,0.00061967404,0.0001678211,0.000011364845,0.000052302752,0.00011670301],"genre_scores_gemma":[0.9979805,0.00016986641,0.0016613681,0.00010971154,0.000050187242,0.000006312363,0.0000060914663,0.000008139311,0.000007835322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991934,0.0000065265413,0.00043676564,0.0000599327,0.0002393399,0.00006404479],"domain_scores_gemma":[0.99959415,0.000052077132,0.0000897701,0.000039434315,0.00016354622,0.00006102653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008595009,0.0000679351,0.00009916593,0.000082725994,0.00003240137,0.00002527676,0.00014950779,0.000017020306,0.0000030259303],"category_scores_gemma":[0.000007700398,0.000055989614,0.000051885905,0.00012569915,0.000018639039,0.00009166874,0.0000016728652,0.00006531593,0.0000018839183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012008919,0.000050744096,0.00085691916,0.0006595739,0.000833272,0.00007622868,0.003935861,0.8924935,0.0014442194,0.034993865,0.00025638417,0.06427934],"study_design_scores_gemma":[0.010253837,0.0010629348,0.7322684,0.0019129813,0.0011998597,0.000030369885,0.018822096,0.15070213,0.014399951,0.00017313569,0.068171605,0.0010026788],"about_ca_topic_score_codex":0.0000011243062,"about_ca_topic_score_gemma":0.000007960658,"teacher_disagreement_score":0.74179137,"about_ca_system_score_codex":0.000022683964,"about_ca_system_score_gemma":0.000009496555,"threshold_uncertainty_score":0.22831902},"labels":[],"label_agreement":null},{"id":"W4254317802","doi":"10.5383/jttm.01.02.004","title":"https://iasks.org/articles/jttm-v01-i2-pp-27-34.pdf","year":2019,"lang":"en","type":"article","venue":"International Journal of Traffic and Transportation Management","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Wolkite University; Universiteit Hasselt","keywords":"Pedestrian; Vendor; Transport engineering; Preferred walking speed; Computer science; Simulation; Engineering; Physical medicine and rehabilitation; Medicine; Business; Marketing","score_opus":0.00638542002829159,"score_gpt":0.2208965035697675,"score_spread":0.2145110835414759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254317802","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98582065,0.00021113045,0.008171786,0.00046175384,0.0013687877,0.00012708834,0.000008736847,0.00005266553,0.0037774255],"genre_scores_gemma":[0.9965201,0.0007663989,0.00097610976,0.00016589585,0.00009155882,0.0000024625292,0.000034176974,0.00001719181,0.0014261267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988134,0.000010712192,0.00047961067,0.00010793493,0.00046473378,0.00012363598],"domain_scores_gemma":[0.9995634,0.000021967537,0.0001182102,0.00007436156,0.00014231456,0.000079756435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017250614,0.00012564949,0.00014299041,0.00017871383,0.000021122934,0.000055343968,0.00019018193,0.00004257011,0.0006654086],"category_scores_gemma":[0.0000022043052,0.00012209393,0.000095349686,0.00009833934,0.000019211955,0.00030264427,0.0000043514215,0.00014256382,0.00020261336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019494609,0.00021587308,0.017588345,0.00020448171,0.001293152,0.00024609573,0.0018788745,0.88669306,0.0005437457,0.023114825,0.002999762,0.065026835],"study_design_scores_gemma":[0.008860623,0.00033209694,0.5317683,0.00047677165,0.00048929016,0.000079363264,0.0047504893,0.34098345,0.00060248683,0.0008523298,0.109874,0.0009307929],"about_ca_topic_score_codex":0.000002182525,"about_ca_topic_score_gemma":0.00001887487,"teacher_disagreement_score":0.5457096,"about_ca_system_score_codex":0.00006687656,"about_ca_system_score_gemma":0.000010712396,"threshold_uncertainty_score":0.7285758},"labels":[],"label_agreement":null}]}